Surprised not to see Andrew Lo's name associated this "groundbreaking research." It would be totally within his "working style" to trot out some fancy-sounding mathematics that pretends to solve some impossibly messy financial problem, and brag about it in some journal that real scientists (who sometimes collaborate with him in hopes of landing a job on Wall Street after their academic career starts to flounder) do not take all that seriously.
On a more serious note, the former deputy dean of the MIT Sloan School of Management (Gabriel Bitran) is currently serving time in a federal penitentiary with his son for a similar sort of chicanery (fancy mathematical pricing formulas that were complete bullshit according to SEC indictment) in order to screw his investors out of millions of dollars.
This was not long after Bitran narrowly escaped charges for sexually assaulting one of his secretaries at the same institution. In which case, I guess there may be some truth after all to the old saying about a man not "getting lucky" twice :)
I remember walking by Lo one afternoon and overhearing a medical doctor trying to catch up with him in order to tell him what a "lifetime admirer" he has been of Lo's work. It could have been anything from Lo's latest antics claiming that he knows how to "harness the power of greed" to cure cancer (the so-called Cancer-X project) to this poor fellow thinking that he had discovered a new way of beating the stock market (perhaps after reading this classy title that Lo published with one of his more attractive acolytes several years ago: The Heretics of Finance.)
The problem with predicting markets is that they suck up information.
When these predictions become public or anyone acts on them, the market automatically adjusts. Then the cycle repeats with people looking for even more leading indicators because the old ones are already priced in.
So, while it's interesting that these "alternative" data points seem correlated with prices, I'm dubious that anyone will profit off them at above-market returns for any sustained period of time.
This is a surprising but well-known phenomenon in mathematical modeling [1, 2] -- simple models tend to outperform complex models in complex situations.
Complex models can give you accuracy refinements during normal situations but need to be detuned/weighted-reduced during abnormal situations.
I'm going to guess its because it doesn't do it at a rate which matters, or in a manner that's actually scalable.
Sorry but I'm not impressed by this number, for various reasons:
- aggregate benchmark means some average of predictions from wall street investors, which is not the "state of the art" to beat, you should beat the best performing funds. Related: the best (and worst) funds are private and don't (all) report performance. Therefore, they are likely not included in the benchmark used and therefore the benchmark is biased.
- 57% doesn't seem that much (only slightly better than chance). Also, there is no variance number
- if they 'win' 1$ in 57% of the cases but 'lost' 2$ in the remaining 43% of the cases it's still a net loss. No numbers are given
- not clear if they are after-casting, i.e. whether they tuned the predictions after the fact happened. In other words: How well does the algorithm perform if you turn it on now and leave it for a year?
They're not beating funds, they're more accurately predicting company earnings than 'Wall Street analysts' -- people who work for investment banks and write stock notes for clients, not people who actually invest.
And the experiment assumes that the job of an analyst is to accurately predict earnings, which is frequently not the case -- analysts often lowball their estimates so that the companies they follow can 'beat and raise'.
What tips this is off is absurdly narrow forecast intervals. If I know that Company X's deal size is $10 million, then in what universe is it fair and reasonable for a forecast to be +/- a million bucks?
Others don't have customers, they trade with their own money (not just small-time traders, by the way).
"Fund beats S&P by 20% for 20 years straight" is a good story?
I am not understanding how a fund could remain open/transparent enough to build trust with potential investors. But still maintain secrecy over the years.
This basically amounts to "good data from other sources, provided granularly and processed intelligently, can predict asset movements."