پیش بینی قیمت سهام در بازار سرمایه با رویکرد هوش مصنوعی
محورهای موضوعی : پژوهشنامه اقتصاد و کسب و کار
عسگر نوربخش
1
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مصطفی شایگانی
2
1 - استادیار، گروه مدیریت مالی، دانشکده مدیریت و حسابداری، دانشکدگان فارابی دانشگاه تهران، قم، ايران
2 - کارشناسی ارشد مدیریت مالی، دانشکده مدیریت و حسابداری، دانشکدگان فارابی دانشگاه تهران، قم، ايران
کلید واژه: شبکه عصبی مصنوعی, حافظه کوتاه مدت ماندگار, شبکه عصبی پیچشی, پیش‌بینی قیمت,
چکیده مقاله :
هدف این پژوهش پیش‌بینی قیمت سهام با استفاده از دو نوع شبکه عصبی در بورس اوراق بهادار تهران است. شبکه‌های عصبی بازگشتی[1] عموماً در پیش‌بینی داده‌های سری زمانی توانایی خوبی دارند، اما شبکه‌ی عصبی پیچشی[2] عمدتا برای کاربردهایی چون بینایی کامپیوتر استفاده می‌شوند. برای انجام این پژوهش از زبان پایتون در ویرایشگر VS code استفاده شده است. جامعه آماری این پژوهش بورس اوراق بهادار تهران می‌باشد. حجم نمونه آماری این پژوهش شامل داده‌های سه نماد بورس اوراق بهادار تهران به شرح ایران خودرو، البرز دارو و توسعه معادن روی ایران است. در این پژوهش از هشت ویژگی قیمت در چارچوب زمانی روزانه از تاریخ 1380 تا تاریخ 1400 استفاده می‌شود که شامل بالاترین قیمت، پایین‌ترین قیمت، قیمت بسته‌شدن، قیمت باز شدن، ارزش معاملات، حجم معاملات، اختلاف قیمت بسته شدن دو روز متوالی، و بازده روزانه است. برای ارزیابی عملکرد مدل‌ها از سه معیار خطای میانگین خطای مطلق، ریشه میانگین مربعات خطا و ضریب تعیین استفاده شده است. نتایج نشان می‌دهد که مدل شبکه عصبی پیچشی توانایی پیش بینی با دقت خوبی را دارا می‌باشد. شبکه‌های عصبی بازگشتی از بهترین نوع شبکه‌ها برای پیش‌بینی قیمت هستند، اما نتایج نشان می‌دهد که شبکه عصبی پیچشی عملکرد بهتری از شبکه عصبی حافظه کوتاه مدت ماندگار داشته است. نتایج نشان می‌دهد که مدل‌های یادگیری عمیق در صورتی که در انتخاب ویژگی‌هایی (متغیرهای مستقل) که بتوانند بیشترین میزان معناداری را در تفسیر علل فراز و فرودهای قیمت در دوره‌های رونق و رکود بازار داشته باشند، قابلیت و توانایی پیش‌بینی قیمت، با دقت قابل قبول را خواهند داشت.
The main objective of this research is to predict stock prices using two types of neural networks in the Tehran Stock Exchange. Python language in the VS Code editor has been used to conduct this research. The statistical population of this research is the Tehran Stock Exchange. The sample size of this research includes data from three symbols of the Tehran Stock Exchange, namely Iran Khodro, Alborz Darou, and Iran Zinc Mines Development. In this research, eight price features are used within the daily timeframe from 2001 to 2021, including the highest price, lowest price, closing price, opening price, transaction value, transaction volume, the difference in closing price between two consecutive days, and daily return. Three metrics of mean absolute error, root mean square error, and coefficient of determination have been used to evaluate the models' performance. The results indicate that the convolutional neural network model has the ability to predict with good accuracy. Recurrent neural networks are among the best types of networks for price prediction, but the results show that the convolutional neural network has performed better than the short-term memory neural network. The results suggest that deep learning models, when selecting features (independent variables) that can express the highest level of significance in interpreting the causes of price fluctuations during market booms and recessions, have the ability and capability to predict prices with acceptable accuracy.
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