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 7/1/2017 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 have improved significantly in the past year. The number of variables indicating a recession is currently one out of 21 (4.76%), which is unchanged from last month.

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 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.

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 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 mid-2014, the revised median recession slack index peaked at 1.16, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low 0.32 in February 2016, before rebounding over the next few months.

The recession slack index is currently 1.17, which is down from last month's value of 1.25. With one brief exception in March (0.97), the slack index has been above 1.0 for the past eight months. The slack index is significantly above the early warning threshold.

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.

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 most recent moving average percentage is 41.3%, which has declined significantly since peaking at 60.3% in November 2016. This material decline in the moving average is notable and suggests an increasing risk of an equity market pullback in the near future. It is rare to see the moving average of increasing slack values fall below 40% and most instances are followed by significant short-term market declines.

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 7/01/2017 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 remained constant at 0.1% in June 2017. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

The peak-trough model forecasts are different from the recession model. 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 7/01/2017 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 7/01/2017 was 5.4%, which was up 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 have improved significantly since early 2016. The decrease in recession risk has been supported by both market and non-market variables.

U.S. recession risk remains very low. However, recessions are not the only source of pullbacks in the equity markets. The U.S. equity market continues to be overvalued and global event risk continues to be elevated. This recent *MarketWatch* article suggests that "*There are only two other times in history when stocks were more expensive than today*."

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 2017 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 6/1/2017 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 have improved significantly in the past year. The number of variables indicating a recession is currently one out of 21 (4.76%), which is unchanged from last month.

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 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.

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 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 mid-2014, the revised median recession slack index peaked at 1.16, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low 0.32 in February 2016, before rebounding over the next few months.

The recession slack index is currently 1.25, which is up from 1.19 last month. With one brief exception in March (0.97), the slack index has been above 1.0 for the past seven months. The slack index is significantly above the early warning threshold.

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.

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 most recent moving average percentage is 42.9%, which has declined significantly since peaking at 60.3% in November 2016. This material decline in the moving average is notable and suggests an increasing risk of an equity market pullback in the near future.

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 6/01/2017 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 remained constant at 0.1% in May 2017. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

The peak-trough model forecasts are different from the recession model. 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 6/01/2017 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 6/01/2017 was 4.7%, which was up 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 have improved significantly since early 2016. The decrease in recession risk has been supported by both market and non-market variables.

U.S. recession risk remains very low. However, recessions are not the only source of pullbacks in the equity markets. The U.S. equity market continues to be overvalued and global event risk continues to be elevated. This recent *MarketWatch* article suggests that "*There are only two other times in history when stocks were more expensive than today*."

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 2017 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 recent 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 5/1/2017 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 have improved significantly in the past year. The number of variables indicating a recession is currently one out of 21 (4.76%), which increased from zero out of 21 last month. The diffusion index only remained at zero for a single month.

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 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.

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 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 mid-2014, the revised median recession slack index peaked at 1.16, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low 0.32 in February 2016, before rebounding over the next few months.

The recession slack index is currently 1.19, which is up sharply from last month's value of 0.97. With the exception of last month, the slack index has been robust for the past six months. The slack index is significantly above the early warning threshold.

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.

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 most recent moving average percentage is 54.0%, which increased from last month's average of 50.8%. Despite the increase in the diffusion index from zero to one in April, the more continuous slack data suggests the robustness of the economy strengthened last month.

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 5/01/2017 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 0.0% to 0.1% in April 2017. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

The peak-trough model forecasts are different from the recession model. 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 5/01/2017 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 5/01/2017 was 3.8%, which was up slightly from last month's revised value of 3.5%.

January and February 2016 marked a potential tipping point in U.S. recession risk, but those conditions proved to be temporary. Conditions have improved significantly since early 2016. The decrease in recession risk has been supported by both market and non-market variables.

Despite the one month increase in the diffusion index, U.S. recession risk remains very low. However, recessions are not the only source of pullbacks in the equity markets. The U.S. equity market continues to be overvalued and global event risk continues to be elevated.

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 2017 Trading Insights, LLC. All rights reserved.

]]>*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment. The Kindle ($9.99) *and* Print ($14.99) versions are both available on Amazon.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>*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 recent 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 4/1/2017 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 have improved significantly in the past year. The number of variables indicating a recession is currently zero out of 21 (0.0%), which declined from one out of 21 last month. The diffusion index has not equaled 0% since late 2014.

In mid-2014, the revised median recession slack index peaked at 1.15, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low 0.32 in February 2016, before rebounding over the next few months.

The recession slack index is currently 0.97, which is down sharply from last month's value of 1.25. While the one-month decline was significant, the slack index is still materially above the early warning threshold.

*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 most recent moving average percentage is 49.20%, which declined from last month's average of 52.4%. In addition, the percentage of variables with increasing slack in the last *month* was only 38.1%, which was the lowest reading since early 2016. I would not assign too much weight to a single monthly value, but the decline is significant, especially when coupled with the sharp drop in the median slack index. Despite the decline in the diffusion index, the more continuous slack data suggests the robustness of the economy has weakened in the past few months.

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 4/01/2017 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 declined from 0.1% to 0.0% in March 2017. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

The aggregate peak-trough model estimates from 1/1/2006 to 4/01/2017 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 4/01/2017 was 3.5%, which was unchanged from last month's value.

However, the sharp decline in the slack index last month and in the number of variables with increasing slack should be monitored for signs of further weakness. That said, the recession model probability forecasts remain very low.

Recessions are not the only source of pullbacks for the equity markets. The equity market continues to be overvalued and there are an unusual number of discrete global risk events on the horizon.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment.

The following video demonstrates how to use the spreadsheet tools that accompanies my new article (*Option Strategy Hedging & Risk Management*) to identify the lowest-cost black-swan hedge using VIX call options and/or put options on the underlying security.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment.

The following video demonstrates how to use the tools on the Futures Hedge tab of the spreadsheet that accompanies my new article: *Option Strategy Hedging & Risk Management*.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment.

The following video demonstrates how to use the tools on the Position Size tab of the spreadsheet that accompanies my new article: *Option Strategy Hedging & Risk Management*.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment.

If you enjoy the article, please take a few minutes to provide a brief review on Amazon. It would be greatly appreciated. Thank you for your support.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment.

If you enjoy the article, please take a few minutes to provide a brief review on Amazon. It would be greatly appreciated. Thank you.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>