Time series methods have been applied to economic data since time immemorial. There is nothing particularly newsworthy here beyond the fact that another wannabe mathematician is trying to find a way to make a quick buck off of unsuspecting investors in the markets.
"Outperformance" is an hackneyed phrase that has long lost any real meaning when uttered by an MBA; there are just too many ways to cook the books in order to produce the desired outcome.<p>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.<p>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.<p>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 :)
> The model makes use of “alternative data” – such as credit card purchase data, smartphone location data, satellite imagery and so on<p>The problem with predicting markets is that they suck up information.<p>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.<p>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.
Why would anyone say that? Why wouldn't you just make a few billion and prove it?<p>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.
> On the 34 companies tested, the MIT researchers' model beat an aggregate Wall Street analyst benchmark in 57.2 percent of quarterly predictions tested in the experiment.<p>Sorry but I'm not impressed by this number, for various reasons:<p>- 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.<p>- 57% doesn't seem that much (only slightly better than chance). Also, there is no variance number<p>- 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<p>- 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?
MIT says a lot of things.<p>This basically amounts to "good data from other sources, provided granularly and processed intelligently, can predict asset movements."
This was discussed here just less than a month ago:<p><a href="https://news.ycombinator.com/item?id=21894862" rel="nofollow">https://news.ycombinator.com/item?id=21894862</a>
Predicting key metrics better than analysts with alternative data is very easy, the hard part is making actionable trading insights. Alternative data gleamed metrics are only a small part of the overall market price.
Url changed from <a href="https://www.enterpriseai.news/2020/01/22/mit-says-its-forecasting-model-outperforms-wall-street-benchmark/" rel="nofollow">https://www.enterpriseai.news/2020/01/22/mit-says-its-foreca...</a>, which points to this.