Comparison of the Ability of Modern and Conventional Metaheuristic and Regression Models to Predict Stock Returns by Accounting Variables and Presenting an Effective Model
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 185
فایل این مقاله در 20 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_AMFA-7-2_012
تاریخ نمایه سازی: 21 اردیبهشت 1401
چکیده مقاله:
Investment in the stock market requires decision-making and access to infor-mation on the future of the stock market. Given the importance of predicting stock returns, the present study aimed to discover the variables and indices that could predict stock returns. The prediction of stock returns has long been a 'hot topic' in advanced countries. While effective steps have been taken in this regard, the accu-rate prediction of stock returns remains a problem due to numerous issues. In this study, an accurate, applicable, and effective model was proposed for the predic-tion of stock returns. The statistical sample included ۱۳۸ active companies of Tehran Stock Exchange (TSE) during ۲۰۰۸-۲۰۱۷, which were selected by the systematic removal method. In total, ۱,۳۸۰ data years were selected for the re-search to evaluate the questions. Data analysis was performed using an adaptive neuro-fuzzy inference system (ANFIS), multi-gene genetic programming, and regression analysis. In addition, statistical tests were applied to evaluate the accu-racy of the model, implemented by MATLAB and GeneXproTools. According to the results, the hybrid metaheuristic method had a lower error rate compared to artificial neural network and regression analysis in terms of stock return predic-tion. Therefore, the proposed model could provide more accurate data within a shorter time to predict the stock market status since it makes predictions after selecting the most optimal input variables through ANFIS.
کلیدواژه ها:
نویسندگان
Mahmood Kohansal Kafshgari
Department of Accounting, Isfahan Branch (Khorasgan), Islamic Azad University, Isfahan, Iran
Alireza Zarei Sodani
Department of Accounting, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
Reza Behmanesh
Department of Industrial engineering, Naghshejahan Higher Education Institute, Isfahan, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :