Deep Learning Classifier with Piecewise Linear Activation Function: An Empirical Evaluation with Intraday Financial Data
Article Type
Research Article
Publication Title
Journal of Financial Data Science
Abstract
Price movement predictions of financial instruments using traditional time-series models with a predefined mathematical structure are common, but this method restricts their ability to learn latent patterns in the data. In recent times, artificial neural networks (ANN) have been able to learn complex hidden patterns from financial datasets using a highly nonlinear architecture. However, most experiments with neural networks require a lot of time to search a suitable network and subsequently train the network. The authors have developed a deep multilayer perceptron (MLP) classifier with a zero-centered piecewise linear unit activation that yielded better classification performance according to their accuracy metric and required consistently less training time compared to a similar MLP network with leaky rectified linear unit and tanh activation functions. The authors illustrate their technique with a large high-frequency dataset on selected bank shares from the Indian stock market. The authors also discuss the theoretical properties and advantages of their proposal.
First Page
94
Last Page
115
DOI
10.3905/jfds.2019.1.018
Publication Date
12-1-2020
Recommended Citation
Banerjee, Soham and Mukherjee, Diganta, "Deep Learning Classifier with Piecewise Linear Activation Function: An Empirical Evaluation with Intraday Financial Data" (2020). Journal Articles. 12.
https://digitalcommons.isical.ac.in/journal-articles/12