Machine learning the mundane
Try to think of the "buzziest", buzzwords you can think of. Support vector machines? No. Random Forests? No. However, group these together (and much more) under the umbrella of "machine learning", and suddenly we have created a buzzword! The basic idea of many of the techniques which underpin machine learning is find relationships between variables. In particular, we do not need to specify the form of the relationship beforehand (eg. linear). In fact the relationships might be highly non-linear. We need to be careful though that we are not data mining too much, and end up fitting to noise. For example, let's say that there's generally linear relationship between two variables. If we have a very small number of observations, the "best" fit found using a machine learning technique could involve actually joining up the points, as opposed to doing a straight line of best fit. This will obviously fit nicely in our sample, but then when we throw new data in, we will likely find that a straight line of best fit could work better. The difficulty with finance is that relationships tend to be less stable (financial time series are not stationary), and often we don't have sufficient data. There is a trade off between optimal solutions in sample, and robustness out-of-sample when we create a trading rule. In other words, we want to have at least some fitting, but equally, overdone and it will just look on paper and not when we're actually running real money.
A funky way to use machine learning might be to infer trading rules directly and this is often the most obvious question to ask. Throw in many, many features based on market data and similar datasets, and see if it can infer trading rule which maximise P&L. However, this is very difficult to get to work, in a live out-of-sample setting for the reasons described above. In practice, we have a better chance of success (out-of-sample) if we create features which we think have an intuitive rationale. Some of these might be based on alternative datasets. We might also use machine learning to classify different types of market, to help us to overweight or underweight signals.
Perhaps a less glamorous way to use machine learning in finance, is to help with the mundane tasks. ...
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