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مقایسه دقت هوشمندی الگوریتم های مبتنی بر داده کاوی جهت برآورد قیمت ( ارزش) سهام | ||
| راهبرد مدیریت مالی | ||
| مقاله 9، دوره 12، شماره 3 - شماره پیاپی 46، مهر 1403 | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2024.40333.2685 | ||
| نویسندگان | ||
| حسین کیانی زاده1؛ علی باغانی* 2؛ محسن حمیدیان3 | ||
| 1دانشجوی دکتری مدیریت مالی، واحد بین الملل کیش، دانشگاه آزاد اسلامی، جزیره کیش، ایران | ||
| 2استادیار گروه مدیریت مالی، واحد بین الملل داشنگاه آزاد کیش، دانشگاه آزاد اسلامی، جزیره کیش، ایران | ||
| 3دانشیار گروه مدیریت مالی، واحد بین الملل داشنگاه آزاد کیش، دانشگاه آزاد اسلامی، جزیره کیش، ایران | ||
| چکیده | ||
| حجم اطلاعات بازار سرمایه به طرز چشمگیری در حال گسترش میباشد و بدون استفاده از الگوریتمهای دادهکاوی و مدلهای کلان داده، بهرهبرداری از این دادهها امکانپذیر نخواهد بود. مطالعات گذشته بیانگر امکان پیشبینی قیمت سهام توسط مدلهای یادگیری ماشین میباشد؛ اما دقت پیشبینی این مدلها مورد ارزیابی قرار نگرفته است. هدف از این پژوهش مقایسه دقت هوشمندی پنج الگوریتم پرکاربرد دادهکاوی شامل شبکه عصبی، رگرسیون لجستیک، نزدیکترین همسایه k، ماشین بردار پشتیبان و اعتبارسنجی ضربدری میباشد. از بین 385 شرکت فعال در بورس اوراق بهادار تهران، 72 شرکت به روش حذف سیستماتیک انتخاب و دقت مدلهای فوق برای پیشبینی قیمت سهام بر روی دادههای روزانه سهام منتحب برای سالهای 1388 تا 1399 پیادهسازی شده است. متغیر قیمت سهام به عنوان متغیر وابسته و متغیرهای قیمت باز شدن، قیمت بسته شدن، بالاترین قیمت، پایینترین قیمت و حجم معاملات، قیمت روزانه ارز آزاد، قیمت طلا و قیمت نفت به عنوان متغیر مستقل استفاده شده است. برای ارزیابی دقت برآورد قیمت سهام از سه شاخص ، MSE و RMSE استفاده شده و از تحلیل واریانس با استفاده از آماره F برای برازش دقت مدلها و از آماره t برای مقایسه دو به دو مدلها با یکدیگر استفاده شده است. نتایج پژوهش نشان داد از بین الگوریتمهای هوشمند استفاده شده، الگوریتم ماشین بردار پشتیبان بیشترین قدرت برآورد قیمت سهام را به خود اختصاص داده است | ||
| کلیدواژهها | ||
| بورس اوراق بهادار؛ الگوریتم های هوشمند؛ یادگیری ماشین؛ داده کاوی | ||
| عنوان مقاله [English] | ||
| Comparison of intelligence accuracy of data mining algorithms to estimate stocks prices | ||
| نویسندگان [English] | ||
| Hossein Kianizadeh1؛ Ali Baghani2؛ Mohsen Hamidian3 | ||
| 1Department of Financial Management, Kish International Branch, Islamic Azad University, Kish Island. Iran | ||
| 2Department of Financial Management, Kish International Branch, Islamic Azad University, Kish Island. Iran | ||
| 3Department of Financial Management, Kish International Branch, Islamic Azad University, Kish Island. Iran | ||
| چکیده [English] | ||
| Forecasting the stock price due to its attractiveness has always been on the focus of experts and capital market activists. In such a way that various prediction models such as technical and fundamental analysis and data mining are increasingly used to predict stock prices. Past studies indicate the possibility of stock price prediction by machine learning models, but the prediction accuracy of these models has not been evaluated. The purpose of this research is to accurately compare the intelligence of five commonly used data mining algorithms, including neural network, Logestic regression, k-nearest neighbors, support vector machines and cross validation. Among the 385 active companies in Tehran Stock Exchange, 72 companies have been selected by the method of systematic elimination and the above models have been implemented to predict stock prices on the daily prices of selected stocks for the years 2009 to 2020. The stock price is used as a dependent variable and changes in the opening price, closing price, highest price, lowest price and volume of trade, daily price of forign currency, gold and oil price are used as independent variables. Three indicators R2, MSE and RMSE are used, to evaluate the accuracy of models, and analysis of variance using F statistics is used to fit the accuracy of the models, and t-student statistic is used to compare two models. The results are showed that among the smart algorithms used, the support vector machine algorithm has the highest accuracy. Matlab software is used to analyze the data and compare the models. | ||
| کلیدواژهها [English] | ||
| stock exchange, intelligent algorithms, machine learning, data mining | ||
| مراجع | ||
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