Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Information Technology Management, Electronic Branch, Islamic Azad University, Tehran, Iran.
Comput Intell Neurosci. 2022 Aug 29;2022:5974842. doi: 10.1155/2022/5974842. eCollection 2022.
The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels-Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.
本文基于不同过滤方法的组合，例如基于技术分析的交易规则优化（ROC、SMA、EMA、WMA 和 MACD 六个级别——非常非常弱（VVW）、非常弱（VW）、弱（W）、强（S）、非常强（VS）和非常非常强（VVS））、马尔可夫链和机器学习（随机森林和支持向量机）过滤股票交易所，并提供 2011 年至 2020 年之间的买入信号。按照每种过滤方法的组合比例，发出买入信号，并基于均值-方差（M-V）模型，根据增加投资组合回报和最小化投资组合风险来优化股票投资组合。根据这一点，从在德黑兰证券交易所上市的 480 家公司中，选择了 85 家活跃公司，并基于两种算法（MOGWO 和 NSGA II）进行股票投资组合优化。分析结果表明，使用 SVM 学习机导致的相关错误比随机森林方法小。因此，此方法用于预测股票价格。根据结果可以看出，如果对公司的股票进行过滤，可以降低交易风险并增加股票投资组合的回报。此外，如果同时应用两种过滤方法，股票投资组合的回报略有增加，而风险增加。在分析中，MOGWO 算法的股票回报率为 133.13%，风险为 3.346%，而 NSGA II 算法的股票投资组合回报率为 107.73%，风险为 1.459%。解决方案方法的比较表明，MOGWO 算法在股票投资组合优化方面具有高效性。
