Forecasting Of Tehran Stock Exchange Index by Using Data Mining Approach Based on Artificial Intelligence Algorithms
Subject Areas : policy makingMohammad Mahmoodi 1 , Akbar Ghasemi 2
1 - Assistant professor, Faculty of management, Accounting department, Islamic Azad University, Firoozkouh branch, Iran
2 - Msc., Faculty of management , Islamic Azad University, Firoozkouh branch, Iran
Keywords: Data mining, Decision tree, artificial intelligence algorithm, Stock Price Changes,
Abstract :
Uncertainty in the capital market means the difference between the expected values and the amounts that actually occur. Designing different analytical and forecasting methods in the capital market is also less likely due to the high amount of this and the need to know future prices with greater certainty or uncertainty. In order to capitalize on the capital market, investors have always sought to find the right share for investment and the right price to buy and sell, and so all the predicted models always seek to answer the three basic questions, i.e., which share, to what extent When and at what price to buy or sell. Before answering the answers given to these questions, you have to answer a more serious question. Including whether forecasting financial markets is possible. Accordingly, in this research, using data mining, we proposed a method for predicting changes in the total stock index of Tehran stock exchanges. The purpose of this research is in the field of applied research. In terms of its implementation, the research is based on a causal research that is carried out using a data collection database. Based on the results obtained from this study on the best decision tree algorithm with respect to the accuracy of 94% of the C & R Tree algorithm, it can be said that this algorithm can be better than predicting stock price changes. Also, using decision tree can also predict changes in the price of the payment.
9. Resources
1) Adel, Azar, Afsar, Amir (2006), Comparison of Classical and Artificial Intelligence Methods in Stock Expected Stock Indicators and Designing a Combined Model, Quarterly Journal of Humanities, No. 4.
2) Alhayari, Ibrahim (2008) Investigating the weak form of capital market efficiency in Tehran Stock Exchange. Stock Exchange Quarterly. Number 4
3) Alizadeh S., Timurpour B, Ghazanfari M.,(2008) , Data mining and knowledge discovery, Iran University of Science and Technology.
5) Bisoi, R., Dash, P. K., (2014). A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter. Journal of Applied Soft Computing, 3(19), pp. 41-56
6) Charkha, P. R., (2008) . Stock Price Prediction and Trend Prediction Using Neural Networks. Journal of First International Conference on Emerging Trends in Engineering and Technology, 3(7), pp. 592-594.
7) Fedaye Nejad, Mohammad Esmaeil (1995), Reviewing the Effectiveness of Tehran Stock Exchange, PhD dissertation, Tehran University, Faculty of Management.
8) Kara, Y., Boyacioglu, M. A., Baykan, O. K., (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Journal of Expert Systems with Applications, 38(5), pp. 5311-5319.
9) Lien Chen, Zhilin Qiao , Minggang wang, chao wang, ruijin du, and harry eugen stanely (2018), Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market, special section on big data learning and discovery, Digital Object Identifier 10.1109/access.2018.2859809.
11) Liao, SH. L., Chu, P. H., You, Y. L.,(2011). Mining the co-movement between foreign exchange rates and category stock indexes in the Taiwan financial capital market. Expert Systems with Applications, 38(4), pp. 324-331.
12) Malav Shastri1, Sudipta Roy2 and Mamta Mittal(2019), Stock Price Prediction using Artificial Neural Model: An Application of Big Data, EAI Endorsed Transactions on Scalable Information Systems.
13) Meshkani, A. and A.Nazemi. 2009. Introduction to Data Mining, Ferdowsi University of Mashhad Press, Mashhad.
14) Moshiri, Saeed, Moravot, Habib (2006), Estimation of total stock returns of Tehran stock exchanges using linear and nonlinear models, Journal of Commercial Law Research, No. 41.
15) Samidha ,Sharma and Abhishek Gupta,(2014) A Survey on Stock Market Prediction Using Various Algorithms Abhishek Gupta et al, Int.J.Computer Technology & Applications,Vol 5 (2),530-533.
16) Seyed Saman Emami (2018), Predicting Trend of Stock Prices by Developing Data Mining Techniques with the Aim of Gaining Profit, Journal of Accounting & Marketing.
17) Sinaei, Hassanali, Mortazavi, Saeedeh ... and Teimouri Asl, Yaser (2005), Tehran Stock Exchange Stock Exchange (TSE) forecasting using Artificial Neural Networks, Accounting and Auditing, Vol. 12, No. 41, pp. 83-59.
19) Surbhi Sharma and Baijnath Kaushik (2018), Quantitative Analysis of Stock Market Prediction for Accurate Investment Decisions in Future, Journal of Artificial Intelligence.
20) Vivek Rajput1 , Sarika Bobde (2016), Stock market forecasting techniques: literature survey, International Journal of Computer Science and Mobile Computing.
21) Xiao Zhong1 and David Enke(2019), Predicting the daily return direction of the stock market using hybrid machine learning algorithms, Zhong and Enke Financial Innovation.
22) Zhi rang, Zhang, Z.Y., et al. (2005). Stock time series forecasting using support vector machines employeing analyst recommendations, Springer-Verlag Berlin Heidelberg.