Several of the explanatory variables are market-based. These variables are available in real-time (no lag), which means they respond very quickly to changing market conditions. In addition, they are never revised. This makes the Trader Edge recession model more responsive than many recession models. The current *and* historical data in this report reflect the current model configuration with all *26 variables*.

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. With the latest changes, there are now a total of 26 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to calculate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 12/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The U.S. economy flirted with entering a recession in early 2016, which was reflected in the deteriorating economic, fundamental, and especially market-based data. The diffusion index, slack index, and recession probability forecasts all captured the weakening conditions. However, the weakness proved to be temporary and the conditions and recession model forecasts improved rapidly.

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April and stabilizing thereafter. Upon detailed examination of the individual economic data series, it is clear that the Government shutdown temporarily affected the economic data. The most recent economic data is no longer affected, *but the shutdown was recently affecting the look-back data and the resulting trends, which is why I smoothed the data for every explanatory variable*. Smoothing the look-back data mitigates the impact of all such data outliers now and in the future. The number of explanatory variables indicating a recession dropped from one (3.8%) to zero (0.0%) in November.

Please note that past estimates and index values will change whenever the historical data is revised. All current and past forecasts and index calculations are based on the latest revised data from the current data set.

The Trader Edge 0.5-sigma diffusion Index equals the percentage of explanatory variables with Z-scores that are *less than 0.5 standard deviations* above their respective recession thresholds. This new diffusion index is much more sensitive than the standard (zero-sigma) diffusion index. As a result, it provides much more detail on the health of the U.S. economy. The new diffusion index is not currently being used in any of the regression models.

The graph of the 0.5-sigma diffusion index from 1/1/2006 to 12/1/2019 is presented in Figure 2 below (in red - left axis). The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The percentage of explanatory variables with Z-scores below the 0.5-sigma early warning threshold dropped from 30.8% to 23.1% in November. The additional level of detail provided by this (more continuous and responsive) metric will be invaluable going forward, especially given the infrequent and more discrete movements of the standard (zero-sigma) diffusion index.

For example, the percentage of variables below their respective 0.5 sigma thresholds seems unusually high, especially with the standard diffusion index equal to zero. To test this hypothesis, I used the entire history to calculate the average 0.5-sigma diffusion index percentage when the zero-sigma diffusion index was zero. The resulting average was only 8.1% - compared to 23.1% at the end of November. In other words, the percentage of explanatory variables that are within 0.5 sigma of their respective recession thresholds is almost three times the historical average. As I alluded to before, this implies that the the recession probability forecasts derived from the zero-sigma diffusion index are understated; however, the downward trend is favorable. This is an area of promising future research.

When combined with the recession slack indices, the new diffusion index will provide even greater insight into rapidly changing conditions.

The Trader Edge recession slack index equals the median standardized deviation of the current value of the explanatory variables from their respective recession thresholds. The resulting value signifies the amount of slack or cushion relative to the recession threshold, expressed in terms of the number of standard deviations. Higher slack values signify larger cushions above recessionary threshold levels. While the *median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

The gray shaded regions in Figure 3 below represent U.S. recessions as defined (after the fact) by the NBER. The *median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

The dark-red, horizontal line at 0.50 standard deviations denotes a possible warning threshold for the recession slack index. Many of the past recessions began when the recession slack index crossed below 0.50. Similarly, many of the past recessions ended when the recession slack index crossed back above 0.0.

In early-2014, the revised median recession slack index peaked at 1.48, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.53 in February 2016, before rebounding over the next few months. For most of 2017 and 2018, the median recession slack index was quite strong, but declined sharply in the fall. In early 2019, the median recession slack index dropped to a low of 0.54, but that was partially due to the temporary and artificial effects of the Government shutdown.

In November 2019, the median recession slack index increased from 0.75 to 0.78. The mean recession slack index increased from 0.87 to 0.94. As I mentioned above, the mean and median slack indices remain relatively close to the 0.5-sigma early warning threshold. This is consistent with the fact that a surprising 23.1% of the explanatory variables are below the 0.5-sigma threshold.

Similar to the situation with the 0.5-sigma diffusion index, the median slack index seems unusually low, especially with the standard diffusion index equal to zero. To test this hypothesis, I used the entire history to calculate the average median slack index when the zero-sigma diffusion index was zero. The resulting average was 1.40 standard deviations above the recession threshold - compared to a median slack index of only 0.78 standard deviations at the end of November. In other words, median slack index is only 0.28 above the early warning threshold - compared to a typical spread of 0.90 standard derivations. As a result, the cushion above the 0.5-sigma early warning threshold is a fraction of its typical value when the diffusion index equals zero. This provides additional evidence that the recession probability forecasts derived from the zero-sigma diffusion index are understated.

Note, all of these values reflect the new smoothed look-back data. It is important to recognize that median is more reliable than the mean, because it is not affected by extreme values. On a positive note, the trend in the recession slack indices is favorable.

To gain further insight into the slack index, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 4, but I personally monitor the monthly percentages as well.

Slack is a standardized value, so it is directly comparable across all variables. More slack indicates a larger cushion relative to a recessionary environment. As a result, we would like to see as many variables as possible with *increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack increased from 51.3% to 56.4% in November. New evidence of economic weakness (or strength) often shows up first in this timely metric.

The ability to track small variations and trend changes over time illustrates the advantage of monitoring the continuous recession slack index. The new slack variable will provide additional insight into the near-term direction of the economy and should be used in conjunction with the median recession slack index.

While it is useful to track the actual recession slack index values and percentage of variables with increasing slack, the diffusion percentages and slack index values are also used to generate the more intuitive probit and logit probability forecasts.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 12/01/2019 are depicted in Figure 5 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate remained decreased from 0.1% to 0.0% in November. According to the model, the probability that the U.S. is *currently* in a recession is extremely remote.

The peak-trough model forecasts are different from the recession model and are much more responsive. The peak-trough models estimate the probability of the S&P 500 being between the peak and trough associated with an NBER recession. The S&P 500 typically peaks before recessions begin and bottoms out before recessions end. As a result, it is far more difficult for the peak-trough model to fit this data and the model forecasts have larger errors than the recession model.

The Trader Edge aggregate peak-trough model equals the weighted-average of nine different models: the probit and logit models based on the diffusion index, the probit and logit models based on the recession slack index, and five neural network models.

The aggregate peak-trough model estimates from 1/1/2006 to 12/01/2019 are depicted in Figure 6 below, which uses the same format as Figure 6, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 12/01/2019 was 4.3%, which decreased slightly from last month's revised value of 5.2%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but deteriorated due to the Government shutdown before rebounding in the last few months. The recession risk appeared to increase in January of 2019, but this was largely due to the effects of the Government shutdown.

