علی لعل بار

This research investigated the development of a stock trading strategy for companies on the New York Stock Exchange (NYSE), a prominent global market. Data was acquired from established libraries and the Yahoo Finance database. The model employed technical analysis indicators and oscillators as input features. Machine learning classification algorithms were used to design trading strategies, and the optimal model was identified based on statistical performance metrics. Accuracy, recall, and F-measure were utilized to evaluate the classification algorithms. Additionally, advanced statistical methods and various software tools were implemented, including Python, Spyder, SPSS, and Excel. The Kruskal-Wallis test was employed to assess the statistical differences between the designed strategies. A sample of 41 actively traded NYSE companies across diverse sectors such as financial services, healthcare, technology, communication services, consumer cyclicals, consumer staples, and energy were chosen using a filter-based approach on June 28th, 2021. The selection criteria included a market capitalization exceeding $200 billion and an average daily trading volume surpassing 1 million shares. Evaluation metrics revealed that the designed random forest trading strategy achieved a good fit with the data and exhibited statistically significant differences from other strategies based on classification learning algorithm.

Stock market activists are trying to find and apply methods so that they can increase the profit of their capital by predicting the future stock price. Therefore, it seems necessary that appropriate, correct and scientific-base principles methods are used to determine the future price of stocks for investors. Economists use econometric methods for forecasting in most cases. Therefore, the aim of this research was to present a new approach in predicting the types of stock movement trends in water-intensive industries using the decision tree approach. The local domain of this research was the companies listed on the Tehran Stock Exchange active in water-intensive industries and the time do-main was during 2016-2020. In the data section, the research was done by collecting the data of the sample companies by referring to the financial statements, explanatory notes and the stock exchange monthly journal; Based on the systematic elimination method, 72 companies active in water-intensive industries were selected as a statistical sample. In order to describe and summarize the collected data, descriptive and inferential statistics have been used using the decision tree approach; the results showed that by using the decision tree approach, it is possible to predict the profit, industry and stock price trends in water-intensive industries. The results obtained in this research are consistent with the documents mentioned in the theoretical framework of research and financial literature.