*Option Strategy Hedging & Risk Management* presents a comprehensive analytical framework and accompanying spreadsheet tools for managing and hedging option strategy risk. I developed these practical techniques to hedge the unique and often overlooked risks associated with trading option strategies. These revolutionary new tools can be applied to *any* option strategy, in *any* market environment.

If you enjoy the article, please take a few minutes to provide a brief review on Amazon. It would be greatly appreciated. Thank you.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

]]>

Look for the new article on Amazon in the next few months.

Brian Johnson

]]>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. After the latest additions and deletions, the total number of explanatory recession model variables is now 21. 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 recent 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 2/1/2017 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).

In December 2014, for the first time since December 2012, one of the 21 explanatory variables indicated a recessionary environment. In 2015 and 2016, the number of explanatory variables indicating a recession varied between one and *eight*, with the peak occurring in January of 2016. Between February 2016 and May 2016, the number of explanatory variables indicating a recession varied between seven and four. From July through December, the number varied between one and two. The number of variables indicating a recession is currently one out of 21 (4.8%), unchanged from the prior month.

As I have explained before, 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 sharp rebound in the price of risk-assets in March/April of 2016 improved the U.S. economic outlook, which significantly reduced the risk of an imminent U.S. recession. The subsequent market gains and improving economic data further reduced the risk of a US recession.

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.

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.

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.

In mid-2014, the revised median recession slack index peaked at 1.15, far above the warning level of 0.50. The recession slack index declined significantly in 2015, with a high of 0.94 and a low of 0.39. The recession slack index fell further in early 2016, reaching its low of 0.32 in February, before rebounding over the next few months.

The recession slack index is currently 1.13, which declined slightly after last months jump from 0.96 to 1.18. The slack index cushion has grown significantly in the past six months and is now comfortably above the early warning level.

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*.

Note the very sharp rebound in the percentage of variables with increasing slack *before* the end of the 2007-2008 great recession. Roughly 90% of the variables demonstrated increasing slack by the end of the recession. Similarly, the percentage of variables with increasing slack plummeted below 30% *months before the 2007-2008 recession began*. The percentage plunged below 30% again in early 2015, well before the softening in the economy and corresponding market pull-back later that year. The most recent moving average percentage is 57.1%, which is down slightly from last month's revised value of 60.3%, which is not surprising given the unusually strong percentage last month. It is unusual for the moving average to exceed 60%. The current value of 52.4% is lower than the recent peak of 58.7% two months ago.

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.

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 2/01/2017 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 0.0% in December 2016 to 0.1% in January 2017. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

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 1/1/2006 to 2/01/2017 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 2/01/2017 was 2.2%, which was down slightly from the revised value of 3.0% at the end of December 2016.

January and February 2016 marked a potential tipping point in U.S. recession risk, but that risk declined significantly in March and April. Recession risk fluctuated in subsequent months, but continued to decline. The recent decrease in recession risk is supported by both market and non-market variables. The median recession slack index has now increased significantly, confirming the improvement in the diffusion index.

The use of several market-based indicators makes the Trader Edge recession model more responsive than many other models. Relative to traditional economic variables, market-based data have important advantages: they are highly predictive, they are never restated, and there is no lag in receiving the data. The market-based variables improved in March and April, reversed direction briefly, but are again trending higher.

However, reduced recession risk does not eliminate downside risk in the equity markets. The U.S. equity market remains significantly overvalued (highest price-to-sales ratio ever). In addition, S&P earnings have declined by roughly 20% since 2015. Over the same period, the price of the S&P 500 index increased by over 10%. This divergence expanded P/E ratios to historically overvalued levels. While we may be poised for a period of earnings growth, earnings would have to increase by almost 30% (at current price levels) to return to the overvalued P/E levels of 2015.

Mark Hulbert's recent article, "The biggest threat to your money now? Ignoring the scent of a bear market," provides further insight into the relative value of the U.S. equity market. According to Hulbert's research, the current stock market is more overvalued that it was at 80-95% of the last century's bull-market peaks - according to six different valuation measures. For those betting on a Trump policy windfall, Hulbert makes a critical point:

I want to address one potential retort to my assertion that the stock market is overvalued: A big cut in the corporate tax rate, which Donald Trump has said he would implement, would give such a big boost to corporate after-tax profits that the stock market would no longer be overvalued.

Don’t believe it. According to an analysis from Ned Davis Research, a cut in the effective corporate tax rate to 15% would add an estimated $12.84 to the S&P 500’s SPX, -0.18% earnings per share. That would reduce the market’s P/E ratio from its current 22.9 to 20.2, when calculated based on trailing 12 months as-reported earnings. That would still be far higher than the 145-year average of 15.6 (according to data from Yale University’s Robert Shiller).

In other words, even if the corporate tax rate were reduced to 15% and corporate earnings were to increase by $12.84 per share, the market would still be overvalued. This rosy picture also assumes that Trump's protectionist trade policies caused no adverse earnings effects. Even in this ideal scenario, the resulting S&P earnings would still be well below the 2015 earnings with much higher prices - not a recipe for low-risk incremental returns.

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.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

The VIX futures project is much more complex than I initially anticipated. I have developed and am continuing to refine an integrated combination of risk, valuation, forecasting, and optimization models, which I am using to manage a new hedged VIX futures trading strategy.

Brian Johnson

]]>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. After the latest additions and deletions, the total number of explanatory recession model variables is now 21. 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 recent 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 1/1/2017 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).

In December 2014, for the first time since December 2012, one of the 21 explanatory variables indicated a recessionary environment. In 2015 and 2016, the number of explanatory variables indicating a recession varied between one and *eight*, with the peak occurring in January of 2016. Between February 2016 and May 2016, the number of explanatory variables indicating a recession varied between seven and four. From July through December, the number varied between one and two. The number of variables indicating a recession is currently one out of 21 (4.8%), down one from the prior month.

As I have explained before, 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 sharp rebound in the price of risk-assets in March/April improved the U.S. economic outlook, which significantly reduced the risk of an imminent U.S. recession. The subsequent market gains and improving economic data further reduced the risk of a US recession.

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.

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.

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.

In mid-2014, the revised median recession slack index peaked at 1.15, far above the warning level of 0.50. The recession slack index declined significantly in 2015, with a high of 0.94 and a low of 0.39. The recession slack index fell further in early 2016, reaching its low of 0.32 in February, before rebounding over the next few months.

The recession slack index is currently 1.22, which is up sharply from 0.96 at the end of November. The current value of 1.22 is the highest U.S. recession cushion since early 2013. However, much higher readings were recorded earlier in the recovery. Regardless, the amount of cushion is significant.

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*.

Note the very sharp rebound in the percentage of variables with increasing slack *before* the end of the 2007-2008 great recession. Roughly 90% of the variables demonstrated increasing slack by the end of the recession. Similarly, the percentage of variables with increasing slack plummeted below 30% *months before the 2007-2008 recession began*. The percentage plunged below 30% again in early 2015, well before the softening in the economy and corresponding market pull-back later that year. The most recent moving average percentage is 57.1%, which is down slightly from last month's revised value of 60.3%, which is not surprising given the unusually strong percentage last month. It is unusual for the moving average to exceed 60%. The current value of 57.1% is indicative of a strengthening U.S. economy.

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.

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 1/01/2017 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 decreased from 0.2% in November 2016 to 0.0% in December 2016. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

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 1/1/2006 to 1/01/2017 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 1/01/2017 was 2.7%, which was down slightly from the revised value of 3.5% at the end of November 2016.

January and February 2016 marked a potential tipping point in U.S. recession risk, but that risk declined significantly in March and April. Recession risk fluctuated in subsequent months, but continued to decline. The recent decrease in recession risk is supported by both market and non-market variables. The median recession slack index has now increased significantly, confirming the improvement in the diffusion index.

The use of several market-based indicators makes the Trader Edge recession model more responsive than many other models. Relative to traditional economic variables, market-based data have important advantages: they are highly predictive, they are never restated, and there is no lag in receiving the data. The market-based variables improved in March and April, reversed direction briefly, but are again trending higher.

However, reduced recession risk does not eliminate downside risk in the equity markets. The U.S. equity market remains significantly overvalued (highest price-to-sales ratio ever). In addition, S&P earnings have declined by roughly 20% since 2015. Over the same period, the price of the S&P 500 index increased by over 10%. This divergence expanded P/E ratios to historically overvalued levels. While we may be poised for a period of earnings growth, earnings would have to increase by almost 30% (at current price levels) to return to the overvalued P/E levels of 2015.

Mark Hulbert's recent article, "The biggest threat to your money now? Ignoring the scent of a bear market," provides further insight into the relative value of the U.S. equity market. According to Hulbert's research, the current stock market is more overvalued that it was at 80-95% of the last century's bull-market peaks - according to six different valuation measures. For those betting on a Trump policy windfall, Hulbert makes a critical point:

I want to address one potential retort to my assertion that the stock market is overvalued: A big cut in the corporate tax rate, which Donald Trump has said he would implement, would give such a big boost to corporate after-tax profits that the stock market would no longer be overvalued.