U.S. recession risk improved slightly in November. The diffusion index decreased from one (3.8%) to zero (0.0%) and the new 0.5-sigma diffusion index declined from 30.8% to 23.1%. The mean and median recession slack indices both increased slightly. Both slack indices remain marginally above the early warning threshold. The moving average of explanatory variables with increasing slack increased from 51.3% to 56.4% in November. The aggregate recession probability dropped from 0.1% to 0.0%. The peak-trough recession probability decreased from 5.2% to 4.3%.

Even with the relatively low recession model probabilities, the limited protection offered by the levels of the recession slack indices continues to be a concern, especially with the weak global economy and ongoing trade war.

Based on the most recent data, the equity allocation percentage regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years is still negative (-0.1%), with an expected drawdown in that period of 35% (from 12/1/2019 levels). Expected price returns are still extremely low in a historical context, especially given the near-term market, economic, and geopolitical risks.

The "Buffett Indicator" regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is materially negative (-5.3%), with an expected drawdown in that 10-year period of 56% (from 12/1/2019 levels).

Overvalued markets can *always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years.

Unlike human prognosticators, the Trader Edge recession model is completely objective and has no ego. It is not burdened by the emotional need to defend past erroneous forecasts and will always consistently apply the insights gained from new data.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>Several of the explanatory variables are market-based. These variables are available in real-time (no lag), which means they respond very quickly to changing market conditions. In addition, they are never revised. This makes the Trader Edge recession model more responsive than many recession models. The current *and* historical data in this report reflect the current model configuration with all *26 variables*.

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. With the latest changes, there are now a total of 26 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to calculate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 11/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The U.S. economy flirted with entering a recession in early 2016, which was reflected in the deteriorating economic, fundamental, and especially market-based data. The diffusion index, slack index, and recession probability forecasts all captured the weakening conditions. However, the weakness proved to be temporary and the conditions and recession model forecasts improved rapidly.

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April and stabilizing thereafter. Upon detailed examination of the individual economic data series, it is clear that the Government shutdown temporarily affected the economic data. The most recent economic data is no longer affected, *but the shutdown was recently affecting the look-back data and the resulting trends, which is why I smoothed the data for every explanatory variable*. Smoothing the look-back data mitigates the impact of all such data outliers now and in the future. The number of explanatory variables indicating a recession dropped from one (3.8%) to zero (0.0%) in October.

Please note that past estimates and index values will change whenever the historical data is revised. All current and past forecasts and index calculations are based on the latest revised data from the current data set.

The Trader Edge 0.5-sigma diffusion Index equals the percentage of explanatory variables with Z-scores that are *less than 0.5 standard deviations* above their respective recession thresholds. This new diffusion index is much more sensitive than the standard (zero-sigma) diffusion index. As a result, it provides much more detail on the health of the U.S. economy. The new diffusion index is not currently being used in any of the regression models.

The graph of the 0.5-sigma diffusion index from 1/1/2006 to 11/1/2019 is presented in Figure 2 below (in red - left axis). The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The percentage of explanatory variables with Z-scores below the 0.5-sigma early warning threshold dropped from 26.92% to 23.08% in October. The additional level of detail provided by this (more continuous and responsive) metric will be invaluable going forward, especially given the infrequent and more discrete movements of the standard (zero-sigma) diffusion index.

For example, the percentage of variables below their respective 0.5 sigma thresholds seems unusually high, especially with the standard diffusion index equal to zero. To test this hypothesis, I used the entire history to calculate the average 0.5-sigma diffusion index percentage when the zero-sigma diffusion index was zero. The resulting average was only 8.14% - compared to 23.08% at the end of October. In other words, the percentage of explanatory variables that are within 0.5 sigma of their respective recession thresholds is almost three times the historical average. As I alluded to before, this implies that the the recession probability forecasts derived from the zero-sigma diffusion index are understated, potentially significantly. This is an area of promising future research.

When combined with the recession slack indices, the new diffusion index will provide even greater insight into rapidly changing conditions.

The Trader Edge recession slack index equals the median standardized deviation of the current value of the explanatory variables from their respective recession thresholds. The resulting value signifies the amount of slack or cushion relative to the recession threshold, expressed in terms of the number of standard deviations. Higher slack values signify larger cushions above recessionary threshold levels. While the *median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

The gray shaded regions in Figure 3 below represent U.S. recessions as defined (after the fact) by the NBER. The *median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

The dark-red, horizontal line at 0.50 standard deviations denotes a possible warning threshold for the recession slack index. Many of the past recessions began when the recession slack index crossed below 0.50. Similarly, many of the past recessions ended when the recession slack index crossed back above 0.0.

In early-2014, the revised median recession slack index peaked at 1.48, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.53 in February 2016, before rebounding over the next few months. For most of 2017 and 2018, the median recession slack index was quite strong, but declined sharply in the fall. In early 2019, the median recession slack index dropped to a low of 0.56, but that was partially due to the temporary and artificial effects of the Government shutdown.

In October 2019, the median recession slack index decreased from 0.81 to 0.75. The mean recession slack index increased from 0.86 to 0.88. As I mentioned above, the mean and median slack indices remain relatively close to the 0.5-sigma early warning threshold. This is consistent with the fact that a surprising 23.08% of the explanatory variables are below the 0.5-sigma threshold.

Similar to the situation with the 0.5-sigma diffusion index, the median slack index seems unusually low, especially with the standard diffusion index equal to zero. To test this hypothesis, I used the entire history to calculate the average median slack index when the zero-sigma diffusion index was zero. The resulting average was 1.40 standard deviations above the recession threshold - compared to a median slack index of only 0.75 standard deviations at the end of October. In other words, median slack index is only 0.25 above the early warning threshold - compared to a typical spread of 0.90 standard derivations. As a result, the cushion above the 0.5-sigma early warning threshold is a fraction of its typical value when the diffusion index equals zero. This provides additional evidence that the recession probability forecasts derived from the zero-sigma diffusion index are understated.

Note, all of these values reflect the new smoothed look-back data. It is important to recognize that median is more reliable than the mean, because it is not affected by extreme values.

To gain further insight into the slack index, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 4, but I personally monitor the monthly percentages as well.

Slack is a standardized value, so it is directly comparable across all variables. More slack indicates a larger cushion relative to a recessionary environment. As a result, we would like to see as many variables as possible with *increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack decreased from 57.7% to 53.8% in October. New evidence of economic weakness (or strength) often shows up first in this timely metric.

The ability to track small variations and trend changes over time illustrates the advantage of monitoring the continuous recession slack index. The new slack variable will provide additional insight into the near-term direction of the economy and should be used in conjunction with the median recession slack index.

