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Ebrahimian, KAMEL, Abbasi, Ebrahim, alam tabriz, akbar, mohammadzadeh, amir. (1402). Stock Price Forecasting with Support Vector Regression Based on Social Network Sentiment Analysis and Technicl Analysis. , 8(29), 53-64. doi: 10.30495/ijfma.2023.21124
KAMEL Ebrahimian; Ebrahim Abbasi; akbar alam tabriz; amir mohammadzadeh. "Stock Price Forecasting with Support Vector Regression Based on Social Network Sentiment Analysis and Technicl Analysis". , 8, 29, 1402, 53-64. doi: 10.30495/ijfma.2023.21124
Ebrahimian, KAMEL, Abbasi, Ebrahim, alam tabriz, akbar, mohammadzadeh, amir. (1402). 'Stock Price Forecasting with Support Vector Regression Based on Social Network Sentiment Analysis and Technicl Analysis', , 8(29), pp. 53-64. doi: 10.30495/ijfma.2023.21124
Ebrahimian, KAMEL, Abbasi, Ebrahim, alam tabriz, akbar, mohammadzadeh, amir. Stock Price Forecasting with Support Vector Regression Based on Social Network Sentiment Analysis and Technicl Analysis. , 1402; 8(29): 53-64. doi: 10.30495/ijfma.2023.21124

Stock Price Forecasting with Support Vector Regression Based on Social Network Sentiment Analysis and Technicl Analysis

International Journal of Finance & Managerial Accounting
دوره 8، شماره 29، تیر 2023، صفحه 53-64  XML اصل مقاله (641.43 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30495/ijfma.2023.21124
نویسندگان
KAMEL Ebrahimian1؛ Ebrahim Abbasi email 2؛ akbar alam tabriz3؛ amir mohammadzadeh4
1Phd-studentat Department of Management، Faculty of Management and Accounting، Qazvin Branch، Islamic Azad University، Qazvin ، Iran
2Associate professor at Alzahra University.
3Professor at Department of Industrial Management ، Management and Accounting Faculty ، Shahid Beheshti University ، Tehran ، Iran
4Department of Industrial at Department of Management، Islamic Azad University Qazvin، Qazvin، Iran
چکیده
This study predicts Price of stocks in the short term by using the analysis of investors' opinions of the social network. The predictability of stock markets, due to having a complex, dynamic and nonlinear system that it has always been one of the challenges for researchers. The effect of users' feelings on the social network and its combination with 20 technical indicators on the accuracy of stock price forecasting. The study period is from the beginning of April 2016 to the end of March 2017 (two years). To access sufficient data, a sample of 14 active companies that had the most comments and posts. Data mining of technical indicators was performed and support vector regression was used to predict. The results show that the use of technical indicators is more accurate compared to combining it with the aggregation of users' emotions and has less RMSE errors. The number of comments has a significant correlation and the results of Granger causality test showed that it is possible to use the aggregation of users' daily emotions to predict stock prices.
کلیدواژه‌ها
Technical analysis؛ Classification algorithms؛ Support Vector Regression؛ text mining
مراجع
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