Recession Model Forecast 04-01-2015

The following article updates the diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through March 2015. In January 2015, I created a new explanatory variable for a 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.

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 4/1/2015 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-early 2012, 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 and remained at one in March.

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 and remained stable in March.

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 04-01-2015

Figure 1: Diffusion Index 04-01-2015

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.

At the end of November 2014, the revised median recession slack index was 1.20, comfortably above the warning level of 0.50. The revised values of the recession slack index declined in each of the subsequent four months, to 1.15, 1.02, 0.99, and 0.70 at the end of March. The value of 0.70 was the lowest value recorded since the end of the Great Recession. The trend in the recession slack index is very troubling and the cushion above the warning level has shrunk considerably over the past four months.

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 04-01-2015

Figure 2: Median Recession Slack Index 04-01-2015

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 04/01/2015 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 04/01/2015 was 0.2%, which was slightly higher than last month's value of 0.1%. According to the model, the probability that the U.S. is currently in a recession continues to be extremely remote.

Aggregate Recession Model  04-01-2015

Aggregate Recession Model 04-01-2015

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 04/01/2015 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 04/01/2015 was 10.8%, which up notably from the revised value of 7.9% at the end of February.  The current peak-trough probability estimate of 10.8% is still well below the early warning threshold of 30% to 40%. The increase in the aggregate peak-trough recession probability estimate is directionally consistent with the decline in the median recession slack index.

Figure 4: Aggregate Peak-Trough Model 04-01-2015

Figure 4: Aggregate Peak-Trough Model 04-01-2015

Conclusion

U.S. recession risk remains low, but increased in March. The diffusion index jumped from zero to two in December, remained at two in January, declined to one in February, and remained at one in March.  The recession slack index has dropped for four consecutive months and is now at its lowest level (0.70) since emerging from the Great Recession.  The peak-trough recession probability estimate increased from a revised value of 7.9% in February to 10.8% at the end of March 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.
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