While it is useful to track the actual recession slack index values and percentage of variables with increasing slack, the diffusion percentages and slack index values are also used to generate the more intuitive probit and logit probability forecasts.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 11/01/2019 are depicted in Figure 5 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate remained constant at 0.0% in October. According to the model, the probability that the U.S. is *currently* in a recession is extremely remote.

The peak-trough model forecasts are different from the recession model and are much more responsive. The peak-trough models estimate the probability of the S&P 500 being between the peak and trough associated with an NBER recession. The S&P 500 typically peaks before recessions begin and bottoms out before recessions end. As a result, it is far more difficult for the peak-trough model to fit this data and the model forecasts have larger errors than the recession model.

The Trader Edge aggregate peak-trough model equals the weighted-average of nine different models: the probit and logit models based on the diffusion index, the probit and logit models based on the recession slack index, and five neural network models.

The aggregate peak-trough model estimates from 1/1/2006 to 11/01/2019 are depicted in Figure 6 below, which uses the same format as Figure 6, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 11/01/2019 was 4.5%, which decreased slightly from last month's revised value of 4.7%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but deteriorated due to the Government shutdown before rebounding in the last few months. The recession risk appeared to increase in January of 2019, but this was largely due to the effects of the Government shutdown.

U.S. recession risk was relatively stable in October. The diffusion index decreased from one (3.8%) to zero (0.0%) and the new 0.5-sigma diffusion index declined from 26.92% to 23.08%. The median recession slack index decreased, but the mean recession slack index increased. Both slack indices remain marginally above the early warning threshold. The moving average of explanatory variables with increasing slack decreased from 57.7% to 53.8% in October. The aggregate recession probability was unchanged at 0.0% and the peak-trough recession probability decreased from 4.7% to 4.5%.

Even with the relatively low recession model probabilities, the limited protection offered by the levels of the recession slack indices continues to be a concern, especially with the weak global economy and ongoing trade war.

Based on the most recent data, the equity allocation percentage regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years is still negative (-0.4%), with an expected drawdown in that period of 36% (from 11/1/2019 levels). Expected price returns are still extremely low in a historical context, especially given the near-term market, economic, and geopolitical risks.

The "Buffett Indicator" regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is materially negative (-5.6%), with an expected drawdown in that 10-year period of 57% (from 11/1/2019 levels).

Overvalued markets can *always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years.

Unlike human prognosticators, the Trader Edge recession model is completely objective and has no ego. It is not burdened by the emotional need to defend past erroneous forecasts and will always consistently apply the insights gained from new data.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>Several of the explanatory variables are market-based. These variables are available in real-time (no lag), which means they respond very quickly to changing market conditions. In addition, they are never revised. This makes the Trader Edge recession model more responsive than many recession models. The current *and* historical data in this report reflect the current model configuration with all *26 variables*.

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. With the latest changes, there are now a total of 26 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to calculate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 10/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The U.S. economy flirted with entering a recession in early 2016, which was reflected in the deteriorating economic, fundamental, and especially market-based data. The diffusion index, slack index, and recession probability forecasts all captured the weakening conditions. However, the weakness proved to be temporary and the conditions and recession model forecasts improved rapidly.

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April and stabilizing thereafter. Upon detailed examination of the individual economic data series, it is clear that the Government shutdown temporarily affected the economic data. The most recent economic data is no longer affected, *but the shutdown was still affecting the look-back data and the resulting trends, which is why I smoothed the data for every explanatory variable*. Smoothing the look-back data will mitigate the impact of all such data outliers now and in the future. The number of explanatory variables indicating a recession increased from zero (0.0%) to one (3.8%) in September.

Please note that past estimates and index values will change whenever the historical data is revised. All current and past forecasts and index calculations are based on the latest revised data from the current data set.

The Trader Edge 0.5-sigma diffusion Index equals the percentage of explanatory variables with Z-scores that are *less than 0.5 standard deviations* above their respective recession thresholds. This new diffusion index is much more sensitive than the standard (zero-sigma) diffusion index. As a result, it provides much more detail on the health of the U.S. economy. The new diffusion index is not currently being used in any of the regression models.

The graph of the 0.5-sigma diffusion index from 1/1/2006 to 10/1/2019 is presented in Figure 2 below (in red - left axis). The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The percentage of explanatory variables with Z-scores below the 0.5-sigma early warning threshold remained stable at 26.9% (7/26) in September. The additional level of detail provided by this (more continuous and responsive) metric will be invaluable going forward, especially given the infrequent and more discrete movements of the standard (zero-sigma) diffusion index. When combined with the recession slack indices, the new diffusion index will provide even greater insight into rapidly changing conditions.

The Trader Edge recession slack index equals the median standardized deviation of the current value of the explanatory variables from their respective recession thresholds. The resulting value signifies the amount of slack or cushion relative to the recession threshold, expressed in terms of the number of standard deviations. Higher slack values signify larger cushions above recessionary threshold levels. While the *median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

The gray shaded regions in Figure 3 below represent U.S. recessions as defined (after the fact) by the NBER. The *median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

The dark-red, horizontal line at 0.50 standard deviations denotes a possible warning threshold for the recession slack index. Many of the past recessions began when the recession slack index crossed below 0.50. Similarly, many of the past recessions ended when the recession slack index crossed back above 0.0.

In early-2014, the revised median recession slack index peaked at 1.48, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.53 in February 2016, before rebounding over the next few months. For most of 2017 and 2018, the median recession slack index was quite strong, but declined sharply in the fall. In early 2019, the median recession slack index dropped to a low of 0.56, but that was partially due to the temporary and artificial effects of the Government shutdown.

In September 2019, the median recession slack index decreased from 0.86 to 0.81. The mean recession slack index increased from 0.78 to 0.86. As I mentioned above, the mean and median slack indices remain relatively close to the 0.5-sigma early warning threshold. This is consistent with the fact that 26.9% of the explanatory variables are below the 0.5-sigma threshold. In other words, the risk of a recession is higher than the risk estimated by the standard diffusion index (and the associated models).

Note, all of these values reflect the new smoothed look-back data. It is important to recognize that median is more reliable than the mean, because it is not affected by extreme values.

To gain further insight into the slack index, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 4, but I personally monitor the monthly percentages as well.

Slack is a standardized value, so it is directly comparable across all variables. More slack indicates a larger cushion relative to a recessionary environment. As a result, we would like to see as many variables as possible with *increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack increased from 53.8% to 56.4% in September. New evidence of economic weakness (or strength) often shows up first in this timely metric.

The ability to track small variations and trend changes over time illustrates the advantage of monitoring the continuous recession slack index. The new slack variable will provide additional insight into the near-term direction of the economy and should be used in conjunction with the median recession slack index.

