Effat Golpar-Raboky; Zahra Heydari Marbari; Seyedeh Narges Mirei
Abstract
This study analyzes the trend of risk and profitability of 60 Iranian listed companies during the period of 2015 to 2022. The research data was extracted from the audited financial statements of these companies and includes key financial variables such as Debt to Equity ratio, Current Ratio, Return on ...
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This study analyzes the trend of risk and profitability of 60 Iranian listed companies during the period of 2015 to 2022. The research data was extracted from the audited financial statements of these companies and includes key financial variables such as Debt to Equity ratio, Current Ratio, Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin, Operating Margin, and Asset Turnover. After normalizing the indicators and numerical scoring based on weighted average, the risk level of the companies was calculated. Then, using a fuzzy logic model, the impact of liquidity and asset variables on profit before tax was analyzed. The results show that most companies are at a medium to low risk level, and in some companies, an upward trend in risk has been accompanied by a decrease in profitability. The application of the fuzzy model has been able to better model the non-linear and complex relationships between financial indicators and can be useful for assessing profitability potential. In addition, to assess the stability of companies' capital structure, fluctuations in the debt-to-equity ratio were analyzed using a 3-year moving average.
Hamideh Nasabzadeh; Mona Hesari
Abstract
This study introduces a hybrid trading strategy combining fuzzified Moving Average (Fuzzy MA) and Relative Strength Index (Fuzzy RSI) indicators for binary options in the Japanese financial market, demonstrating enhanced adaptability and profitability. Using fuzzy logic and Genetic Algorithms for parameter ...
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This study introduces a hybrid trading strategy combining fuzzified Moving Average (Fuzzy MA) and Relative Strength Index (Fuzzy RSI) indicators for binary options in the Japanese financial market, demonstrating enhanced adaptability and profitability. Using fuzzy logic and Genetic Algorithms for parameter optimization, the strategy aims to maximize profit while fairly evaluating different methods through multiple performance metrics, including the Sharpe ratio and drawdown. By adapting traditional indicators to capture the inherent uncertainty and volatility of the market, the research focuses on the EUR/USD currency pair. Three approaches are investigated: Fuzzy MA, Fuzzy RSI, and the combined Fuzzy MA+RSI strategy. Results show that the combined strategy significantly outperforms individual fuzzy indicators, offering superior adaptability and profitability across volatile market conditions. This study contributes to the field of binary options trading by showcasing the potential of fuzzy logic and optimization techniques, highlighting the importance of considering a range of performance metrics for a comprehensive evaluation of trading strategies.
