Hello. I’m currently working on my undergraduate capstone project and checking other schools’ featured projects for ideas. I was particularly interested in a couple of projects developed for stock price prediction using RNN:

• ML-based Investment Analytical Tool, from UBerkeley’s Master in Information and Data Science. Using stock prices from Yahoo, fundamental data from Intrino and news data from Google News they try to predict stock price evolution for some S&P 500 companies using LSTM networks.

• Machine Learning Engineer Nanodegree: Using only stock prices from Yahoo and also using LSTM and Stacked LSTM networks, they try to predict stock price evolution and also added an algorithm that recommends trades based on those predictions.

I’ve also checked some work based on Restricted Boltzmann Machines stacked over a Multi-layer Perceptron to classify stocks in “going up” and “going down” depending on whether the NN predicts them to go.

Having considered that, what do you think of the following idea: try to pull information from S&P 500 companies (both stock price evolution and fundamental data, as far back as I can) and try to come up with a good model using deep belief networks and deep learning networks to predict stock price evolution. I’d be creating a baseline model using logistic regression or multi-layer perceptron to compare performance.

Do you think it’s doable? Do you think it is an interesting project to carry on or would it be something that everyone knows leads nowhere because companies use Algorithm X and Model Y for this purpose? Do you think this can be done with limited hardware available (just a PC)?

Thank you.