While it is useful to track the actual recession slack index values and percentage of variables with increasing slack, the diffusion percentages and slack index values are also used to generate the more intuitive probit and logit probability forecasts.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 10/01/2019 are depicted in Figure 5 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate remained constant at 0.0% in September. According to the model, the probability that the U.S. is *currently* in a recession is extremely remote.

The peak-trough model forecasts are different from the recession model and are much more responsive. The peak-trough models estimate the probability of the S&P 500 being between the peak and trough associated with an NBER recession. The S&P 500 typically peaks before recessions begin and bottoms out before recessions end. As a result, it is far more difficult for the peak-trough model to fit this data and the model forecasts have larger errors than the recession model.

The Trader Edge aggregate peak-trough model equals the weighted-average of nine different models: the probit and logit models based on the diffusion index, the probit and logit models based on the recession slack index, and five neural network models.

The aggregate peak-trough model estimates from 1/1/2006 to 10/01/2019 are depicted in Figure 6 below, which uses the same format as Figure 6, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 10/01/2019 was 4.8%, which increased slightly from last month's revised value of 3.8%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but deteriorated due to the Government shutdown before rebounding in the last few months. The recession risk appeared to increase in January of 2019, but this was largely due to the effects of the Government shutdown.

U.S. recession risk was relatively stable in September. The diffusion index increased from zero (0.0%) to one (3.8%) and the new 0.5-sigma diffusion index was unchanged at 26.9%. The median recession slack index decreased, but the mean recession increased. Both slack indices remain slightly above the early warning threshold. The moving average of explanatory variables with increasing slack increased from 53.8% to 56.4 in September. The aggregate recession probability was unchanged at 0.0% and the peak-trough recession probability increased from 3.8% to 4.8%.

Even with the relatively low recession model probabilities, the limited protection offered by the levels of the recession slack indices continues to be a concern, especially with the weak global economy and ongoing trade war.

Based on the most recent data, the equity allocation percentage regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years is still negative (-1.47%), with an expected drawdown in that period of 38% (from 10/1/2019 levels). Expected price returns are still extremely low in a historical context, especially given the near-term market, economic, and geopolitical risks.

The "Buffett Indicator" regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is materially negative (-3.8%), with an expected drawdown in that 10-year period of 51% (from 10/1/2019 levels).

Overvalued markets can *always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years.

Unlike human prognosticators, the Trader Edge recession model is completely objective and has no ego. It is not burdened by the emotional need to defend past erroneous forecasts and will always consistently apply the insights gained from new data.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>For the past year, I have been tracking several prospective explanatory variables for the Trader Edge recession model. Unfortunately, the demands of teaching at the KFBS last year did not leave me sufficient time to evaluate these new variables.

In the past month, I tested a number of prospective explanatory variables and I integrated six of these new variables into the recession model. They cover areas of the economy and market that were not adequately represented by the other variables, further expanding the breadth and robustness of the model. Increasing the number of explanatory variables reduces the discrete impact of each individual variable and also helps the model correctly identify different types of recessions that are triggered by a wider range of factors.

I also removed one explanatory variable that was based on the money supply. After evaluating many different money supply variables in the past month (independently and in combination with other variables), I concluded that the unprecedented level of central bank intervention has compromised the predictive value of these statistics for the foreseeable future. The new model has 26 explanatory variables: 21 from the previous model, plus six new variables, minus the money supply variable. I have also identified and am monitoring a few other variables that have explanatory power and are logical leading indicators of the US economy.

Due to the unique nature of the diffusion process and median slack variables, I do not re-estimate the general model coefficients or rebuild the neural network models when adding or removing variables. All of the current and historical data in this report reflects the *current* list of 26 variables.

While doing the above research, I was reminded again that the median and mean slack index values have been hovering just above the early warning threshold of 0.5 standard deviations for the past year. I thought it would be interesting to calculate the percentage of the explanatory variables that had already crossed below the 0.5-sigma early warning threshold. I calculated these values for the entire history and for the latest date. The new metric is called the 0.5-Sigma Diffusion Index. It is much more sensitive than the standard (zero-sigma) diffusion index. As a result, it provides much more granular detail on the health of the U.S. economy. I have not attempted to estimate probit, logit, or neural network models for the new 0.5 Sigma Diffusion Index, but it is an interesting potential area of future research. In the interim, I plan to include a chart for the new diffusion index every month.

The following article updates the diffusion indices, recession slack index, aggregate recession model, and aggregate peak-trough model through August 2019. The current *26-variable* model has a diverse set of explanatory variables and is quite robust. Each of the explanatory variables has predictive power individually; when combined, the group of indicators is able to identify early recession warnings from a wide range of diverse market-based, fundamental, technical, and economic sources.

*and* historical data in this report reflect the current model configuration with all *26 variables*.

The graph of the diffusion index from 1/1/2006 to 9/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April and stabilizing thereafter. Upon detailed examination of the individual economic data series, it is clear that the Government shutdown temporarily affected the economic data. The most recent economic data is no longer affected, *but the shutdown was still affecting the look-back data and the resulting trends, which is why I smoothed the data for every explanatory variable*. Smoothing the look-back data will mitigate the impact of all such data outliers now and in the future. The number of explanatory variables indicating a recession remained constant at zero (0.0%) in August.

The Trader Edge 0.5-sigma diffusion Index equals the percentage of explanatory variables with Z-scores that are less than 0.5 standard deviations above their respective recession thresholds.

The graph of the 0.5-sigma diffusion index from 1/1/2006 to 9/1/2019 is presented in Figure 2 below (in red - left axis). The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

The percentage of explanatory variables with Z-scores below the 0.5-sigma early warning threshold dropped from 42.3% to 26.9% in August. The additional level of detail provided by this (more continuous and responsive) metric will be invaluable going forward, especially given the infrequent and more discrete movements of the standard (zero-sigma) diffusion index. When combined with the recession slack indices, the new diffusion index will provide even greater insight into rapidly changing conditions.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

*median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In early-2014, the revised median recession slack index peaked at 1.48, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.53 in February 2016, before rebounding over the next few months. For most of 2017 and 2018, the median recession slack index was quite strong, but declined sharply in the fall. In early 2019, the median recession slack index dropped below to a low of 0.56, but that was partially due to the temporary and artificial effects of the Government shutdown.

In August 2019, the median recession slack index increased from 0.57 to 0.78. The mean recession slack index remained relatively constant in August, dropping from 0.76 to 0.75. As I mentioned above, the mean and median slack indices remain uncomfortably close to the 0.5-sigma early warning threshold. This is consistent with the fact that 26.9% of the variables are below the 0.5-sigma threshold. In other words, the risk of a recession is higher than the risk depicted by the standard diffusion index (and the associated models).

