Main Article Content
datamining, genetic algorithm, arima, ANN, linear regression, stock market
Background: For a long time, there has been a trend of trading of shares. Brokerage firms and dealers buy/sell stocks for clients and companies. Their work is based on knowing how the share price of the company will react in the market. Market/ share price predictions are useful as the investor/broker can attempt to predict the output in order to maximize his dividends or minimize his losses.
Methodology: R and Python tools are used to sort, segregate and process the data, and techniques/algorithms such as Genetic Algorithm, ARIMA, Artificial Neural Networks, and Linear Regression are used to forecast results of data. Along with the model data, external factors affecting share prices also be taken into account.
Findings: For each of the applied algorithms, their results are compared and the difference in output with the real-time values has been observed and recorded.
Implications: Using data mining techniques, an attempt is made to estimate a prediction model to help forecast share prices.
2. Hamed, I.M., Hussein, A.S. & Tolba, M.F., 2012. An Intelligent Model for Stock Market Prediction. International Journal of Computational Intelligence Systems, 5(4), pp.639–652. https://doi.org/10.1080/18756891.2012.718108
3. Kamble, R.A., 2018. Short and long term stock trend prediction using decision tree. In Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017. pp. 1371–1375. https://doi.org/10.1109/ICCONS.2017.8250694
4. Khatri, S.K. & Srivastava, A., 2016. Using sentimental analysis in prediction of stock market investment. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2016: Trends and Future Directions. pp. 566–569. https://doi.org/10.1109/ICRITO.2016.7785019
5. Lin, L. et al., 2004. The applications of genetic algorithms in stock market data mining optimisation. In Management Information Systems. pp. 273–280.
6. Maini, S.S. & Govinda, K., 2018. Stock market prediction using data mining techniques. In Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017. pp. 654–661. https://doi.org/10.1109/ISS1.2017.8389253
7. Momoh, O., 2018. Stock Analysis. Investopedia. Available at: https://www.investopedia.com/terms/s/stock-analysis.asp [Accessed November 2, 2018].
8. Nivetha, R.Y. & Dhaya, C., 2017. Developing a Prediction Model for Stock Analysis. In Proceedings - 2017 International Conference on Technical Advancements in Computers and Communication, ICTACC 2017. pp. 1–3. https://doi.org/10.1109/ICTACC.2017.11
9. Ponnam, L.T. et al., 2017. A comparative study on techniques used for prediction of stock market. In International Conference on Automatic Control and Dynamic Optimization Techniques, ICACDOT 2016. pp. 1–6. https://doi.org/10.1109/ICACDOT.2016.7877541
10. Rajput, V. & Bobde, S., 2017. Stock market prediction using hybrid approach. In Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2016. pp. 82–86. https://doi.org/10.1109/CCAA.2016.7813694
11. Srinivasan, N. & Lakshmi, C., 2018. Forecasting stock price using soft computing techniques. In ICONSTEM 2017 - Proceedings: 3rd IEEE International Conference on Science Technology, Engineering and Management. pp. 158–161. https://doi.org/10.1109/ICONSTEM.2017.8261274
12. Tiwari, S., Bharadwaj, A. & Gupta, S., 2018. Stock price prediction using data analytics. In International Conference on Advances in Computing, Communication and Control 2017, ICAC3 2017. pp. 1–5. https://doi.org/10.1109/ICAC3.2017.8318783
13. Wang, C. Te & Lin, Y.Y., 2016. The prediction system for data analysis of stock market by using Genetic Algorithm. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015. pp. 1721–1725.
14. Wang, F. et al., 2016. Enhancing Stock Price Prediction with a Hybrid Approach Based Extreme Learning Machine. In Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. pp. 1568–1575. https://doi.org/10.1109/ICDMW.2015.74
15. Xing, T. et al., 2013. The analysis and prediction of stock price. In Proceedings - 2013 IEEE International Conference on Granular Computing, GrC 2013. pp. 368–373. https://doi.org/10.1109/GrC.2013.6740438