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Machine Learning for Finance
book

Machine Learning for Finance

by James Le, Jannes Klaas
May 2019
Intermediate to advanced content levelIntermediate to advanced
456 pages
11h 38m
English
Packt Publishing
Content preview from Machine Learning for Finance

Advantage actor-critic models

Q-learning, as we saw in the previous sections, is quite useful but it does have its drawbacks. For example, as we have to estimate a Q value for each action, there has to be a discrete, limited set of actions. So, what if the action space is continuous or extremely large? Say you are using an RL algorithm to build a portfolio of stocks.

In this case, even if your universe of stocks consisted only of two stocks, say, AMZN and AAPL, there would be a huge amount of ways to balance them: 10% AMZN and 90% AAPL, 11% AMZM and 89% AAPL, and so on. If your universe gets bigger, the amount of ways you can combine stocks explodes.

A workaround to having to select from such an action space is to learn the policy, , directly. Once ...

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Publisher Resources

ISBN: 9781789136364Supplemental Content