Individual Gurus

Is Warren Buffett’s track record explicable and replicable? In the June 2018 update of their paper entitled “Buffett’s Alpha”, Andrea Frazzini, David Kabiller and Lasse Pedersen model Warren Buffett’s exceptional investing performance based on replicating exposures of Berkshire Hathaway overall and of its publicly traded holdings to six factors. Four of the factors are those conventionally used to explain stock returns: market return, size, book-to-market ratio and momentum. The other two factors are betting-against-beta (buy low beta and avoid high beta) and quality (profitable, growing, dividend-paying). They further create portfolios that track Berkshire Hathaway’s factor exposures, leveraged to the same active risk as Berkshire Hathaway. Using monthly stock returns and accounting data for a broad sample of U.S. stocks, quarterly Berkshire Hathaway SEC Form 13F holdings and monthly returns for six factors specified above during October 1976 through March 2017, along with contemporaneous open-end active mutual fund performance data, they find that:

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What happens to the rankings of Guru Grades after weighting each forecast by forecast horizon and specificity? In their March 2017 paper entitled “Evaluation and Ranking of Market Forecasters”, David Bailey, Jonathan Borwein, Amir Salehipour and Marcos Lopez de Prado re-evaluate and re-rank market forecasters covered in Guru Grades after weighting each forecast by these two parameters. They employ original Guru Grades forecast data as the sample of forecasts, including assessments of the accuracy of each forecast. However, rather than weighting each forecast equally, they:

• Apply to each forecast a weight of 0.25, 0.50, 0.75 or 1.00 according to whether the forecast horizon is less than a month/indeterminate, 1-3 months, 3-9 months or greater than 9 months, respectively.
• Apply to each forecast a weight of either 0.5 for less specificity or 1.0 for more specificity.

Using a sample of 6,627 U.S. stock market forecasts by 68 forecasters from CXO Advisory Group LLC, they find that: Keep Reading

As summarized in “Twisting Buffett’s Preferred Stocks-bonds Allocation”: (1) Warren Buffett’s preferred fixed asset allocation of 90% stocks and 10% short‐term government bonds (90-10), rebalanced annually, is sensible for U.S. markets; and, (2) investors may be able to beat this allocation modestly by adding simple annual dynamics. Are findings similar internationally? In his July 2016 paper entitled “Global Asset Allocation in Retirement: Buffett’s Advice and a Simple Twist”, Javier Estrada extends his analysis of U.S. markets to 20 other countries. He assumes a 1,000 (local currency unit) nest egg to start a 30‐year retirement. Annual withdrawals (either 4% or 3% of the initial amount, adjusted annually for inflation) and rebalancing to the target allocation occur at the beginning of each year. The first 30‐year retirement interval is 1900‐1929 and the last 1985‐2014, for a total of 86 rolling intervals. He first compares performances of eight fixed stocks-bonds allocations, rebalanced annually, ranging from 100-0 to 30-70. He then compares a fixed 90-10 allocation to one with a dynamic twist that, at the end of each year, compares the stock market’s annualized total return over the last five years to its annualized total return since the beginning of the sample. If 5-year performance exceeds long-term performance, the annual withdrawal comes from stocks with rebalancing to 90-10. If long-term performance exceeds 5-year performance, the annual withdrawal comes from bonds with no portfolio rebalancing (giving stocks time to recover). He focuses on average portfolio failure rate (running out of money within 30 years) and average terminal wealth across countries as key performance metrics. Using annual stock and short-term government bond real total returns (adjusted by local inflation rate) in local currencies for 21 countries as compiled by Dimson‐Marsh‐Staunton for 1900 through 2014, he finds that: Keep Reading

Each year, Forbes calculates the performance of columnist recommendations assuming: (1) equal initial investments in each stock pick when published; (2) 1% trading friction for each purchase; and, (3) matching benchmark investments in the S&P 500 Index for each pick with no trading friction. Because matching benchmark investments are spread across the year, the benchmark performance is not the same as the annual performance of the S&P 500 Index. In his February 10, 2016 column for Forbes, Ken Fisher reports the performance of the recommendations he made in his column during 2014, as follows: “Calculated by Forbes, taking a hypothetical 1% brokerage commission haircut, my basket lagged the S&P 500 (without any brokerage haircut) by 5% equal dollars invested. This is the seventh year out of 20 that my picks have lagged and the third year in a row.” Using data from the 18 annual performance summary columns covering 1998 through 2015, we find that: Keep Reading

What is Warren Buffett’s preferred fixed asset allocation, and how does it perform? In his October 2015 paper entitled “Buffett’s Asset Allocation Advice: Take It … With a Twist”, Javier Estrada examines Warren Buffett’s 2013 implied endorsement of a fixed allocation of 90% stocks and 10% short‐term bonds (90/10). Specifically, he tests the performance of eight fixed asset allocations ranging from 100/0 to 30/70. Testing assumes a $1,000 nest egg at retirement, a withdrawal rate of 4% of the initial amount adjusted annually for inflation and a 30‐year retirement. At the beginning of each year, the retiree makes the annual withdrawal and rebalances to the target allocation. The first 30‐year retirement interval is 1900‐1929 and the last 1985‐2014, for a total of 86 rolling intervals. He further explores two adjustments (twists) to the 90/10 allocation:

1. T1 – If stocks are up the past year, take the annual withdrawal from stocks and rebalance to 90/10. If stocks are down, take the annual withdrawal from bonds and do not rebalance.
2. T2 – If stocks outperform bonds the past year, take the annual withdrawal from stocks and rebalance to 90/10. If stocks underperform, take the annual withdrawal from bonds and do not rebalance.

