Abstract—This paper is intended as a follow up to a previous study of ours - Financial Time Series Forecasting - A Machine Learning Approach. The aforementioned study evaluates traditional machine learning techniques for the task of financial time series forecasting. In this paper, we attempt to make use of the same base dataset, with the difference of making use of a novel branch of machine learning techniques known as Deep Learning. These techniques have been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. These deep architectures are known to excel in tasks such as image and text recognition, but have not been exploited as much in the field of finance. In particular, for this study we will be making use of Convolutional Neural Networks (CNNs) to forecast the next period price direction with respect to the current price. We achieve an accuracy of 65% when forecasting the next month price direction and 60% for the next week price direction forecast. Whilst these results are anything but random, we are not able to match or surpass results obtained by industry leading techniques such as Logistic Regression and Support Vector Machines.
Index Terms—Data science, deep learning, fintech, machine learning, stock market.
The authors are with the Department of Artificial Intelligence, University of Malta, Malta (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Alexiei Dingli and Karl Sant Fournier, "Financial Time Series Forecasting – A Deep Learning Approach," International Journal of Machine Learning and Computing vol. 7, no. 5, pp. 118-122, 2017.