Data Scientist turning Quant (III) — Using LSTM Neural Networks to Predict Tomorrow’s Stock Price
Short background about me. I am a data scientist by trade and my investment portfolio turned from great (thanks to the 10-year bull market rally) to not-so-great. For the purpose of these blog posts, I am naive and assume that using the tools I use every day in my job could help me to be a better investor in the future. My goal is to experiment and to learn, and I would like to take you, the reader, on a ride with me in this exploratory multi-part series on predicting the stock market.
While not necessary, I suggest you to read the series in chronological order, starting with Part I about my motivation. In Part II I tried to predict 1-day and 7-days stock direction moves using Logistic Regression and Random Forest models and various well-known technical analysis indicators like RSI and moving-average crossings.
The models’ performance was far from being convincing. TA indicators in were found to be non-predictive. Interestingly enough, I found past returns to have the highest feature importance when it came to predict future stock directions.
I asked myself: How well would I do predicting the stock price by solely using past price information? In this Part III, I will do exactly that! I will train and explore various Long…
