The following article updates the diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through December 2018. 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 1/1/2019 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. The number of variables indicating a recession jumped from three (14.3%) to five (23.8%) in December; however, if I update the data through Friday's close (1/18/2019), two of the market-related variables would be back above their recession thresholds due to the market rebound in January. That said, there are still several explanatory variables that are only marginally above their respective recession thresholds.
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.
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 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.
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 1.35, far above the warning level of 0.50. The recession slack index declined significantly in 2015 and reached a low of 0.32 in February 2016, before rebounding over the next few months. In early 2017, the median recession index peaked at 1.42, but declined in the fall before rebounding at year-end.
In December 2018, the median recession slack index decreased from a revised value of 0.76 to 0.64. The mean recession slack index declined alarmingly from 0.81 to 0.43 during the same period. The median recession slack indices are still marginally above the early warning threshold of 0.50, but the mean recession slack index dropped below the early warning threshold for the first time since 2009.
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 3-month moving average of the percentage of variables with increasing slack dropped from 31.7% last month to only 25.4% in December. It is extremely unusual for this value to drop this low. This is the lowest 3-month average of variables with increasing slack since 2007. New evidence of economic weakness (or strength) often shows up first in this timely metric. The recent trend is very troubling, but could be mitigated if the equity markets continue to rebound in the coming months.
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 1/01/2019 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 0.5% in November to 3.9%. According to the model, the probability that the U.S. is currently in a recession continues to be remote. However, if the markets do not maintain their recent gains, this probability will continue to increase.
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 01/01/2019 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 1/01/2019 was 29.6%, which almost doubled last month's revised value of 15.8%. The peak-trough probability would be expected to decrease next month if the equity markets maintain their gains through the end of January.
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 increased significantly in December. The diffusion index jumped from three (14.3%) to five (23.8%) in December. In addition, both of the recession slack indices declined materially and the mean recession slack index fell below the early warning threshold for the first time since the great recession. The moving average of explanatory variables with increasing slack paints an equally bleak picture. Only 25.4% of the variables had increasing slack, which was the lowest percentage since 2007. The aggregate recession probability is still relatively low, but the peak-trough probability almost doubled in December and is now almost 30%.
The equity markets have rebounded so far in January, which would improve the outlook - at least temporarily. However, we have reached another inflection point. The next few months will be very telling. Stay tuned...
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 has improved to +1.05%, with an expected drawdown in that period of 32% (from 1/1/2019 levels). These values have improved recently due to the market correction, but expected price returns of positive 1.05% per year are still quite low, especially with 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 (-2.9%), with an expected drawdown in that 10-year period of almost 50% (-48%) (from 1/1/2019 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%.
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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