As I mentioned last month, I will be teaching the MBA derivatives class again for the University of North Carolina's Kenan-Flagler Business School (KFBS). I reduced my teaching schedule this year to a single MBA derivatives class, which begins next month. This will provide a better balance between teaching and trading and will allow more time for new research going forward.
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.
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 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).
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 remained constant at zero (0.0%) in August.
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.
0.5-Sigma Diffusion Index
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.
Recession Slack Index
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 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.
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 46.2% to 53.8% in August. 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.
Aggregate Recession Probability Estimate
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.
Aggregate Peak-Trough Probability Estimate
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 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%.
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 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%.
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.
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).
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.
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