Artificial intelligence prediction of stock prices using social media

TL;DR

This paper presents a neural network model using LSTM and social media data to predict stock prices, achieving over 76% accuracy through hyperparameter tuning and data augmentation.

Contribution

It introduces a novel approach combining GloVe embeddings pretrained on tweets with an augmentation strategy and hyperparameter optimization for stock prediction.

Findings

Final model accuracy of 76.14% on test data.

Hyperparameter tuning significantly improved model performance.

Data augmentation helped mitigate limited dataset size.

Abstract

The primary objective of this work is to develop a Neural Network based on LSTM to predict stock market movements using tweets. Word embeddings, used in the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained specifically on 2 billion tweets. To overcome the limited size of the dataset, an augmentation strategy is proposed to split each input sequence into 150 subsets. To achieve further improvements in the original configuration, hyperparameter optimisation is performed. The effects of variation in hyperparameters such as dropout rate, batch size, and LSTM hidden state output size are assessed individually. Furthermore, an exhaustive set of parameter combinations is examined to determine the optimal model configuration. The best performance on the validation dataset is achieved by hyperparameter combination 0.4,8,100 for the dropout, batch size, and hidden units respectively. The final testing accuracy of the model is 76.14%.