Labyrinth at Schonbrunn Garden, Vienna; Credit: Andrea Schaufler and Wikimedia Commons
Origin of long-short hedge funds
“Hedge funds” were pioneered some 70 years ago by Australian financier Alfred Winslow Jones. His idea was to combine a “long” position (i.e., one that profits if the securities go up in price), typically a set of growth stocks, with a “short” position (i.e., one that profits if the securities go down in price) on the other part of the portfolio. Jones argued that this “long-short” strategy is more stable that either of the two parts by themselves — if the overall market goes up, the long positions are likely to dominate, producing gains, whereas if the overall market goes down, the short positions are likely to soften the blow, perhaps even yielding overall gains in spite of the down market.
Jones’ original hedge fund performed very well. By 1966, Jones’ fund, over the previous five years, had outperformed the best mutual fund by 44%, and over the previous ten years had outperformed the best mutual fund by 87%. In the wake of Jones’ success, hundreds of new hedge funds were marketed, mostly following a similar long-short design.
Today the term “hedge fund” is used as an umbrella to include a wide range of investment funds, typically not open to the general public, that employ some special investment technique. Thousands of hedge funds are now in operation, with approximately $4 trillion in assets in the U.S. alone.
Long-short hedge funds continue to dominate the field. According to Preqin, over 30% of U.S. hedge funds, with roughly $683 billion in assets, specialize in a long-short strategy. Clients of these funds are willing to pay the steep fees (typically 2% of assets per year, plus 20% of any profits) charged by these funds for the skill required to effectively manage such a portfolio.
Long-short hedge funds take a hit in 2022
Two October 2022 Bloomberg News reports (see A and B) paint a somewhat discouraging picture of U.S. hedge funds, particularly those that employ a long-short or other discretionary stock-picking strategy. Only about 21% of these hedge funds have a positive return so far in 2022; approximately 27% have a 0% to 10% decline, and some 53% have a decline of over 10%. On average, U.S. equity hedge funds are down 15% through the end of September. This analysis also found that the long stocks in U.S. long-short hedge funds are, on average, down 31% year-to-date, while shorts are down only 20%. In other words, these funds are losing more on the disappointing performance of long positions than they are profiting from short positions. Such results are, of course, contrary to the usual expectations of hedge funds, namely to act as a hedge against significant losses.
Several specific hedge funds have had particularly disappointing performance in 2022. Chase Coleman’s Tiger Global lost 4.4% in September, extending its year-to-date decline to 52%. Similarly, Alex Sacredote’s Whale Rock fund dropped 6% in September, for a 41% year-to-date loss — six of Whale Rock’s largest holdings fell sharply, including Intuit (down 40%) and Microsoft (down 31%). Some funds, such as Sean Gamino’s Heron Bay Capital, have essentially been shuttered, merging their assets with some larger institution. A few hedge funds are doing well, such as the $24 billion Eureka Fund, which has reported a 4.2% year-to-date gain through September 2022; but these the exceptions rather than the rule.
Bloomberg reports that approximately $25 billion was withdrawn from long-short U.S. equity hedge funds from January 2022 through August. Net withdrawals over the past five years have topped $100 billion.
What went wrong? Bloomberg’s Nishant Kumar observes,
The decline of long-short equity hedge funds has been years in the making. Part of the problem may be that they got out of the habit of hedging. Many managers’ tactics were honed during a decade of low interest rates that powered rising share prices. They could make money with leveraged bets on high-flying stocks. Even so, as a group, equity hedge funds underperformed a simple S&P 500 index fund in strong bull market years like 2013 and 2019. Now portfolios loaded with growth and technology shares and just a smattering of shorts have captured most of the downside in this market.
Some predict a retrenchment of sorts, particularly in the long-short sector. Edoardo Rulli, deputy chief investment officer at UBS, for one, predicts that a reset in may be coming, leaving only the stronger players standing. “Formation of new funds will go down; the number of funds will possibly shrink. … It’s not a bad thing.”
Dismal record of market forecasters
These results are consistent with the dismal record of market forecasters, as we observed in an earlier Mathematical Investor article. Jeff Sommer, a financial writer for the New York Times, summarized the disappointing record of 2020 stock market forecasters as follows: “[A]s far as predicting the future goes, Wall Street’s record is remarkable for its ineptitude.” Sommer noted that in December 2019, the median consensus of an ensemble of prominent Wall Street analysts was that the S&P 500 index would rise 2.7% in 2020. The result was up 15% — a forecasting error of 12 percentage points. But that is only part of the story. In April, after the market crash in March, the revised consensus of a Bloomberg survey of analysts was that the market would fall 11% overall for the calendar year. So the final result (up 15%) was really off the consensus prediction by 26 percentage points.
Along this line, Nir Kaissar analyzed a set of predictions by market forecasters over a 17-year period from 1999 through 2016. He found that although there was a modest correlation between the average forecast and the year-end price of the S&P 500 index for the given year, these predictions were surprisingly unreliable during major shifts in the market. Strategists overestimated the S&P 500’s year-end price by 26.2 percent on average during the three recession years 2000 through 2002, yet they underestimated the index’s level by 10.6 percent for the initial recovery year 2003. A similar phenomenon was seen in 2008, when the strategists in his study overestimated the S&P 500’s year-end level by a whopping 64.3 percent in 2008, but then underestimated the index by 10.9 percent for the first half of 2009. In other words, as Kaissar lamented, the forecasts were least useful when they mattered most.
Our own 2017 analysis of market forecasters amply confirmed these findings. In a study of 68 market forecasters, including many very well-known figures in the field, we found an overall accuracy score of 48%, not significantly different than chance.
Can stock pickers pick profits?
These grim facts raise the question: Can stock pickers and other managers of equity-based hedge funds really pick stocks effectively? Some managers and organizations may be able to do this consistently, but the recent disappointing overall performance figures underscore that those with the requisite skill are, at the least, a shrinking population. Further, keep in mind the “survivorship bias” phenomena: stock-picking hedge fund managers who have not done particularly well have, in many cases, simply been replaced or seen their funds merged away. Thus, the number of managers and/or organizations that can truly manage a long-short portfolio or other actively managed portfolio sufficiently well to compensate for the steep fees may be even smaller than the commonly recognized.
All of this, of course, is entirely consistent with the efficient market hypothesis, as originally propounded by economists Eugene Fama, Lars Hansen and Robert Shiller: In an era of vastly more powerful computer-assisted investment operations (much more sophisticated than in Alfred Winslow Jones’ day), truly profitable opportunities are rarer than ever before. Thus, relatively unsophisticated efforts, whether by amateurs or professionals, are almost certain not to achieve market-beating results.
But as we emphasized in our 2022 Significance article How “backtest overfitting” in finance leads to false discoveries (see also this preprint), markets are not efficient by design; instead, market efficiency is a byproduct of fierce competition, with both winners and losers. Nowadays, the true winners in this competition are predominately those organizations, mostly quantitative hedge funds, that employ very sophisticated and rigorous statistical techniques, often including advanced machine learning methodologies, operating on numerous massive datasets, and running on state-of-the-art computing equipment. For example, Forbes’ list of the highest-earning hedge fund managers is dominated by those who employ advanced quantitative analysis, as opposed to those who employ mainly a conventional “discretionary” or “technical analysis” approach.
As we concluded in an earlier Mathematical Investor article, the best advice in today’s world is this: Go with big data and machine learning, or leave finance to those who do.