Artificial intelligence in investing

Hi,

I would like to start a thread concerning the impact of artificial intelligence and our quantitative investment approach. As Google and other companies are making fast progress in creating AIs, and now AIs are starting to code better AIs than humans can. AI Software Learns to Make AI Software | MIT Technology Review

I know this question I am gonna ask is simple guessing and pure opinion:
Do you think AIs could outpace our investment development capabilities here at Portfolio123, eventually influencing the market so much so that Value or Momentum won’t work anymore?
Do you think it makes sense for Portfolio123 to include AI capabilities into the tool set?

FYI: I am not a technician or software engineer. So no idea how that would work.

My personal feeling is that artificial intelligence can very fast take over most of the financial markets, if the big players think that these programs can make far better returns than they can.

What do you think?

Interesting question. Here’s me $0.02.

I don’t think there’s really any such thing as artificial intelligence. That which is referred to as AI is, in my opinion, just plain better programming wrapped up in a jazzier marketing handle.

Ultimately, from the very first computer ever invented and the very first program to the most powerful hardware and software used today, there is one thing that has never changed: A computer does only one thing, it recognizes that a current is on or off. Everything else involves human-generated algorithms that decide how to translate things human want to do or know into on-off patterns, and how these dumb limited machines respond to the ever-changing on-off patterns given to it by humans. So actually, computers have not gained one iota of intelligence since day one. They do the exact same singular thing; deal with circuit on-off patterns.

What has changed, dramatically so, incredibly dramatically so, is the ability of humans to do more and more with on-off. We create more and more sophisticated types of instructions we can translate to on-off and we build machines that can process the patterns in better and faster ways. What commentators today refer to as AI is, ultimately, better HI (human intelligence) used to create better software and better hardware.

Self-driving cars, for example, are not at all self-driving. They depend on better human inputs (sensors of all kinds measuring all sorts of external things) responding in pre-programmed ways with success (getting from point A to point B) or failure (crashing, etc.) determined by the adequacy of the human efforts to figure out what sorts of inputs should be measured and what sorts of responses the dumb machine should make. If humans become good enough at these tasks, seemingly self-driving cars will become ubiquitous. If humans are unable to keep up with all the things they need to consider, then this will become a coo, but ultimately doomed science-fair project. It all depends on the humans. (By the way, self-flying planes, “fly by wire” is old hat and, I understand, used regularly even in everyday commercial flight. But given the “cost” of an algorithm that missed something that should have been taken into account, we’re still not willing to let it do everything and even when it’s in use, we still want humans in the cockpit who can over-ride; I suspect cars will evolve the same way – more and better incarnations of what we now think of as “cruise control”) but maintain the ultimate ability of the human to take over.

What does it mean for us? Probably more progress along the lines we’ve already been traversing. If Graham and Dodd could be resuscitated and shown the existing p123, they’d undoubtedly be dazzled by the AI already implemented. And there’s room for more. For example, we all know the limitations on testing insofar as it can tell the future. But I already roughed out ideas on how a future-oriented “backtester” could work, and I’m not even a computer guy. Ditto market timing. Its limitations are known. And so, too, are the things we could work on to address them by adding more intelligence. It’s just a matter of the skill set to get from back-of-the-envelope to actual use, which are considerable and beyond what I can deliver, but not necessarily beyond the skills of what others in the future will likely be able to deliver as each generation builds on the efforts of those that went before.

Can things like value or momentum ever be replaced? Maybe. Ultimately, the price of a stock always was and always will be the match between the prices at which willing buyers and willing sellers will transact. The role of financial theory (from which value, momentum, etc. spring) is to help us try to anticipate the behavior of willing buyers and willing sellers. The more effective we get in this sort of thing, the better our investment results will be and if it turns out that future buyers and sellers make choices that bear no relationship to any currently-known financial theory, then so be it (and the field of behavioral finance, including the work of Robert Shiller I drew upon for what I presented here as noise trading is already working along these lines).

Ultimately, however, there is no AI. It still comes down to better and better and better HI in control of still-dumb machines that still know nothing more than on-off.

A website has already been trying crowd-sourced machine learning algorithms to use in investing. The website is:

https://numer.ai

The user generated submissions (on anonymized data) apparently direct the investment of a hedge fund.

Here’s a longer write-up of how the site operates:

https://medium.com/numerai/rogue-machine-intelligence-and-a-new-kind-of-hedge-fund-7b208deec5f0#.slsmgy6rb

Marc makes some good points. And I might even argue that there will always be a place for people like him to make money.

Marc makes money by finding stocks that do not reflect their true value. When he goes long he finds stocks that are undervalued. To think there will no longer be a place for him in the future you have to believe one of 2 things. First, you have to believe that computers can find the true value of an equity better than Marc can. Or you have to believe that AI will reduce market inefficiencies and always drive stocks to their “true” value.

Finance theory is a well advanced area of study. Can a computer do better than a trained professional at finding the “true” value of a company. This could be argued at length. Let me even accept that maybe a machine can do marginally better. But I say not much better than Marc.

