Stock market prediction using ensemble of graph theory, machine learning and deep learning models
Publication Date
1-12-2020
Document Type
Conference Proceeding
Publication Title
ACM International Conference Proceeding Series
DOI
10.1145/3378936.3378972
First Page
85
Last Page
92
Abstract
Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this paper, a novel approach is proposed using graph theory. This approach leverages Spatio-temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model.
Keywords
Big data analytics, Deep learning, Financial networks, Graph theory, Machine learning, Spatiotemporal, Stock market, Time series forecasting
Department
Computer Science; Economics
Recommended Citation
Pratik Patil, Ching Seh M. Wu, Katerina Potika, and Marjan Orang. "Stock market prediction using ensemble of graph theory, machine learning and deep learning models" ACM International Conference Proceeding Series (2020): 85-92. https://doi.org/10.1145/3378936.3378972
