An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks
Journal Title: Informatics - Year 2018, Vol 5, Issue 3
Abstract
There have been many machine learning-based studies to forecast stock price trends. These studies attempted to extract input features mostly from the price information with little focus on the trading volume information. In addition, modeling parameters to specify a learning problem have not been intensively investigated. We herein develop an improved method by handling those limitations. Specifically, we generated input variables by considering both price and volume information with even weight. We also defined three modeling parameters: the input and the target window sizes and the profit threshold. These specify the input and target variables, between which the underlying functions are learned by multilayer perceptrons and support vector machines. We tested our approach over six stocks and 15 years and compared with the expected performance over all considered parameter specifications. Our approach dramatically improved the prediction accuracy over the expected performance. In addition, our approach was shown to be stably more profitable than both the expected performance and the buy-and-hold strategy. On the other hand, the performance was degraded when the input variables generated from the trading volume were excluded from learning. All these results validate the importance of the volume and the modeling parameters in stock trading prediction.
Authors and Affiliations
Thuy-An Dinh and Yung-Keun Kwon
Direct Visual Editing of Node Attributes in Graphs
There are many expressive visualization techniques for analyzing graphs. Yet, there is only little research on how existing visual representations can be employed to support data editing. An increasingly relevant task...
Medical and Para-Medical Personnel’ Perspectives on Home Health Care Technology
User-based research is strongly recommended in design for older adults. The aim of this paper is to focus the attention on the poorly explored role of medical and para-medical personnel’s perspective on home health car...
MaPSeq, A Service-Oriented Architecture for Genomics Research within an Academic Biomedical Research Institution
Genomics research presents technical, computational, and analytical challenges that are well recognized. Less recognized are the complex sociological, psychological, cultural, and political challenges that arise when g...
Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables
Wearable fitness trackers have gained a new level of popularity due to their ambient data gathering and analysis. This has signalled a trend toward self-efficacy and increased motivation among users of these devices. F...
Reinforcement Learning for Predictive Analytics in Smart Cities
The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal...