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Machine Learning and Financial Markets


John, the big question is does ML have any place in making predictions in the financial markets. You mention “automated trade management” but that assumes you have some sort of system to choose the stocks in the first place.

As to “optimisation” all of ML seems to have that object at its heart. But is the prediction of financial instrument pricing a suitable topic for ML? If not, Keras et al is a waste of time.

As to “time series data” it presumably boils down to the independence or otherwise of the price movements of financial instruments. Determinism or randomness. Some claim they are a random walk others that patterns such as trends or mean reversion are to be found.

As a laymen in the world of ML (but an interested one) it seems to me so far that ML’s great successes have come in deterministic systems. The rules of chess, Go, cards, poker. Where the probabilities can be accurately assesses and odds forecasted.

Although some improvement seems to have been made in predicting chaotic systems such as the weather over the last 50 years would it be accurate to say that ML is better at predicting what is than what may be? Recommender systems on Netflix and Amazon rely on the fact that while people’s taste can change in general it doesn’t and the same people will carry on liking the same books and films.

Reinforcement learning to fly a toy helicopter seems to have been a great success but again I assume that there may be at least a partly deterministic environment involved with the exception of the wind; Newtonian laws presumably dominate.

So, out of the vast plethora of machine learning algos out there from Baysian classification to neural networks to genetic algorithms most of them actually, broadly, aim to do the same thing. And so the question remains that while ML may be of use in image recognition and astronomical classification is it actually of much use in predicting the behaviour of an apparently chaotic system such as the stock market?

Or is the stock market perhaps NOT random and chaotic? Certainly not in the long term – it is merely a reflection of increased wealth from 300 years of technological revolution.

But in particular have you personally had any success in generating alpha from using ML? And if so, what approach and class of ML algo do you prefer?

3 thoughts on “Machine Learning and Financial Markets

  1. How complex should a ML model (such as genotick) really be? Doesn’t it have to virtually be able to predict the ever-changing behaviour of millions of traders? If it can’t do that, isn’t it useless as it can’t handle change or predict anything reliably? Could it be a game where the only winning move is not to play and the most comlex model with millions of variables tells you to buy and hold?
    Just some thoughts, thanks

    1. I am more and more inclined to think that “trading” is, in the long term, a total waste of time unless one has some sort of built in advantage.

      Of course in reality there is no such thing as “buy and hold” because the vast majority of stocks go bust or are acquired. And an “index” is a kind of quantitative system. That said, yes, I wholeheartedly agree with you: buy and hold an index. Probably the MSCI World.

      I still believe one can apply simple timing to assets but for that you only need a trend line.

      Nonetheless I continue to investigate Genotick and the like, perhaps more out of philosophical curiosity than anything. Perhaps I will change my mind. I’m thinking of visiting a few of the better London based quant traders like Winton – I would be interested to see if and how they are using ML. And whether with any success.

      1. The built-in advantage of infices is that they are the most popular form of investment. I’d love to hear any insights you find on your ML journey, thanks again for all of your passion and articles!

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