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
Keywords
Related Articles
- EP ID EP44147
- DOI https://doi.org/10.3390/informatics5030036
- Views 286
- Downloads 0
Back-Off Time Calculation Algorithms in WSN
In a Mobile Wireless Sensor Mesh Network (MWSMN), based on the IEEE 802.15.4 standard, low power consumption is vitally important since the network devices are mostly battery driven. This is especially true for devices...
Alt-Splice Gene Predictor Using Multitrack-Clique Analysis: Verification of Statistical Support for Modelling in Genomes of Multicellular Eukaryotes
One of the main limitations of the typical hidden Markov model (HMM) implementation for gene structure identification is that a single structure is identified on a given sequence of genomic data—i.e., identification of...
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...
Multiple-Criteria Decision Support for a Sustainable Supply Chain: Applications to the Fashion Industry
With increasing globalization and international cooperation, the importance of sustainability management across supply chains has received much attention by companies across various industries. Companies therefore stri...
Web Apps Come of Age for Molecular Sciences
Whereas server-side programs are essential to maintain databases and run data analysis pipelines and simulations, client-side web-based computing tools are also important as they allow users to access, visualize and an...