Recession Model Forecast: 04-01-2020

I made a number of significant improvements to the recession model in January of 2020. If you missed the January recession model post, or if you would like to review the improvements to the models, please revisit the Recession Model Forecast: 01-01-2020. Earlier this month, I reduced the number of input variables in all of the peak-trough neural network models and expanded the number of individual models to 12. This further constrained the models and made them even more robust - especially when interpreted as single aggregate peak-trough forecast. No changes were made to any of the explanatory variables.

I also recently developed a SEIR model for COVID-19, with variables for the magnitude and timing of social distancing restrictions, as well a probabilistic variable for decaying immunity. The results were ominous and are not fully reflected in equity prices, especially after the 31% rebound from the March 23rd lows to late April. The growth rate in new Coronavirus cases very closely matches my model estimates during the social distancing phase. I documented the model results in an in-depth article titled: "New Coronavirus Model and the Economy," which I posted on April 1, 2020.

Monthly Update

This article updates the diffusion indices, recession slack index, aggregate recession model, and aggregate peak-trough model through March 2020. When interpreting the results, please be aware that the economic effects of COVID-19 will not be fully reflected in all of the explanatory variables (due to reporting lags in the economic data) until May or even June. However, a number of the variables are already capturing these effects, particularly the market-based variables.

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.

Diffusion Index

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 the trend in the diffusion index are two of the variables used to estimate the probit, logit, and neural network model forecasts.

The graph of the diffusion index from 1/1/2006 to 4/1/2020 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). Due to the typical NBER delay, I have preliminarily shaded the likely beginning of a new U.S. recession in March 2020. 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.

Preliminary signs of weakness in the diffusion index reemerged in late 2018 and conditions deteriorated rapidly in December and January before rebounding in February through April of 2019 and stabilizing thereafter.  However, the slack indices remained depressed throughout 2019.

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 did temporarily affect the look-back data and the resulting trends. I initially went back and smoothed the trend data for every economic variable, but the new trend calculation is even more effective at reducing the impact of outliers, which makes it more robust.  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 jumped from one (3.8%) to 11 (42.3%) in March, which is the highest diffusion index value since the great recession - by far.

As explained above, it will take another one to two months before the effects of the coronavirus are fully reflected in all of the explanatory variables, but a number of these variables are already capturing these effects.

Please note that past estimates and index values will change whenever the historical data is revised and/or whenever model improvements are implemented.  All current and past forecasts and index calculations are based on the most recent models using the latest revised data from the current data set.

Figure 1: Diffusion Index 04-01-2020

Figure 1: Diffusion Index 04-01-2020

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. 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 0.5-sigma diffusion index and the trend in the new diffusion index are two of the variables used to estimate the probit, logit, and neural network model forecasts.

The graph of the 0.5-sigma diffusion index from 1/1/2006 to 4/1/2020 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). As explained above, I have preliminarily shaded the likely beginning of a new U.S. recession in March 2020.

The percentage of explanatory variables with Z-scores below the 0.5-sigma early warning threshold spiked from 30.8% to 73.1% in March, almost exclusively due to the economic effects of Coronavirus. 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.

As I noted in past months, the percentage of variables with Z-scores below their respective 0.5 sigma thresholds had been unusually high, especially with the standard diffusion index equal to zero. This significantly reduced the potential cushion to any adverse economic shocks and accelerated the decline due to the Coronavirus.

This new 0.5-sigma diffusion index and the trend in the new diffusion index are now both used directly in the neural network recession models. When combined with the recession slack indices, the new diffusion index provides even greater insight into rapidly changing conditions.

Figure 2: 0.5 Sigma Diffusion Index 04-01-2020

Figure 2: 0.5 Sigma Diffusion Index 04-01-2020

Recession Slack Indices

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. I use both of these values in the neural network models and in the probit and logit models.

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 March 2020, the median recession slack index plunged from 0.79 to 0.10. The mean recession slack index (affected more by outliers) plummeted from 0.87 to -0.34. Similar to the situation with the 0.5-sigma diffusion index, the mean and median slack indices had been unusually low. This made the U.S. economy particularly vulnerable to any adverse economic shocks, which accelerated the decline due to the Coronavirus. Note, all of these values reflect the new smoothed trend data.

Figure 3: Recession Slack Index 04-01-2020

Figure 3: Recession Slack Index 04-01-2020

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 sharply from 50.3% to 41.0% in March. The percentage of variables with increasing slack was only 19.2% in March. New evidence of economic weakness (or strength) often shows up first in this timely metric. The sharp decline in February (the previous month) provided the first glimpse of the eventual effects of the coronavirus.

Figure 4: MA(3) % Slack Increasing 04-01-2020

Figure 4: MA(3) % Slack Increasing 04-01-2020

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 provides 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 averages the estimates from probit and logit models derived from the level and trend in a subset of the four variables described above: the original diffusion index, the 0.5-sigma diffusion index, the slack indices, and the percentage of variables with increasing slack. The aggregate recession model estimates from 1/1/2006 to 4/01/2020 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). As explained above, I have preliminarily shaded the likely beginning of a new U.S. recession in March 2020. I suggest using a warning threshold of between 20-30% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate jumped from 0.1% to 59.7% in March, which is almost double the suggested early warning level of 30%. Given the lag in many of the economic data series, the probability that the U.S. is currently in a recession is now extremely high.

Figure 5: Aggregate Recession Model 04-01-2020

Figure 5: Aggregate Recession Model 04-01-2020

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 is a weighted-average of the estimates from a number of 12 different neural network models, all of which use the levels and trends of the four variables described above: the original diffusion index, the 0.5-sigma diffusion index, the slack indices, and the percentage of variables with increasing slack.

The aggregate peak-trough model estimates from 1/1/2006 to 4/01/2020 are depicted in Figure 6 below, which uses the same format as Figure 5, except that the shaded regions represent the periods between the peaks and troughs associated with NBER recessions. Due the unavoidable NBER delay, I have preliminarily shaded the likely peak associated with the beginning of a new U.S. recession in February 2020. The value of the S&P 500 index is also included (in blue - right axis).

The aggregate peak-trough model probability estimate for 4/01/2020 was 98.1%, which exploded from last months revised value of 12.4%. Even with the effects of the Coronavirus not fully reflected in the economic data, it is extremely likely that the S&P 500 index is now between the peak and trough associated with a new U.S. recession.

Figure 6: Aggregate Peak-Trough Model 04-01-2020

Figure 6: Aggregate Peak-Trough Model 04-01-2020

Ongoing Coronavirus Risks

The Coronavirus can be spread by asymptomatic individuals, limiting the effectiveness of precautionary actions. Despite the projected risks of opening the economy too soon, many U.S. states and foreign countries are already relaxing some or even all restrictions on social interaction, even before adequate testing programs have been implemented. With immunity levels far below the 65% threshold required to limit the spread of the virus, this could result in a very large spike in new cases and a new round of restrictions.

Similarly, there is no guarantee that those who have been infected will have or will retain immunity to subsequent infections. I have also read that there are already over 30 different Coronavirus mutations, which could dramatically limit the potential development or effectiveness of a vaccine. The Coronavirus may not be a short-term problem.

Even if the economies were opened prudently and safely, consumers would not return to their past behavior immediately (due to economic and health-related self preservation). Consumer behavior in some products or services will eventually return to some semblance of normalcy, but others may never recover. The generation that lived through the great depression was forever changed by the experience.The longer the Coronavirus pandemic lasts, the more likely we will see similar permanent behavioral changes in consumers.

Finally, the recent negative price of crude oil and the ongoing depressed demand raises the possibility of instability in several countries that are unusually dependent on crude oil. Of particular concern are many countries in the Middle East and Russia.

Conclusion

The March 2020 changes in the diffusion indices, slack indices, and the recession model forecasts were alarming and unprecedented - even with the Coronavirus effects not fully reflected in the economic data. The diffusion index jumped from one (3.8%) to 11 (42.3%) in March. The new 0.5-sigma diffusion index spiked from 30.8% to 73.1%. The mean and median recession slack indices both plummeted this month (to -0.34 and 0.10 respectively). The moving average of explanatory variables with increasing slack dropped sharply from 51.3% to 41.0% in March. The aggregate recession probability vaulted from 0.1% to 59.7%. The peak-trough recession probability exploded from 12.4% to 98.1%.

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 moved into positive territory (+2.0%), with an expected drawdown in that period of 30% (from 4/1/2020 levels). Expected price returns are still 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 still alarmingly negative (-7.3%), with an expected drawdown in that 10-year period of 62% (from 4/1/2020 levels).

Given the unprecedented and ongoing effects of shutting down the U.S. and global economies, I used forward estimates of annualized GDP declines of 4% in Q1 and 34% in Q2 when calculating the most recent Buffet Indicator ratio. Similar forward-looking adjustments would be required when calculating P/E or other valuation ratios. The resulting Buffett ratio incorporates forward-looking near-term GDP estimates as well as market data as of 4/1/2020. Even with the large equity declines through the end of March, the most recent Buffett Indicator value was higher than 99% of the historical Buffett Indicator ratios since 1951. As is evidenced by the data, a large decline in equity prices does not necessarily imply that stocks are undervalued. History offers compelling evidence that bullish equity positions today will face reduced returns over the coming years.

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

Brian Johnson

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About Brian Johnson

I have been an investment professional for over 30 years. I worked as a fixed income portfolio manager, personally managing over $13 billion in assets for institutional clients. I was also the President of a financial consulting and software development firm, developing artificial intelligence based forecasting and risk management systems for institutional investment managers. I am now a full-time proprietary trader in options, futures, stocks, and ETFs using both algorithmic and discretionary trading strategies. In addition to my professional investment experience, I designed and taught courses in financial derivatives for both MBA and undergraduate business programs on a part-time basis for a number of years.
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