I am learning machine learning to use it for stock market price forecasting. While doing that I got this question. If we take any country with stock exchange they have more than one investment assests for trading and investing such as commodity, stock, futures,option,forex etc.
Lets say a quant wants to make a machine learning model for stock price prediction in US market. There are thousands of companies (about 2800) stocks are listed in NYSE.
How a quant will make a ML model for predicting stock price?
Currently what I am doing is that I teach a ML model using data(OHLC) from a particular stock(eg APPL between 1990-2016) and use this as my standard model. Then I use this standard model to predict AAPLs or other 2800's stock price(eg: IBM,F). But this method has many drawbacks and these are the once which comes into my mind 1) survival ship bias 2) trained only in one stock and only have knowledge about its pattern.
so how a quant trains a ML model? In this US stock market example he/she/it instead of creating a general model for 2800 stocks whether they create 2800 individual models for 2800 stocks and use it for prediction of that stock
example for predicting GM future price, a ML model is created for GM called GMmodel and then it is used for prediction GMs future price. am I right?
- Here I meant quant as individuals and institutions who use machine learning for trading