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An Intelligent System for Determination of Stop–Loss and Take–Profit Limits: A Dynamic Decision Learning Approach via Fuzzy Soft Set Approach
Emre Ari, Alp Ustundag, Mahmut Sami Sivri, Omer Faruk Gurcan, Omer Faruk Beyca and Ahmet Berkay Gultekin
Forecasting stock market prices presents a significant challenge due to the complex and unpredictable nature of the data involved. Accurate predictions can yield substantial financial benefits for traders and investors. The complexity, noise, and non-linearity of stock price data make this task especially difficult. Technological advancements have shifted trading strategies towards automated systems, emphasizing the need to determine optimal transaction points dynamically. A popular method to mitigate losses and increase gains involves using technical analysis to set predetermined thresholds for managing trades. This research focuses on dynamically establishing stop-loss (SL) and take-profit (TP) levels using an in-depth analysis of historical stock data, employing techniques such as standard deviation and Sharpe Ratios, and integrating the Fuzzy Soft Set (FSS) approach. The study categorizes TP/SL levels for strategies suited to either selling (short) or buying (long) positions. It compares the returns from end-of-day Open to Close with those from TP/SL levels to evaluate the effectiveness of these strategies. The primary goal is to refine trading strategies to navigate the volatile stock market, help traders and investors minimize losses and maximize profits, and advance trading practices in this dynamic field.
Keywords: Stop-Loss (SL), Take-Profit (TP), Artificial Intelligence (AI), Stock Prediction, Open to Close (OTC), Sharp Ratio (SR), Fuzzy Soft Sets (FSS)