Note, all of these values reflect the new smoothed look-back data. It is important to recognize that median is more reliable than the mean, because it is not affected by extreme values.

To gain further insight into the slack index, I recently began calculating a derivative value: the percentage of variables with increasing slack each month. The possible values range from zero percent to 100 percent. Due to the monthly volatility, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 4, but I personally monitor the monthly percentages as well.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack increased from 46.2% to 53.8% in August. New evidence of economic weakness (or strength) often shows up first in this timely metric.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 9/01/2019 are depicted in Figure 5 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate decreased from a revised value of 0.2% in July to 0.0% in August. According to the model, the probability that the U.S. is *currently* in a recession is extremely remote.

The aggregate peak-trough model estimates from 1/1/2006 to 09/01/2019 are depicted in Figure 6 below, which uses the same format as Figure 6, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 9/01/2019 was 4.3%, which declined modestly from last month's revised value of 7.0%.

U.S. recession risk moderated slightly in August. The diffusion index remained constant at zero (0.0%) and the new 0.5-sigma diffusion index declined from 42.3% to 26.9%. The median recession slack index increased, but the mean recession was stable. Both slack indices remain slightly above the early warning threshold. The moving average of explanatory variables with increasing slack increased from 46.2% to 53.8% in August. The aggregate recession probability decreased slightly in August (to 0.0%) and the peak-trough recession probability dropped from 7.0% to 4.3%.

Hulbert's recent MarketWatch article cited research that used the household equity allocation percentage as a tool for forecasting long-term (10-year) future equity returns. The resulting correlation was so strong (-0.90) that I was compelled to duplicate the research and verify the results myself. I did so and the correlation is correct. It is highly unusual to ever see correlations that high in actual market data. Furthermore, a strong argument can be made for a causal link due to the direct effects of both market valuation and behavioral finance on the household equity allocation percentage.

Based on the most recent data, the regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years fell to -1.39%, with an expected drawdown in that period of 38% (from 9/1/2019 levels). Expected price returns are still extremely low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is materially negative (-3.8%), with an expected drawdown in that 10-year period of 51% (from 9/1/2019 levels).

*always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>I reduced my teaching schedule this year to a single MBA derivatives class, which begins in October. This will provide a better balance between teaching and trading and will allow much more time for new research going forward.

In the past two months, I have developed, coded, tested, and implemented two new long-term proprietary strategies: one for commodity futures and one for currency futures. Both look quite promising. The additional time this month also allowed me to smooth the look-back periods for the explanatory variables in the recession model, *which are reflected in this report*. If I have time before I return to teach in October, I hope to test several new variables that I have been considering for the model. Worst case, I will evaluate the new variables after the derivatives class ends.

The following article updates the diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through July 2019. The current 21-variable model has a diverse set of explanatory variables and is quite robust. Each of the explanatory variables has predictive power individually; when combined, the group of indicators is able to identify early recession warnings from a wide range of diverse market-based, fundamental, technical, and economic sources.

Several of the explanatory variables are market-based. These variables respond very quickly to changing market conditions and are never revised. This makes the Trader Edge recession model much more responsive than other recession models. The current *and* historical data in this report reflect the current model configuration with all 21 variables.

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. With the latest changes, there are now a total of 21 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to calculate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 8/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April. Upon detailed examination of the individual economic data series, it is clear that the Government shutdown temporarily affected the economic data. The most recent economic data is no longer affected, *but the shutdown was still affecting the look-back data and the resulting trends, which is why I smoothed the data*. Smoothing the look-back data will mitigate the impact of all such data outliers going forward. The number of explanatory variables indicating a recession remained constant at one (4.8%) in July.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the NBER. The *median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In mid-2014, the revised median recession slack index peaked at 1.31, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.54 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.41, but declined in the fall before rebounding at year-end. In early 2019, the median recession slack index dropped below 0.50, but that was due to the temporary and artificial effects of the Government shutdown.

In July 2019, the median recession slack index increased from 0.72 to 0.76. The mean recession slack index increased slightly in June, from 0.57 to 0.69. Note, all of these values reflect the new smoothed look-back data. It is important to recognize that median is more reliable than the mean, because it is not affected by extreme values. The median recession slack index is still slightly above the warning level.

To gain further insight into the slack index, I recently began calculating a derivative value: the percentage of variables with increasing slack each month. The possible values range from zero percent to 100 percent. Due to the monthly volatility, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 3, but I personally monitor the monthly percentages as well.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack remained constant at 49.2% in July. New evidence of economic weakness (or strength) often shows up first in this timely metric.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 8/01/2019 are depicted in Figure 4 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate decreased from a revised value of 0.2% in June to 0.1% in July. According to the model, the probability that the U.S. is *currently* in a recession is extremely remote.

The aggregate peak-trough model estimates from 1/1/2006 to 08/01/2019 are depicted in Figure 5 below, which uses the same format as Figure 4, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 8/01/2019 was 5.4%, which declined modestly from last month's revised value of 6.1%.

U.S. recession risk moderated slightly in July. The diffusion index remained constant at one (4.8%), but the mean and median recession slack indices both increased. Both slack indices remain slightly above the early warning threshold. The moving average of explanatory variables with increasing slack remained constant at 49.2% in July. The aggregate recession probability decreased slightly in July (to 0.1%) and the peak-trough recession probability dropped from 6.1% to 5.4%.

Hulbert's recent MarketWatch article cited research that used the household equity allocation percentage as a tool for forecasting long-term (10-year) future equity returns. The resulting correlation was so strong (-0.90) that I was compelled to duplicate the research and verify the results myself. I did so and the correlation is correct. It is highly unusual to ever see correlations that high in actual market data. Furthermore, a strong argument can be made for a causal link due to the direct effects of both market valuation and behavioral finance on the household equity allocation percentage.

Based on the most recent data, the regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years fell to -1.53%, with an expected drawdown in that period of 39% (from 8/1/2019 levels). Expected price returns are still extremely low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is materially negative (-3.77%), with an expected drawdown in that 10-year period of 51% (from 8/1/2019 levels).

*always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>Several of the explanatory variables are market-based. These variables respond very quickly to changing market conditions and are never revised. This makes the Trader Edge recession model much more responsive than other recession models. The current *and* historical data in this report reflect the current model configuration with all 21 variables.

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. With the latest changes, there are now a total of 21 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to calculate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 7/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April. Upon detailed examination of the individual economic data series, it is clear that the Government shutdown temporarily affected the economic data. The most recent economic data is no longer affected. The number of explanatory variables indicating a recession declined from three (14.3%) to two (9.5%) in June.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the NBER. The *median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In mid-2014, the revised median recession slack index peaked at 1.31, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.24 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.43, but declined in the fall before rebounding at year-end.

