مهندسی صنایع و مدیریت

مهندسی صنایع و مدیریت

طراحی سیستم پشتیبان تصمیم‌گیری مدیریت سبد‌ سهام با بهره‌گیری از روش‌های علم ‌داده (بازار بورس اوراق بهادار تهران)

نوع مقاله : پژوهشی

نویسندگان
نوید جواهری
نجمه نشاط
عباسعلی جعفری ندوشن
گروه مهندسی صنایع، دانشکده‌ی فنی مهندسی، دانشگاه میبد، یزد.
10.24200/j65.2024.63158.2375
چکیده
افزایش سودآوری و کاهش میزان ریسک، مستلزم انتخاب مسیر هوشمند سرمایه‌گذاری ضمن بهره‌گیری از تحلیل داده است؛ لذا امروزه ارائه‌ی روشی در قالب سیستم پشتیبان تصمیم‌گیری مدیریت سبد‌ سهام، ضمن درک بهتر جایگاه علم ‌داده، ضروری است؛ لذا در پژوهش حاضر، علاوه‌بر دستیابی به این مهم، ضمن ترکیب روش‌های علم ‌داده با مدل مارکویتز، مدل‌های طبقه‌بندی و پیش‌بینی نیز ایجاد شده‌اند، که در تشخیص تقلب مالی شرکت‌ها مؤثر هستند. همچنین، به‌منظور خوشه‌بندی شرکت‌های فعال در بورس اوراق بهادار تهران، الگوریتم‌های سلسله‌مراتبی و کامینز استفاده شده‌اند. درخصوص طبقه‌بندی داده‌ها، الگوریتم طبقه‌بندی ماشین ‌بردار پشتیبان ‌خطی با دقت 70٪ در مقایسه با الگوریتم‌های‌ درخت ‌تصمیم، نایوبیز، نزدیک‌ترین همسایه، و پرسپترون چند‌لایه و درخصوص ساخت مدل‌های پیش‌بینی، درخت تصمیم با کمینه‌ی میزان خطا، در مقایسه با الگوریتم‌های نایوبیز، نزدیک‌ترین همسایه، پرسپترون چند‌لایه شناسایی شده‌اند. داده‌های اولیه در پژوهش حاضر، شامل 20 عنوان شاخص مالی و داده‌های روزانه‌ی قیمت سهام شرکت‌ها در فضای برنامه‌نویسی پایتون بوده‌اند. 
کلیدواژه‌ها
مدیریت سبد سهام
مدل‌سازی
علم ‌داده
طبقه‌بندی
خوشه‌بندی
بخش‌بندی
موضوعات

عنوان مقاله English

Designing a Decision Support System for Portfolio Management using Data Science Methods (Tehran Stock Exchange)

نویسندگان English

Navid Javaheri
Najmeh Neshat
Abbasali Jafari Nodoushan
Department of Industrial Engineering, Faculty of Engineering, Meybod University, Meybod, Iran.
چکیده English

Increasing profitability and reducing risk always requires choosing a smart investment path while taking advantage of data analysis; Therefore, it is necessary to provide a technique in the form of a decision support system for stock portfolio management while better understanding the position of data science. In this research, while combining data science methods with the Markowitz model, classification and forecasting models have also been created. Detecting financial fraud of companies is also effective; Hierarchical and Cummins algorithms have also been used in order to cluster active companies in the Tehran Stock Exchange. In terms of data classification, the linear support vector machine classification algorithm has been identified with 70% accuracy in comparison with the decision tree, Nyobies, nearest neighbour, and multilayer perceptron algorithms, and in terms of building prediction models, the decision tree with the minimum amount of error, in comparison with Nyobies algorithms, is the closest Neighbor, multilayer perceptron has been identified. The primary data in the current research includes twenty titles of financial indicators and daily data of stock prices of companies in the Python programming space. It was concluded that choosing stocks from among the clusters formed by different stocks of companies will reduce the risk of diversifying the stock portfolio. The mentioned system will be able to generalize as a comprehensive model for data in different time intervals and the indicators desired by the analyst, thus helping investors in a fast and accurate analytical way. The performance of the final technique will be such that the investor first selects several desired companies according to the heard, analysis, and news; It predicts the price performance of companies; Then, it separates the companies that have succeeded in accepting the condition by using the clustering model and separates the portfolio of various stocks. It forms according to the amount of risk and expected return. Importantly, as mentioned, the presented classification and forecasting models, in addition to being used in the formation and management of the stock portfolio, will be able to be effective in predicting the possible fraud of companies.

کلیدواژه‌ها English

Portfolio management
modeling
data science
classification
clustering
segmentation
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