Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study

Smaranda BELCIUG¹, Adrian SĂNDIȚĂ¹, Hariton COSTIN²*,
Silviu-Ioan BEJINARIU², Pericle Gabriel MATEI^3
1 University of Craiova, 13 Alexandru Ioan Cuza St., Craiova, 200585, Romania
sbelciug@inf.ucv.ro
² Institute of Computer Science, Romanian Academy, No. 2 Codrescu Street, Iași, 700481, Romania
hcostin@gmail.com (*Corresponding author)
³ Ferdinand I Military Technical Academy Bucharest, 39-49 George Coşbuc Blvd., Bucharest 5, 050141, Romania
pericle.matei@mta.ro

Abstract: This paper presents an intelligent decision system based on statistical learning that regards the tactics of an investor in predicting the next intraday stock price. Significant percentages can be won or lost depending on the tactics applied for buying/selling shares. This paper includes a case study regarding the efficiency of a group of machine learning techniques that work together in a competitive/collaborative manner with a view to achieving an overall price forecast for the next intraday transaction. In order to illustrate the advantages of this intelligent decision system this work provides a concrete example concerning the price forecast for the next intraday transaction for Transilvania Bank (TLV), the stock market at the Bucharest Stock Exchange (BVB), Romania. An important part of the decision system lies in the competitive stage, because only the best competitors are chosen for the ultimate decision-making process. In the collaborative stage of the statistical learning framework one uses a weighted voting system that outputs the final intraday stock price. The results obtained show that this intelligent system outperforms each stand-alone method.

Keywords: Artificial intelligence, Machine learning, Prediction methods, Statistical learning, Stock markets.

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CITE THIS PAPER AS:
Smaranda BELCIUG, Adrian SĂNDIȚĂ, Hariton COSTIN, Silviu-Ioan BEJINARIU, Pericle Gabriel MATEI, Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study, Studies in Informatics and Control, ISSN 1220-1766, vol. 30(2), pp. 43-54, 2021. https://doi.org/10.24846/v30i2y202104