In June 2019, the median recession slack index decreased from 0.58 to 0.51. The mean recession slack index increased slightly in June, from 0.66 to 0.68. It is important to know that median is more reliable than the mean, because it is not affected by extreme values. The median recession slack index is essentially at the warning level. This is particularly troubling given that the effects of the shutdown are no longer artificially depressing the current economic data.

To gain further insight into the slack index, I recently began calculating a derivative value: the percentage of variables with increasing slack each month. The possible values range from zero percent to 100 percent. Due to the monthly volatility, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 3, but I personally monitor the monthly percentages as well.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack declined from 50.8% to 47.6% in June. New evidence of economic weakness (or strength) often shows up first in this timely metric.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 7/01/2019 are depicted in Figure 4 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate increased from a revised value of 0.3% in May to 0.4% in June. According to the model, the probability that the U.S. is *currently* in a recession is extremely low.

The aggregate peak-trough model estimates from 1/1/2006 to 07/01/2019 are depicted in Figure 5 below, which uses the same format as Figure 4, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 7/01/2019 was 9.3%, which declined modestly from last month's revised value of 14.4%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but deteriorated due to the Government shutdown before rebounding in the last few months.

U.S. recession risk moderated in June. The diffusion index dropped from a revised value of three (14.3%) to two (9.5%) in June. The changes in the mean and median recession slack indices were mixed, but both slack indices remain only marginally above the early warning threshold. The moving average of explanatory variables with increasing slack declined from 50.8% to 47.6% in June. The aggregate recession probability increased slightly in June (to 0.4%) and the peak-trough recession probability dropped from 14.4% to 9.3%.

Even with the relatively low recession model probabilities, the limited protection offered by the levels of the recession slack indices continues to be a significant concern.

Hulbert's recent MarketWatch article cited research that used the household equity allocation percentage as a tool for forecasting long-term (10-year) future equity returns. The resulting correlation was so strong (-0.90) that I was compelled to duplicate the research and verify the results myself. I did so and the correlation is correct. It is highly unusual to ever see correlations that high in actual market data. Furthermore, a strong argument can be made for a causal link due to the direct effects of both market valuation and behavioral finance on the household equity allocation percentage.

Based on the most recent data, the regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years fell to -1.59%, with an expected drawdown in that period of 39% (from 7/1/2019 levels). Expected price returns are still extremely low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is materially negative (-3.98%), with an expected drawdown in that 10-year period of 52% (from 7/1/2019 levels).

*always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>

Several of the explanatory variables are market-based. These variables respond very quickly to changing market conditions and are never revised. This makes the Trader Edge recession model much more responsive than other recession models. The current *and* historical data in this report reflect the current model configuration with all 21 variables.

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. With the latest changes, there are now a total of 21 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to calculate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 6/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April. Upon detailed examination of the individual economic data series, it is quite clear that the Government shutdown temporarily affected the economic data. Fortunately, the most recent economic data does not appear to be affected, which means the latest model forecasts are no longer tainted by the questionable data. The number of variables indicating a recession jumped from two (9.5%) to four (19.0%) in May.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

The gray shaded regions in Figure 2 below represent U.S. recessions as defined (after the fact) by the NBER. The *median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In mid-2014, the revised median recession slack index peaked at 1.37, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.27 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.41, but declined in the fall before rebounding at year-end.

In May 2019, the median recession slack index increased from 0.66 to 0.73. The mean recession slack index decreased in April, from 0.71 to 0.68. It is important to know that median is typically more reliable than the mean, because it is not affected by extreme values. The recession slack indices remain only slightly above their early warning thresholds - even after reversing the effects of the shutdown on the economic data.

To gain further insight into the slack index, I recently went back and calculated a derivative value: the percentage of variables with increasing slack each month. The possible values range from zero percent to 100 percent. Due to the monthly volatility, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 3, but I personally monitor the monthly percentages as well.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack declined from 61.9% to 54.0% in May.

New evidence of economic weakness (or strength) often shows up first in this timely metric. The rebound in this variable is somewhat encouraging, but the continued decline in the median and mean slack indices is cause for continued concern, especially with the recent weakness in the equity markets in May.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 6/01/2019 are depicted in Figure 4 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate increased from a revised value of 0.1% in April to 0.5% in May. According to the model, the probability that the U.S. is *currently* in a recession is extremely low.

The aggregate peak-trough model estimates from 1/1/2006 to 06/01/2019 are depicted in Figure 5 below, which uses the same format as Figure 4, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 6/01/2019 was 17.3%, which spiked significantly from last months revised value of 11.2%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but have deteriorated dramatically due to the Government shutdown before reversing in the last few months.

U.S. recession risk definitely increased in May, although recession risk remains relatively low. The diffusion index doubled from two (9.5%) to four (19.0%) in May. The changes in the mean and median recession slack indices were mixed, but both slack indices remain only marginally above the early warning threshold. The moving average of explanatory variables with increasing slack declined from a relatively high level of 61.9% to 54.0% in May. The aggregate recession probability increased slightly in May (to 0.5%) and the peak-trough recession probability jumped from 11.2% to 17.3%.

I explained a few months ago that it was likely that a few of the economic variables were affected by the Government shut-down, which I have now confirmed. While it is encouraging that the model was sensitive to these changes, it also illustrates the importance of qualitatively evaluating exogenous factors that are unknown to the model. The current model readings are representative of the true economic environment. Recession risk increased in May, but the equity market rebound in June (month-to-date) would mitigate some of this risk if the market hold its gains through the end of the month. The limited protection offered by the levels of the recession slack indices continues to be troubling.

Finally, Hulbert's recent MarketWatch article cited research that used the household equity allocation percentage as a tool for forecasting long-term (10-year) future equity returns. The resulting correlation was so strong (-0.90) that I was compelled to duplicate the research and verify the results myself. I did so and the correlation is correct. It is highly unusual to ever see correlations that high in actual market data. Furthermore, a strong argument can be made for a causal link due to the direct effects of both market valuation and behavioral finance on the household equity allocation percentage.

Based on the most recent data, the regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years fell to -1.39%, with an expected drawdown in that period of 38% (from 6/1/2019 levels). Expected price returns are still low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is slightly negative (-3.89%), with an expected drawdown in that 10-year period of 51% (from 6/1/2019 levels).

Overvalued securities can *always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years. In addition, it is especially troubling that almost all domestic and international equity markets have suffered dramatic drawdowns in the past 12 months, some as large as 25% to 30%.

Brian Johnson

Copyright 2019 Trading Insights, LLC. All rights reserved.

]]>*and* historical data in this report reflect the current model configuration with all 21 variables.

The graph of the diffusion index from 1/1/2006 to 5/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April. Upon detailed examination of the individual economic data series, it is quite clear that the Government shutdown temporarily affected the economic data. Fortunately, the most recent economic data does not appear to be affected, which means the latest model forecasts are no longer tainted by the questionable data. The number of variables indicating a recession remained constant at two (9.5%) in April.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

*median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In mid-2014, the revised median recession slack index peaked at 1.37, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.27 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.41, but declined in the fall before rebounding at year-end.

In April 2019, the median recession slack index increased from 0.48 to 0.54. The mean recession slack index continued to increase in April, from 0.68 to 0.70. It is important to know that median is typically more reliable than the mean, because it is not affected by extreme values. The good news is that the lows in the past few months were due to the effects of the temporary Government shutdown on several economic series. The bad news is that the recession slack indices remain at or just slightly above their early warning thresholds - even after reversing the effects of the shutdown on the economic data.

To gain further insight into the slack index, I recently went back and calculated a derivative value: the percentage of variables with increasing slack each month. The possible values range from zero percent to 100 percent. Due to the monthly volatility, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 3, but I personally monitor the monthly percentages as well.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack remained constant at 58.7% in April. This rebound is still slightly artificial and is due to reversing the effects of the Government shutdown. It will take another month or so for the 3-month moving average to be representative.

New evidence of economic weakness (or strength) often shows up first in this timely metric. The rebound in this variable is somewhat encouraging, but the continued decline in the median and mean slack indices is cause for continued concern, especially with the recent weakness in the equity markets in May.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 5/01/2019 are depicted in Figure 4 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate decreased from a revised value of 1.1% in March to 0.2% in April. According to the model, the probability that the U.S. is *currently* in a recession is extremely low.

The aggregate peak-trough model estimates from 1/1/2006 to 05/01/2019 are depicted in Figure 5 below, which uses the same format as Figure 4, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 5/01/2019 was 13.3%, which was unchanged from last months revised value of 13.3%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but have deteriorated dramatically due to the Government shutdown before reversing in the last few months.

U.S. recession risk was relatively stable in April. The diffusion index was unchanged, with two variables (9.5%) indicating a recession in April. The mean and median recession slack indices both increased slightly, but the more reliable median recession slack index remains very close to the early warning threshold. The moving average of explanatory variables with increasing slack remained constant at a relatively high level in April, but the average is still affected by reversing the effects of the government shutdown. The aggregate recession probability decreased slightly in April (to 0.2%), but the peak-trough recession probability remained constant at 13.3%.

I explained two months ago that it was likely that a few of the economic variables were affected by the Government shut-down, which I have now confirmed. While it is encouraging that the model was sensitive to these changes, it also illustrates the importance of qualitatively evaluating exogenous factors that are unknown to the model. The current model readings are representative of the true economic environment. While the April recession model forecasts are more encouraging, recession risk is still slightly elevated and the level of the median recession index is troubling.

Finally, Hulbert's recent MarketWatch article cited research that used the household equity allocation percentage as a tool for forecasting long-term (10-year) future equity returns. The resulting correlation was so strong (-0.90) that I was compelled to duplicate the research and verify the results myself. I did so and the correlation is correct. It is highly unusual to ever see correlations that high in actual market data. Furthermore, a strong argument can be made for a causal link due to the direct effects of both market valuation and behavioral finance on the household equity allocation percentage.

Based on the most recent data, the regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years fell to 2.6%, with an expected drawdown in that period of 29% (from 5/1/2019 levels). Expected price returns are still low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is slightly negative (-0.16%), with an expected drawdown in that 10-year period of 40% (from 5/1/2019 levels).

Overvalued securities can *always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years. In addition, it is especially troubling that almost all domestic and international equity markets have suffered dramatic drawdowns in the past 12 months, some as large as 25% to 30%.

Brian Johnson

Copyright 2018 Trading Insights, LLC. All rights reserved.

]]>*and* historical data in this report reflect the current model configuration with all 21 variables.

The graph of the diffusion index from 1/1/2006 to 4/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February and March. Upon detailed examination of the individual economic data series, it is quite clear that the Government shutdown temporarily affected the economic data. Fortunately, the most recent economic data does not appear to be affected, which means the latest model forecasts are no longer tainted by the questionable data. The number of variables indicating a recession dropped from three (14.3%) to two (9.5%) in March.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

*median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In mid-2014, the revised median recession slack index peaked at 1.37, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.27 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.41, but declined in the fall before rebounding at year-end.

In March 2019, the median recession slack index dropped from a 0.60 last month to 0.50. Conversely, the mean recession slack index continued to increase in March, from 0.63 to 0.68. It is important to know that median is typically more reliable than the mean, because it is not affected by extreme values. The good news is that the lows in the past few months were due to the effects of the temporary Government shutdown on several economic series. The bad news is that the recession slack indices remain at or just slightly above their early warning thresholds - even after reversing the effects of the shutdown on the economic data.

To gain further insight into the slack index, I recently went back and calculated a derivative value: the percentage of variables with increasing slack each month. The possible values range from zero percent to 100 percent. Due to the monthly volatility, I provide the three-month moving average of the percentage of variables with increasing slack in Figure 3, but I personally monitor the monthly percentages as well.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack increased from 44.4% last month to 57.1% in March. This rebound is artificial and is due to reversing the effects of the Government shutdown. It will take another couple of months for the 3-month moving average to be representative, although the monthly numbers going forward will be meaningful.

New evidence of economic weakness (or strength) often shows up first in this timely metric. The rebound in this variable is somewhat encouraging, but the continued decline in the median and mean slack indices is cause for continued concern, especially if the rebound proves to be short-lived.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 4/01/2019 are depicted in Figure 4 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate increased slightly from a revised value of 0.6% in February to 0.8% in March. According to the model, the probability that the U.S. is *currently* in a recession is extremely low. However, if the markets do not maintain their recent gains, this probability will increase.

The aggregate peak-trough model estimates from 1/1/2006 to 04/01/2019 are depicted in Figure 5 below, which uses the same format as Figure 4, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 4/01/2019 was 12.8%, which decreased by 3.4% from last months revised value of 16.2%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but have deteriorated dramatically due to the Government shutdown before reversing in February.

U.S. recession risk decreased slightly in March. The diffusion index declined from from three variables (14.3%) to two variables (9.5%) in February. The mean recession slack index increased, but the more reliable median recession slack index decreased. Both of the recession slack indices remain at or only marginally above the early warning recession threshold. The moving average of explanatory variables with increasing slack also increased in March, but this was also due to reversing the effects of the government shutdown. The aggregate recession probability increased slightly in March (to 0.8%), but the peak-trough recession probability declined to 12.8%.

