Hybrid Metaheuristic Algorithms and Artificial Neural Networks for Stock Price Prediction
محل انتشار: چهارمین کنفرانس ملی پژوهش در حسابداری و مدیریت
سال انتشار: 1399
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 530
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شناسه ملی سند علمی:
MANAGECONF04_102
تاریخ نمایه سازی: 22 مرداد 1399
چکیده مقاله:
Stock price prediction is a very important topic for investors and corporations because through its forecasting, they can increase their profits and raise their capital. Therefore, investigation of the stock price movements and exact determination of the future of the stocks is at the center of attention for financial researchers. However, stock price movements follow multiple complicated factors which result in difficulty of forecasting the exact stock price movements. Consequently, more exact and innovative models and methods to prediction of stock price are developed in recent years. The aim of this study is to evaluate the efficiency of using technical indicators such as closing price, lowest price, highest price, exponential moving average, etc. in prediction of stock prices. For more exact examination of the relationship between technical indicators and stock prices in the considered time intervals, we used Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Harmony Search (HS) algorithms as metaheuristic methods for stock price prediction. The GA is used for selection of the best optimization indicators. Beside selection of optimized indicators, we used PSO and HS to training the neural network, minimizing the network error, obtaining optimized weights and the best number of hidden layer simultaneously. In order to compare the proposed models performance and to choose the best model according to the amount of error, we used eight estimation criteria for error assessment. In experimental results show that a hybrid ANN-HS algorithm has the best performance. Finally, we used Run tests as a non-parametric test for testing the EMH in a weak form.
کلیدواژه ها:
Technical Indicators ، Artificial Neural Network ، Genetic Algorithm ، Harmony Search ، particle Swarm Optimization algorithm
نویسندگان
Milad Shahvaroughi Farahani
MSc in Financial Engineering, Department of Financial Management, Khatam University, Tehran, Iran
Seyed Hossein Razavi Haji Agha
Assistant Professor of Management, Khatam University, Tehran, Iran
Saeed Rahimian
Assistant Professor of Financial Management, Khatam University, Tehran, Iran
Babak Majidi
Assistant Professor of Computer Engineering, Khatam University, Tehran, Iran