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Given a set of data very similar to the Motley Fool CAPS system, where individual users enter BUY and SELL recommendations on various equities. What I would like to do is show each recommendation and I guess somehow rate (1-5) as to whether it was good predictor<5> (ie. correlation coefficient = 1) of the future stock price (or eps or whatever) or a horrible predictor (ie. correlation coefficient = -1) or somewhere in between.

Each recommendation is tagged to a particular user so that it can be tracked over time. I can also track market direction (bullish/bearish) based off of something like an sp500 price. The components I think that would make sense in the model would be:

user

direction (long/short)

market direction

sector of stock

The thought is that some users are better in bull markets than a bear (and vice versa), and some are better at shorts than longs- and then a combination the above. I can automatically tag the market direction and sector (based on the market at the time and the equity being recommended).

The thought is that I could present a series of screens and allow me to rank each individual recommendation by displaying available data absolutely, market and sector outperformance for a specific time period out. I would follow a detailed list for ranking the stocks so that the ranking is as objective as possible. My assumption is that a single user is right no more than 57% of the time - but who knows.

I could load the system and say "Let's rank the recommendation as a predictor of stock value 90 days forward"; and that would represent a very explicit set of rankings.

NOW here is the crux - I want to create some sort of machine learning algorithm that can identify patterns over a series of time so that as recommendations stream into the application we maintain a ranking of that stock (ie. similar to correlation coefficient) as to the likelihood of that recommendation (in addition to the past series of recommendations ) will affect the price.

Now here is the super crux. I have never taken an AI class / read an AI book / never mind specific to machine learning. So I cam looking for guidance - sample or description of a similar system I could adapt. The place to look for info or any general help. Or even push me in the right direction to get started...

My hope is to implement this with F# and be able to impress my friends with a new skill set in F# with the implementation of machine learning and potentially something (application/source) I can include in a tech portfolio or blog space;

Thank you for any advice in advance.

1 Answer

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by (32.8k points)

You can use data mining to predict companies that will default (be unable to make debt payments) and shorting them to proceeds to buy shares in companies less likely to default. 

There are some techniques include survival analysis, sequence analysis (Hidden Markov Models, Conditional Random Fields, Sequential Association Rules), and classification/regression.

I hope this answer helps you with respect to machine learning.

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