43 ICI Bucharest © Copyright 2012-2021. All rights reserved ISSN: 1220-1766 eISSN: 1841-429X Studies in Informatics and Control, 30(2) 43-54, June 2021 https://doi.org/10.24846/v30i2y202104 1. Introduction A simplifed defnition of the bourse or stock exchange is: the market where diferent shares of stock, bonds and other fnancial instruments are being sold and bought. The stock market facilitates the money fow between investors and stock issuers. The moment when an investor believes that a certain company can potentially develop, she/he buys stock shares in order (a) to be part of that business or (b) to sell the stock shares at a higher price. An unwritten capital market law states: “Buy low, sell high”. An investor must have a strategy when deciding what needs to be bought/sold and also tactics for buying/selling. In what regards the strategy, an investor would buy stock shares if the issuer has good fnancial results; has clients/outlets for his products/services; gives dividends to shareholders; the executive management has achieved good results in recent years; the sector in which he operates is expanding. Obviously when the issuer is no longer satisfying these requirements the investor would consider selling. The stock market produces massive amounts of data. This data needs to be processed by machine learning (ML) techniques. In recent years more and more artifcial intelligence algorithms have been utilized to predict the stock market. A hybrid stock selection method that included stock prediction using extreme learning machine and stock scoring was applied on the A-share market of China (Yang et al., 2019). Switching regime, ANFIS and GARCH techniques have been employed in order to design a forecasting model for the stock market risk (Kristjanpoller & Mitchell, 2018). A combined fuzzy system and GARCH model was utilized to forecast stock market volatility (Hung, 2011). A hybrid model that uses neural networks (NNs) and fuzzy interference was used as a forecasting system (Badrul et al., 2015). Neural networks were yet again used to predict the stock market index in (Belciug & Săndiță, 2017), and (Moghaddam, Moghaddam & Esfandyari, 2016). A genetic algorithm-based approach to feature discretization in artifcial neural network has been used for the forecast of the stock price index (Kim & Han, 2000). In (Pehlivanli, Asikgil & Gulay, 2016) the support vector machines (SVMs) are combined with four flter methods based on di ferent metrics to obtain fltered features for forecasting stock prices for Istanbul Stock Exchange market. A hybrid model that consists of two linear models (autoregressive moving average model, exponential smoothing model) and a recurrent neural network were used for the prediction of stock returns (Rather, Agarwal & Sastry, 2015). An interesting approach was the analysis of twitter feeds that were found to be correlated with the Dow Jones Industrial Average (Bollen, Mao & Zeng, 2011). A hybrid time-series model was used for forecasting leading Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study Smaranda BELCIUG 1 , Adrian SĂNDIȚĂ 1 , Hariton COSTIN 2 *, Silviu-Ioan BEJINARIU 2 , 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. Signifcant percentages can be won or lost depending on the tactics applied for buying/selling shares. This paper includes a case study regarding the efciency 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 fnal intraday stock price. The results obtained show that this intelligent system outperforms each stand-alone method. Keywords: Artifcial intelligence, Machine learning, Prediction methods, Statistical learning, Stock markets.