Recession Model Forecast 03-01-2015

The following article updates the diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through February 2015. I read an article last month about a market-based metric that was indicating a recession. I tested the new indicator and discovered that it had an extensive history, over which it had consistently identified recessionary environments with minimal lag. In addition, none of the other explanatory variables captured this particular market characteristic. As a result, I created a new explanatory variable for the new market-based indicator, bringing the total number of explanatory recession model variables to 19. The current and historical data in this report reflect the current model configuration with all 19 variables.

The beauty of these types of models is that adding new variables does not require re-estimating the coefficients. The models only use historical changes in the diffusion index (the percentage of explanatory variables indicating a recession) and historical changes in the median recession slack index.

In July 2014, two new explanatory variables were added to the Trader Edge Recession Models and one explanatory variable was replaced. The swapped variables measured similar economic data, but the new series had more predictive power and was more forward-looking. For more information on the changes in July 2014, please see "Two New Improvements to Trader Edge Recession Models."

Diffusion Index

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession.  With the additions, there are now a total of 19 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 1/1/2003 to 3/1/2015 is presented in Figure 1 below (in red - left axis).  If you would like to view a graph of the earlier historical data (going back to 1960), please revisit A New Recession Slack Indicator. 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).

In December 2014, for the first time since late 2011-early2012, two of the 19 explanatory variables simultaneously indicated a recessionary environment and the same two variables still indicated a recessionary environment in January 2015. The number of variables indicating a recession dropped to one in February.

In non-recessionary environments, such weakness tends to persist for a few months and then dissipates.  However, if the weakness becomes more widespread or lingers for many months, that would be more problematic. It did persist for two consecutive months, but began to reverse course in February.

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.

Figure 1: Diffusion Index 03-01-2015

Figure 1: Diffusion Index 03-01-2015

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.

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.

During the month of January, the median recession slack index dropped from a revised value of 1.14 to 0.91, but bounced back to 1.02 in February. Since emerging from the great recession, 0.73 (on 9/1/2012) was the lowest recorded value of the recession slack index, so the current value of the recession index (1.02) indicates a larger recession cushion than we had in the fall of 2012. The recession slack index is well above the warning level of 0.50.

The ability to track small variations and trend changes over time illustrates the advantage of monitoring the continuous recession slack index in addition to the diffusion index above, which moves in discrete steps.

While it is useful to track the actual recession slack index values directly, the values are also used to generate the more intuitive probit and logit probability forecasts.

Figure 2: Median Recession Slack Index 03-01-2015

Figure 2: Median Recession Slack Index 03-01-2015

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 1/1/2003 to 03/01/2015 are depicted in Figure 3 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 30-40% for the aggregate recession model (green horizontal line).

The aggregate recession model probability estimate for 03/01/2015 was 0.1%, which was slightly lower than last month's value of 0.3%. According to the model, the probability that the U.S. is currently in a recession continues to be extremely remote.

Figure 3: Aggregate Recession Model  03-01-2015

Figure 3: Aggregate Recession Model 03-01-2015

Aggregate Peak-Trough Probability Estimate

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 1/1/2003 to 03/01/2015 are depicted in Figure 4 below, which uses the same format as Figure 3, 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 03/01/2015 was 7.6%, which was down slightly from the revised value of 11.8% at the end of January.  The current peak-trough probability estimate of 7.6% is still well below the early warning threshold of 30% to 40%. The decrease in the aggregate peak-trough recession probability estimate is consistent with the decline in the diffusion index and the increase in the median recession slack index.

Figure 4: Aggregate Peak-Trough Model 03-01-2015

Figure 4: Aggregate Peak-Trough Model 03-01-2015


U.S. recession risk remains low and declined in February. The diffusion index jumped from zero to two in December, remained at two in January, and declined to one in February.  The recession slack index dropped from 1.14 to 0.91 in January and bounced back to 1.02 in February.  The peak-trough recession probability estimate decreased from a revised value of 11.8% in January to 7.6% at the end of February 2015.  All of the forecast values are well inside their respective warning thresholds.

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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. I have also written four books on options and derivative strategies.
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