Using U.S. stock market and U.S. Treasury bill (T-bill) annual real total returns as compiled by Dimson‐Marsh‐Staunton for 1900 through 2014, he finds that: Keep Reading

In February 2008, a reader requested evaluation of the market timing value of Xyber9 trend forecasts for the U.S. stock market, as developed and presented by Robert Taylor, CEO of Trend Corporation, Inc. Our conclusion then was that the claimed accuracy rate probably derives not from forecasting skill but from defining targets that are hard to miss. In June 2014 via email, Robert Taylor reported: “I was nominated for the Nobel Memorial Prize in Economics in March of 2000 for proving the financial markets are not random, but rather predictable. During the past 8 and a half years my U.S. Market forecasts…averaged better than 80% accuracy, including several years with an accuracy of over 90%, while the worse year produced over 70% accuracy.” He claims an aggregate forecast accuracy “greater than 83%”. He bases his forecasts on “Taylor’s Law”: “The financial market’s expansion and contraction is qualitatively in direct correlation to the increases and decreases in gravitational fluctuations experienced at the human level. The increases in market price are in direct response to decreases in gravitational forces; the decreases in market price are in direct response to the increases in gravitational forces.” He measures forecast accuracy as follows:

“The lowest price on the last day of a downtrend will be below the highest price recorded on the last day of the previous uptrend. The highest price on the last day of the uptrend will be above the lowest price recorded on the last day of the previous down trend.”

Robert Taylor observed that the accuracies reported in Guru Grades are rather low and inquired about a review of his forecasts, which for the U.S. market typically project short-term (a few days) trends in the S&P 500 Index and/or SPDR S&P 500 (SPY). Using daily highs and lows for the S&P 500 Index during March 2006 (when the Xyber9 historical forecasts commence) through June 2014, we find that: Keep Reading

As of the end of 2012, we stopped adding forecasts to the Guru Grades database and published a preliminary report on findings. As of the end of 2013, we have completed the grading of all forecasts added during 2012 and now publish a final Guru Grades report.

The final report encompasses 6,582 graded forecasts from 68 gurus, adding 123 forecasts to the preliminary study (an increment of about 2%). The final report also incorporates a few minor corrections. The relatively small number of additional grades and corrections do not materially affect preliminary findings. Specifically:

• Terminal forecast accuracy is still 46.9%.
• Averaged by guru rather than across all forecasts, terminal accuracy is still 47.4%.

Accuracy rates change for individual gurus involved in the 123 incremental forecasts. We have updated the detailed forecast grading for these individuals. The histogram of guru accuracies in the final report is somewhat more symmetric than that in the preliminary report.

The final Guru Grades report will remain available indefinitely as a caution to investors on: (1) the (un)predictability of complex systems such as financial markets; and, (2) the risk or relying on grades self-assigned by students of financial markets.

Do quantitatively-driven mutual fund managers such as John P. Hussman, Ph.D, president of Hussman Investment Trust, successfully time the stock market? He describes his market timing approach as follows: “The key elements in evaluating securities and market conditions are ‘valuations’ and ‘market action.’ Each unique combination of these conditions results in a distinct Market Climate, with its own profile of expected return and risk.” His investment approach, as applied to funds such as Hussman Strategic Growth (HSGFX), is to “align our investment position with the prevailing Market Climate and shift that position when sufficient evidence of a Climate shift emerges.” Does this fund demonstrate good market timing? Using weekly dividend-adjusted returns for HSGFX during 11/21/00 (the earliest available) through 7/26/13 (660 weekly returns), along with contemporaneous weekly returns for the S&P 500 Index and the Russell 2000 Index as benchmarks, we find that: Keep Reading

In his 2013 book The Little Book of Market Myths: How to Profit by Avoiding the Investing Mistakes Everyone Else Makes, author Ken Fisher, chairman and CEO of Fisher Investments, “covers some of the most widely believed market and economic myths–ones that routinely cause folks to see the world wrongly, leading to investment errors.” His hope is that “the book helps you improve your investing results by helping you see the world a bit clearer. And I hope the examples included here inspire you to do some sleuthing on your own so that you can uncover still more market mythology.” Some notable points from the book are: Keep Reading

In a series of emails, Jim Rohrbach, president of Investment Models, Inc., expressed disagreement with the findings of “Jim Rohrbach’s Technical Timing Approach” and requested removal of the review. So that readers can assess the basis of his request, here are the verbatim  emails, with raw links replaced by descriptive links: Keep Reading