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ARIMA vs LSTM on NASDAQ stock exchange data
PublicationThis study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using...
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Exploring Cause-and-Effect Relationships Between Public Company Press Releases and Their Stock Prices
PublicationThe aim of the work is to design and implement a method of exploring the cause-and-effect relationships between company announcements and the stock prices on NASDAQ stock exchange, followed by a brief discussion. For this purpose, it was necessary to download the stock quotes of selected companies from the NASDAQ market from public web sources. Additionally, media messages related to selected companies had to be downloaded, and...
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Exploring Stock Traders’ Cognitive Biases: Research Design and Simulator Framework
PublicationCognitive bias is a phenomenon that has been extensively studied in stock trading and many other fields. This paper presents a framework for a Mobile Stock Trading Simulator (MSTS) that facilitates automatic investment in stocks with minimal human influence, by investigating the behavioral patterns and cognitive errors of stock market investors. The paper aims to determine whether investors’ investment strategies can be improved...