Abstract
Cognitive 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 by detecting investment threats and reducing investment errors based on investors’ transaction histories. To accomplish this, we built a stock exchange simulator and implemented a decision tree to classify cognitive biases into one of six categories. By incorporating the behavioral patterns and cognitive biases of stock market investors into the MSTS's architecture, and by implementing a decision tree and stock exchange simulator, we can minimize the impact of human influence on automatic investments.
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