Don’t believe it. According to an analysis from Ned Davis Research, a cut in the effective corporate tax rate to 15% would add an estimated $12.84 to the S&P 500’s SPX, -0.18% earnings per share. That would reduce the market’s P/E ratio from its current 22.9 to 20.2, when calculated based on trailing 12 months as-reported earnings. That would still be far higher than the 145-year average of 15.6 (according to data from Yale University’s Robert Shiller).

In other words, even if the corporate tax rate were reduced to 15% and corporate earnings were to increase by $12.84 per share, the market would still be overvalued. This rosy picture also assumes that Trump's protectionist trade policies caused no adverse earnings effects. Even in this ideal scenario, the resulting S&P earnings would still be well below the 2015 earnings with much higher prices - not a recipe for low-risk incremental returns.

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.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

It is earnings season again, which is one of the best times to exploit earnings-related option pricing anomalies. Option traders are savvy, but Earnings volatility is a difficult concept and it affects every option in the matrix differently.When markets move, it is very difficult for market-makers to accurately apply the unique earnings volatility adjustments across the entire matrix. This creates value-added opportunities for option traders with the right tools.

Fortunately, there is a precise framework that quantifies the *exact* impact of earnings volatility on the value of every option. I introduced this analytical framework in my recent book,* Exploiting Earnings Volatility: An Innovative New Approach to Evaluating, Optimizing, and Trading Option Strategies to Profit from Earnings Announcements.*

"Exploiting Earnings Volatility also includes two Excel spreadsheets. The Basic spreadsheet employs minimal input data to estimate current and historical earnings volatility and utilizes those estimates to forecast future levels of implied volatility around earnings announcements. The Integrated spreadsheet includes a comprehensive volatility model that simultaneously integrates and quantifies every component of real-world implied volatility, including earnings volatility. This powerful tool allows the reader to identify the precise level of over or undervaluation of every option in the matrix and to accurately forecast future option prices and option strategy profits and losses before and after earnings announcements. The Integrated spreadsheet even includes an optimization tool designed to identify the option strategy with the highest level of return per unit of risk, based on the user’s specific assumptions."

After releasing *Exploiting Earnings Volatility* last year, I made a breakthrough in applying these tools in my own proprietary trading.

The optimization spreadsheet that accompanies the book uses a genetic algorithm that was designed for the free version Solver (included with Microsoft Excel). The free version of Solver is useful in identifying very attractive risk-adjusted earnings strategies, but I have taken this much further in my own trading.

I developed a linear version of the optimization problem that works with Lindo's "What's Best" software, which is much more powerful than the free version of Solver. The linear version still relies on data from the Integrated spreadsheet, but runs in a matter of seconds and *always* finds the single best optimal solution - rather than simply an attractive solution.

In addition, I modified the optimization problem to constrain each of the "True" or Market-Implied Greeks, to limit the maximum number of positions, and to remove the use of specific spreads. The new linear optimizer can now own a true portfolio of covered option positions, subject to my specific constraints. The resulting solutions are even more attractive than those identified by the free version of Solver.

Unfortunately, I cannot offer this version of the optimizer to the public, but the Integrated spreadsheet provides all of the required data for this optimization problem, which could easily be exported to a separate spreadsheet for optimization with What's Best or even a more powerful version of Solver. For readers who are willing to invest the time to take optimized earnings strategies to the next level, the payoffs are remarkable.

Brian Johnson

Copyright 2017 Trading Insights, LLC. All rights reserved.

Brian Johnson

]]>Each of the explanatory variables has predictive power individually; when combined together, the group of indicators is able to identify early recession warnings from a wide range of diverse market-based, fundamental, technical, and economic sources. After the latest additions and deletions, the total number of explanatory recession model variables is now 21. 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 recent 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 12/1/2016 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).

In December 2014, for the first time since December 2012, one of the 21 explanatory variables indicated a recessionary environment. In 2015 and 2016, the number of explanatory variables indicating a recession varied between one and *eight*, with the peak occurring in January of 2016. Between February 2016 and May 2016, the number of explanatory variables indicating a recession varied between seven and four. From July through November, the number varied between one and two. The number of variables indicating a recession is currently two out of 21 (9.5%), up one from the prior month.

As I have explained before, 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 sharp rebound in the price of risk-assets in March/April improved the U.S. economic outlook, which significantly reduced the risk of an imminent U.S. recession. The subsequent market gains and improving economic data further reduced the risk of a US recession.

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.

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.

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.

In mid-2014, the revised median recession slack index peaked at 1.15, far above the warning level of 0.50. The recession slack index declined significantly in 2015, with a high of 0.94 and a low of 0.39. The recession slack index fell further in early 2016, reaching its low of 0.32 in February, before rebounding over the next few months.

