Despite the terrible record of hedge funds in beating simple passive investing it seems that they keep trying "new strategies.
The track record of hedge funds is poor and even that database has a large "survivorship bias" . So may funds close or the managers reinvent themselves with new fund names and new strategies it is literally impossible to have reliable data on their performance.
Hedge funds have been closing particularly those with the traditional long short strategy (long favored stocks/short unfavored stocks. It also seems that some funds are using the long/short strategy based on smart beta factors.
Traditional long-short hedge funds and their managers have recently felt like an endangered species, with many giving up after finding it increasingly hard to turn a profit, as Bloomberg News detailed Tuesday. The latest to throw in the towel is John Burbank, who hit it big after the financial crisis, but this week announced he was shutting his hedge fund Passport Capital. After two years of poor returns, recently from investments in Saudi Arabia, Burbank said he needed to rethink how he invests. He may launch a bitcoin fund.
But hope springs eternal new hedge funds emerge peddling new strategies and if there is a short period of success money rolls in. Hedge funds are usually limited legally to what are called "sophisticated investors". But their behavior shows they are anything but.
Some funds hit by the long bull market are switching to long only strategies based on computer stock screening...something that could be done more cheaply and more transparently with smart beta funds.
Joel Greenblatt for years a successful stock picking value investor has created a blended portfolio combining the passive strategy of part of the money invested in the S+P 500 and part of the portfolio long stocks he favors and short those that he sees as overvalued..a hybrid of passive and long short. Of course any strategy involving short positions has potential for large losses and it is unclear that the long and short positions can offset each other. Again with smart beta ETFs with fees of less than .10% and broad index ETFs with fees near zero it may well be investors are better off with a combination of a broad index combined with smart beta for lower cost and volatility with potential for higher returns as well
John Burbank who made returns of 200% betting against mortgage backed securities in 2007. Funds flowed into his funds based on that success. But not surprisingly the success of 2007 didn't translate into long term success in the stock and bond markets...and the money flowed out assets are now under $1 billion .
Passport has struggled for years to equal its crisis-era success, and now manages a fraction of what was once a roughly $5 billion war chest. Several prospective investors approached about the new venture say the firm’s flagship fund was down double digits last year, and was down again in 2017.
Passport said in a letter to investors late Monday it would close its main fund, having earlier told some investors it expected further redemptions by year-end, a person close to the firm said.
Burbank has indicated he is moving some of his assets in a fund concentrating on cryptocurrencies. It is hard to see how anyone even the most experienced trader can claim any expertise in tis area.
Other funds are moving into esoteric non stock investments according to Bloomberg such as "
One of the new type of hedge funds are based on
The Future Is Bumpy: High-Tech Hedge Fund Hits Limits of Robot Stock Picking
Voleon is among investors deploying machine learning, a technology in which computers develop trading strategies. It’s harder than it sounds
Machine learning, a set of techniques that empowers computers to find patterns in data without using rules prescribed by humans, has been producing advances in a range of fields, from robotics to weather forecasting to language translation. The technique is at the heart of efforts to build self-driving cars.
Why not use it to crack financial markets? The notion has led to an arms race of sorts, as multibillion-dollar investment firms that already were mathematically focused have been signing up the smartest computer scientists and statisticians they can find.
The gambit seems to be working for two of this year’s top-performing hedge funds. Quantitative Investment Management LLC, up 68% this year in its biggest fund, attributes its success to the technique. Teza Capital Management LLC credits machine learning in part for its more than 50% gain so far this year.
Yet instances of parlaying machine learning into investing success over a sustained period are rare. Much of the reason can been seen in the yearslong struggle of Voleon, one of the first investment firms to commit itself fully to the kind of machine learning that is producing many advances in other fields.
These computer experts discovered what anyone in the financial markets could have told them. Markets are complex and dynamic with patterns, particularly shorter term which constantly change. Nothing based on past data could have predicted the 2008 financial crisis..although a few people such as those profiled in the movie the Big Short did so through rolling up their sleeves with old fashioned research. Not only is there lack of consistency in returns even in those years of high returns it is unclear what the volatility is and if leverage is used. Ultimately leverage is the downfall of many strategies that show eye popping short term returns.
The WSJ continues
The basic problem they faced was that markets are so chaotic. Machine-learning systems have been best applied so far to situations where patterns are more of a repeating nature, and thus easier to discern, such as in playing the ancient game of Go or even guiding a driverless car. The financial markets are “noisier”—continually being affected by new events, the relationships among which are frequently shifting.
The protean nature of the markets also means yesterday’s relationships can vanish as investors figure them out and move to take advantage of them. This isn’t a problem faced by machine learning in other fields, such as converting human speech to text; computer engineers can count on human speech continuing to have the same basic characteristics
George Soros the master trader who eschewed such techniques wrote of the reflexivity of markets years ago. And Richard Brookstaber who worked for years in financial derivatives for major investment banks makes arguments for the complexity of markets in his recent book .The End of Theory which focuses on the influence of human interactions on financial markets...the exact opposite of combing through machine learning
It's hard not to look at the constantly changing hot strategies among hedge funds and not conclude that individuals are not better off not fitting the category of "sophisticated investors" and getting access to these funds. Anyone remember Long Term Capital with its Nobel Prize winning and top Wall Street brains at the helm ?