I explained two months ago that it was likely that a few of the economic variables were affected by the Government shut-down, which I have now confirmed. While it is encouraging that the model was sensitive to these changes, it also illustrates the importance of qualitatively evaluating exogenous factors that are unknown to the model. The current model readings are representative of the true economic environment. While the March recession model forecasts are more encouraging, recession risk is still elevated and the level of the median recession index is troubling.

Finally, Hulbert's recent MarketWatch article cited research that used the household equity allocation percentage as a tool for forecasting long-term (10-year) future equity returns. The resulting correlation was so strong (-0.90) that I was compelled to duplicate the research and verify the results myself. I did so and the correlation is correct. It is highly unusual to ever see correlations that high in actual market data. Furthermore, a strong argument can be made for a causal link due to the direct effects of both market valuation and behavioral finance on the household equity allocation percentage.

Based on the most recent data, the regression model indicates that the expected *annual price return* of the S&P 500 index for the next 10 years fell to 2.9%, with an expected drawdown in that period of 28% (from 4/1/2019 levels). Expected price returns are still low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual price return* of the S&P 500 index for the *next 10 years* is only slightly positive (+0.07%), with an expected drawdown in that 10-year period of 39% (from 4/1/2019 levels).

Overvalued securities can *always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years. In addition, it is especially troubling that almost all domestic and international equity markets have suffered dramatic drawdowns in the past 12 months, some as large as 25% to 30%.

Brian Johnson

Copyright 2018 Trading Insights, LLC. All rights reserved.

]]>*and* historical data in this report reflect the current model configuration with all 21 variables.

The graph of the diffusion index from 1/1/2006 to 3/1/2019 is presented in Figure 1 below (in red - left axis). The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value of the S&P 500 index is also included (in blue - right axis).

However, preliminary signs of weakness reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February. Upon detailed examination of the individual economic data series, it is quite clear that the Government shutdown temporarily affected the economic data. Fortunately, the most recent economic data does not appear to be affected, which means the latest model forecasts are no longer tainted by the questionable data. The number of variables indicating a recession dropped from five (23.8%) to three (14.3%) in February, which reversed the temporary effects of the Government shutdown.

*median* recession slack index is used in the recession models, I am now including the *mean* recession slack index in the graph as well.

*median* recession slack index is depicted in purple and is plotted against the right axis, which is expressed as the number of standard deviations above the recession threshold. The *mean* recession slack index is depicted in blue and is also plotted against the right axis.

In mid-2014, the revised median recession slack index peaked at 1.35, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.27 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.45, but declined in the fall before rebounding at year-end.

In February 2019, the median recession slack index rebounded from a low of 0.49 last month to 0.69. Similarly, the mean recession slack index bounced back from a low of 0.36 last month to 0.64 at the end of February. The good news is that the lows last month were due to the effects of the temporary Government shutdown on several economic series. The bad news is that the recession slack indices are still only marginally above their early warning thresholds - even after reversing the effects of the shutdown on the economic data.

*increasing* slack. Given the diverse nature of the explanatory variables, it is unusual to see more than 60% of the variables with increasing slack or fewer than 40% of the variables with increasing slack. These extreme values are significant and predictive of the near-term direction of economic growth and *often the equity market*.

The 3-month moving average of the percentage of variables with *increasing* slack increased from 38.1% last month to 44.4% in February. This is again due to reversing the effects of the Government shutdown. It will take a few more months for the 3-month moving average to be representative, although the monthly numbers going forward will be meaningful. New evidence of economic weakness (or strength) often shows up first in this timely metric. The rebound in this variable is somewhat encouraging, but the continued decline in the median and mean slack indices is cause for continued concern, especially if the rebound proves to be short-lived.

The Trader Edge aggregate recession model is the average of four models: the probit and logit models based on the diffusion index and the probit and logit models based on the recession slack index. The aggregate recession model estimates from 1/1/2006 to 3/01/2019 are depicted in Figure 4 below (red line - left vertical axis). The gray shaded regions represent NBER recessions and the blue line reflects the value of the S&P 500 index (right vertical axis). I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate decreased from a revised value of 5.3% in January to 0.4% in February. According to the model, the probability that the U.S. is *currently* in a recession is once again quite low. However, if the markets do not maintain their recent gains, this probability will increase.

The aggregate peak-trough model estimates from 1/1/2006 to 03/01/2019 are depicted in Figure 5 below, which uses the same format as Figure 4, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions.

The aggregate peak-trough model probability estimate for 3/01/2019 was 14.9%, which decreased sharply from last months revised value of 37.7%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions improved significantly since early 2016, but have deteriorated dramatically due to the Government shutdown before reversing in February.

U.S. recession risk decreased significantly in February, primarily as a result of reversing the effects of the Government shutdown. The diffusion index declined from from five variables (23.8%) to three variables (14.3%) in February. Both of the recession slack indices also rebounded sharply, but remain only marginally above the early warning recession threshold. The moving average of explanatory variables with increasing slack also increased in February. The aggregate recession probability dropped significantly in February (to 0.4%), as did the peak-trough probability (to 14.9%).

I explained last month that it was likely that a few of the economic variables were affected by the Government shut-down, which I have now confirmed. While it is encouraging that the model was sensitive to these changes, it also illustrates the importance of qualitatively evaluating exogenous factors that are unknown to the model. The February model readings are representative of the true economic environment. While the February recession model forecasts are more encouraging, recession risk is still elevated.

Based on the most recent data, the regression model indicates that the expected annual price return of the S&P 500 index for the next 10 years rebounded slightly (3.2%), with an expected drawdown in that period of 27% (from 3/1/2019 levels). Expected price returns are still low in a historical context, especially given the near-term market, economic, and geopolitical risks.

I completed a similar historical regression analysis using the "Buffett Indicator", which is the ratio of equity market capitalization to GDP. The correlation is not quite as strong, but is still very significant (-0.74). The Buffett Indicator regression model currently indicates that the expected *annual* price return of the S&P 500 index for the *next 10 years* is still negative (-1.0%), with an expected drawdown in that 10-year period of over 50% (-42%) (from 3/1/2019 levels).

*always* become more overvalued - especially in the near-term. That said, history offers compelling evidence that bullish equity positions today will face significant headwinds over the coming years. In addition, it is especially troubling that almost all domestic and international equity markets have suffered dramatic drawdowns in the past 12 months, some as large as 25% to 30%.

Brian Johnson

Copyright 2018 Trading Insights, LLC. All rights reserved.

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