StockBot: LSTM-Based Model for Accurate Stock Market Price Prediction and Decision Making

Stock market behavior forecasting is an important task that requires careful attention. The right prediction of stock prices can lead to attractive gains for investors. However, due to the complex nature of the data, predicting stock market behavior is challenging. Machine learning experts have developed various models to predict the future values of stock market groups.

Traditional machine learning techniques like support vector regression, random forests, and the bayesian model have been used in the past. But recently, researchers have started using deep learning models, specifically deep neural networks like LSTM and encoder-decoder, to predict stock market behavior. These models are more efficient in dealing with the time-series nature of the data.

Researchers from Stanford University introduced a new approach called StockBot. It is a price prediction model based on stacked LSTM that aims to help investors make daily decisions about whether to sell or buy stocks. The model is designed to predict stock prices for new stocks that don’t have enough historical data.

The Need for Accurate Stock Market Prediction

Predicting stock market behavior accurately is crucial for investors to make profitable decisions. However, the non-stationary, noisy, inter-dependent, and chaotic nature of stock market data makes it challenging to achieve accurate predictions. Machine learning experts have developed various models to address this challenge.

The Rise of Deep Learning Models

Traditional machine learning techniques have been used in the past for stock market prediction. But recently, deep learning models like LSTM and encoder-decoder have become popular. These models are more efficient in handling the time-series nature of stock market data.

StockBot: A New Approach

StockBot is a new approach proposed by researchers from Stanford University. It is a price prediction model based on stacked LSTM. Its aim is to predict stock prices for new stocks that don’t have sufficient historical data.

How StockBot Works

The authors of StockBot proposed training the model specifically to an industry type, such as “energy” or “finance.” They combined past and future prices from multiple tickers in the same industry to create a mixed training and/or test set. This allows the model to operate in two modes: predicting stock prices for all tickers or just a single one. A bot is used to perform buy or sell operations based on the predicted closing values. The algorithm followed by the bot is as follows:

  1. Calculate the δi changes given by δi = sign(ci+1 − ci), where ci is the stock price on the ith day.
  2. Check the evolutions of δi by following ∆i = δi+1 − δi.

The decision to buy or sell is made based on the value of ∆. A ∆ value of -2 indicates the end of a trough, prompting the bot to buy. Conversely, a ∆ value of 2 indicates the beginning of a dip, prompting the bot to sell.

Experimental Study

The authors of StockBot conducted an experimental study comparing different prediction models. They found that single or double-stacked LSTMs were the best architectures. They also found that predicting multiple days together was more interesting and simpler than predicting one day at a time. The decisions made by the bot outperformed even the most aggressive ETFs and investment products provided by investment firms.


StockBot is a new model for stock market prediction that offers two major advantages. Firstly, it can predict stocks that have a limited historical database by training on other firms in the same sector. Secondly, it provides decision support through a bot that knows when to buy or sell based on predicted closing values. This model has the potential to help investors make more informed decisions and maximize gains.

This article is based on the research article “StockBot: Using LSTMs to Predict Stock Prices”. All credit for this research goes to the researchers on this project. You can find the paper and the GitLab link in the sources provided.

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