It is the other assumption that one really has to think about. The AIs will not be limited to buying undervalued companies hoping they will correct to their "true’ value. The AI will almost certainly be buying on “noise” factors too. Momentum being just one of theses factors.

Taking momentum as an example, the AIs will almost certainly be buying overvalued companies that it expects to continue to appreciate. Also,the AIs will almost certainly be selling companies that are undervalued already but are likely to continue their decline due to momentum or other factors. If this is true then the computers will, at times, increase the market inefficiencies. Marc will be there to capitalize on the inefficiencies or mis-pricings.

When we think of AI we often think that they will rid the market inefficiencies and make every equity fairly priced. But at times AIs may magnify the market inefficiencies.

Think of it this way. Would a really smart machine have been selling stocks short in the early part of the tech bubble? I argue that in the early stages the AIs would have been buying stocks and contributing to the bubble—at least in the early stages.

I am not sure that AIs will necessarily reduce the inefficiencies. Not always anyway. Following the herd, however, will almost certainly be more dangerous in the future. The machines will almost certainly be effective at picking off the weaker members of the herd—like any predator. Okay, I probably watched too may Terminator movies as a child.

Here’s my two cents.

I operate on the assumption that there will never be a market that’s 100% efficient. There will always be inefficiencies that very clever investors can exploit.

I do, however, think that the increasing use of AI can alter those inefficiencies a great deal, and can enable those who use AI in the right way to exploit them far better for a period of time.

A lot of what I do using P123 could be done far more efficiently if I had the programming skills and a superfast computer. For example, I’d love to ask a computer to design millions of permutations of 500 different factors and test them over periods of different lengths and with different numbers of stock holdings and using different measures of risk-adjusted returns and, using correlation results, figure out the ideal backtest for OOS performance of a 15-stock portfolio, and then perform that backtest on those millions of permutations and come to the ideal ranking system.

Now could an AI machine come up with the idea I just stated? I doubt it.

And that article on Numerai strikes me as smoke-and-mirrors wishful thinking.

  • Yuval

AI is a generic word with some marketing connotation for a set of cutting-edge software programming techniques at a point in time.
The concept of AI is making software able to adapt to unplanned situations, that’s all. Software able to change its own structure is not new, but new techniques give a framework to solve more generic problems. I was working on an “AI” project for a national nuclear agency in 1991. It would certainly not been labelled as AI today. In the 90s AI was grouping techniques such as rule-based system (P123 is AI under this definition), fuzzy logics and neural networks. Now that you have fuzzy logics in washing machines, it is not considered AI any more. What people call AI is genetic algorithms, machine learning… New techniques will certainly have an impact on intraday/intraweek trading strategies. I may be wrong, but I doubt it will have a big impact on longer time units. HFT has not killed value investing.

If people can’t even stick with strategies they understand during inevitable periods of underperformance, imagine how quickly they’ll ditch the magic AI robot when it underperforms.

As others have said, AI is a marketing term, that actually does not have any intelligence per se, personally I prefer the term ML, machine learning, and even that is another marketing gimmick. In the end AI, or ML today is simply a variety of techniques to curvefit data (that does not sound exciting though).

The tasks in which ML has been most successful are problems average humans can do roughly as well, because they are deterministic. i.e. image recognition, voice recognition, etc. Give it parameters XYZ (the pixels of an image) and the data will be deterministic in that it will always result in an image of a cat, a dog or something else. The market works differently, it is impossible to have all the parameters that will determine prices, at least for now, so even if you have lots and lots of parameters, XYZ data today might result in market up, and tomorrow market down.

You can certainly find inefficiencies through quantitative data, but usually a simple regression will be enough to find these relationships.

patrick o’shaugnessey has a podcast and a recent episode dealt with machine learning in automated investment strategies.

https://play.google.com/music/m/Dgmg5yw2tbd7icpw32rqlero3vu?t=Jeremiah_Lowin__Machine_Intelligence_and_Risk_Management_-_Invest_Like_the_Best_EP20-Invest_Like_the

My main takeaway … if you think overfitting is an easy trap now, wait until you get machine learning involved and you have an automated process repeating an overfitted piece of logic iteratively from false pattern recognition.

So has anybody here read “The Moon is a Harsh Mistress” by Robert Heinlein?

It’s a scifi takeoff on the American revolution only here, the moon (a penal colony) declares indolence from earth. One of the protagonists is a “self aware” computer that sort-of becomes a leader of the movement. Heinlein can’t always keep that story point as clean as one might like, but it might be a fun read for those who are into this topic.

If you would blend machine learning with value investing, you could easily end up with something very similar to the ranking systems that I use in my Smart Alpha models.

CLARIFICATION:

Predicting stock returns is EXACTLY the type of problem that AI does better than humans.

However, AI is still more of an art than a science. Success depends on picking the right framework for the job. I spent at least six years on this site developing an algorithm that builds stock-picking systems.