List of Articles Ali Laalbar

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  1 - Presenting a Model for Predicting Various Types of Stock Movement Trends in Water-Intensive Industries Using a Decision Tree
  Mohammad  Bahmani Ali Lalbar
  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 t More

  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. Manuscript profile
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  2 - The effect of information disclosure on market reaction with meta-analysis approach
  Shiva Zamani Majid Zanjirdar Ali Lalbar
  10.22034/amfa.2021.1937478.1625
  The present research aims to conduct meta-analysis of the effect of information disclosure on market reaction. In order to integrate the results of different researches and identify the determinants of relations between information disclosure and market reaction, we use More

  The present research aims to conduct meta-analysis of the effect of information disclosure on market reaction. In order to integrate the results of different researches and identify the determinants of relations between information disclosure and market reaction, we used meta-analysis methodology as a quantitative statistical method. To perform the meta-analysis method, the scientific magazines all over the world (the published papers relating the research variables from 1990 to 2020) were identified and gathered as statistical population and. As a result, 86 studies were analyzed using systematic removal. The results of the relating studies published during this period indicate that most of these studies are heterogeneous. By classifying these studies based on different measurement criteria of information disclosure and market reaction and also by calculating the intragroup statistics along with identifying the factor of this heterogeneity, we found that these diverse measurement criteria used in the mentioned studies are considered as contradiction factors in research results. We also found that there is no meaningful relation between Non-financial information component, company cycle, type of industry and company size with market reaction whereas there is a meaningful relation between financial information and market reaction. Manuscript profile
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  3 - Designing a Trading Strategy to Buy and Sell the Stock of Companies Listed on the New York Stock Exchange Based on Classification Learning Algorithms
  Nasser Heydari Majid Zanjirdar Ali Lalbar
  10.22034/amfa.2022.1967149.1796
  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 indi More

  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. Manuscript profile