StockBot: Using LSTMs to Predict Stock Prices

TL;DR

This paper introduces a novel LSTM-based model for stock price prediction and a decision-making bot that outperforms market benchmarks, achieving significantly higher gains.

Contribution

It presents a new LSTM-based approach for accurate stock prediction and a StockBot that strategically makes buy/sell decisions to maximize profits.

Findings

StockBot outperforms the market with ~15 times higher gains.

The LSTM model provides accurate market forecasts.

The approach effectively captures non-linear market trends.

Abstract

The evaluation of the financial markets to predict their behaviour have been attempted using a number of approaches, to make smart and profitable investment decisions. Owing to the highly non-linear trends and inter-dependencies, it is often difficult to develop a statistical approach that elucidates the market behaviour entirely. To this end, we present a long-short term memory (LSTM) based model that leverages the sequential structure of the time-series data to provide an accurate market forecast. We then develop a decision making StockBot that buys/sells stocks at the end of the day with the goal of maximizing profits. We successfully demonstrate an accurate prediction model, as a result of which our StockBot can outpace the market and can strategize for gains that are ~15 times higher than the most aggressive ETFs in the market.