The recession slack index is currently 0.96, which has rebounded to a much more comfortable level, materially above the early-warning level of 0.50. The recession slack index increased from 0.95 to 0.96 in November and is now at its highest level in the last 15 months. Nevertheless, the current slack cushion is still below the levels experienced in 2013 and 2014.

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*.

Note the very sharp rebound in the percentage of variables with increasing slack *before* the end of the 2007-2008 great recession. Roughly 90% of the variables demonstrated increasing slack by the end of the recession. Similarly, the percentage of variables with increasing slack plummeted below 30% *months before the 2007-2008 recession began*. The percentage plunged below 30% again in early 2015, well before the softening in the economy and corresponding market pull-back later that year. The most recent moving average percentage is 61.9%, which is unusually strong and is indicative of a strengthening U.S. economy. I will continue to track this variable in future reports.

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.

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 12/01/2016 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 0.1% in October 2016 to 0.2% in November 2016. According to the model, the probability that the U.S. is *currently* in a recession continues to be extremely remote.

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 1/1/2006 to 12/01/2016 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 12/01/2016 was 3.5%, which was up slightly from the revised value of 2.7% at the end of October 2016.

January and February 2016 marked a potential tipping point in U.S. recession risk, but that risk declined significantly in March and April. Recession risk fluctuated in subsequent months, but continued to decline. The recent decrease in recession risk is supported by both market and non-market variables. The median recession slack index has increased significantly, confirming the improvement in the diffusion index.

The use of several market-based indicators makes the Trader Edge recession model more responsive than many other models. Relative to traditional economic variables, market-based data have important advantages: they are highly predictive, they are never restated, and there is no lag in receiving the data. The market-based variables improved in March and April, reversed direction briefly, but are again trending higher.

However, reduced recession risk does not eliminate downside risk in the equity markets. The U.S. equity market remains significantly overvalued (highest price-to-sales ratio ever). In addition, S&P earnings have declined by roughly 20% since 2015. Over the same period, the price of the S&P 500 index increased by approximately 7%. This divergence expanded P/E ratios to historically overvalued levels. While we may be poised for a period of earnings growth, earnings would have to increase by almost 30% (at current price levels) to return to the overvalued P/E levels of 2015.

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.

Brian Johnson

Copyright 2016 Trading Insights, LLC. All rights reserved.

I finally got a response from Amazon today. It appears that Amazon has corrected the problem. The new version displays the text and images correctly in all pre-publication and post-publication (look inside) viewers. Amazon does not provide the publisher a means of testing the e-book after publication, but the image problem appears to be resolved.

Amazon did not share with me the process initial customers should follow to download a copy of the new e-book file, but this should be possible. Please contact Amazon and inquire about a new download. New E-Book purchases should not be affected by the earlier conversion problem.

I apologize again for the inconvenience. Please let me know if you experience any problems with the new file.

The Kindle ($9.99) and print ($14.99) versions are now both available on Amazon. I apologize again for the inconvenience.

Brian Johnson

Copyright 2016 Trading Insights, LLC. All rights reserved.

Brian Johnson

Copyright 2016 Trading Insights, LLC. All Rights Reserved.

]]>Amazon has concluded that its conversion program is the probable cause of the problem, not the file format. Unfortunately, I am still waiting for them to resolve this issue. While Amazon is working to correct this problem, the Kindle ($9.99) version will be unavailable. However, the print ($14.99) version is still available via Amazon.

The image issue is very frustrating. Before publication, I uploaded the file and used Amazon's online previewer to test its functionality. I also downloaded the pre-publication .mobi file on my Kindle fire. The images worked *perfectly* on both. In addition, I verified the HTML image format in the file was identical to the format in my first two books. Finally, the images display correctly in the HTML file uploaded to Amazon.

The problem only surfaced *after* publication in Amazon’s look inside previewer and apparently in several other devices. Since I cannot replicate the problem with the original file, I need Amazon’s help to resolve the issue, which now appears to have occurred during their conversion process.

I have sent Amazon many emails on this issue. They initially promised me a response by November 18th and then again by November 24th. Both deadlines have passed with no response from Amazon.

I will post more information here on the status of the e-book as soon as it becomes available. Once the problem has been resolved, please contact Amazon for the latest version of the e-book if you downloaded the original version. I apologize again for the inconvenience.

Brian Johnson

Copyright 2016 Trading Insights, LLC. All rights reserved.

Brian Johnson

Copyright 2016 Trading Insights, LLC. All Rights Reserved.

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