Evaluation of Stochastic and ANN Model for Karachi Stock Exchange Prices Prediction

References

1. Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and Artificial Neural networks models for stock price prediction. Journal of Applied Mathematics.
2. Almasarweh, M., and S.AL Wadi. (2018). ARIMA Model in predicting Banking Stock Market Data. Modern Applied Science 12(11), 309-312.
3. Anwar, S., and Ismal, R. (2011). Robustness analysis of artificial neural network and support vector machine in making a prediction. In(2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications. 256-261.
4. Anwar, S., & Mikami, Y. (2011). Comparing accuracy performance of ANN.MLR,and GARCH model in predicting time deposit return of islamic bank. International Journal of Trade and Finance 2(1), 44-51.
5. Box, G, and G. Jenkins. (1970). Time series analysis, forecasting and control. San Francisco,CA, Holden-Day.
6. Chiang , Y. M., F. J. Chang, B.J. D. Jou, and P. F. Lin. (2007). Dynamic ANN for precipitation estimation and forecasting from radar observations. Journal of Hydrology 334(1-2), 250-261.
7. Coskun, H., A. Diyar, and K. Fevzi. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications 36(2), 3839-3844.
8. Dase, K. R., and D. D. Pawar. (2010). Application of Artificial Neural Network for stock market predictions, A review of literature. International Journal of Machine Intelligence 2(2), 14-17.
9. Franses, P. H., and H. Ghijsels. (1999). Additive outliers GARCH and forecasting volatility. International Jornal of Forecasting 15(1), 1-9.
10. Hiransha, M., E. A Gopalakrishnan, K. M. Vijay, and K. P Soman. (2018). NSE stock market prediction using deep-learning models. International Conference on Computational Intelligence and Data Science. Procedia Computer Science. 1351-1362.
11. Hornik, K., M. Stinchicombe, and H. White. (1990). Using multilayer feedforward networks for universal approximation. Neural Networks( Neural Networks, 3, 551-560.) 3, 551-560.
12. Jayasuriya, D Dulani. (2017). Is artifical intelligence of the oracle of Delphi for stock markets ? Artificial intelligence and stock market predictability. 1-37.
13. Jenkins, G.E. P., and G. Box. (1970). GM Time Series Analysis. Forecasting and Control . San Francisco, California, Holden-Day.
14. Khashei, M., and M. Bijari. (2010). An artificial neural network(p,d,q) model for timeseries forecasting. Expert systems with applications , 37, 479-489.
15. Korir, B. (2018). Evaluation modelling and forecasting volatility of daily and weekly returns on Nairobi securities exchange using garch model. IOSR Journal of Economics and Finance(IOSR-JEF) 9(6), 10-21.
16. Lee, K., Yoo, S., and Jin. J. J. (2007). Neural network model vs SARIMA model in forecasting Korean stock price index(KOSPI). Issues in Information Systems 8(2), 372-378. DOI: 10.48009/2_iis_2007_372-378.
17. Lippman, R. (1987). An introduction to computing with neural nets. IEEE Assp magazine 4(2), 4-22.
18. Makridakis, S. (1993). Accuracy measures, theoretical and practical concerns. International Journal of Forecasting 4(9), 527-529.
19. Masters, T. (1993). Practical neural network recipes in C++. Newyork, Academic press.
20. Merh, N., Saxena, V. P., & Pardasani, K. R. (2010). A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Business Intelligence Journal, 3(2), 23-43.
21. Meyler, A., Kenny, G., and Quinn, T. (1998). Forecasting Irish inflation using ARIMA models. 1-48.
22. Naeini, M. P., Taremian, H., & Hashemi, H. B. (2010, October). Stock market value prediction using neural networks. In 2010 international conference on computer information systems and industrial management applications (CISIM) (pp. 132-136). IEEE.
23. Nochai, R., and Nochai, T.(2006). ARIMA model for forecasting oil palm price. 2nd IMT-GT Regional Conference on Mathematics, Statistics and applications. 13-15.
24. Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.
25. Pandey, V. S., and Bajpai, A. (2019). Predictive efficiency of ARIMA and ANN models, A case analysis of Nifty Fifty in Indian stock market. International Journal of Applied Engineering Research, 14(2).
26. Pieleanu, F. D. (2016). Predicting the evolution of bet index ,using an ARIMA model. Jornal of Information systems and operations management 10(1), 151-162.
27. Tabachnick, B. G., and Fidell, L. S. (2001). SAS for windows workbook for Tabachnick and Fidell using multivariate statistics. Allyn and Bacon.
28. Tabachnick, B. G., Fidell, L.S., and Ullman, J. B. (2007). Using multivariate statistics. Vol.5, Boston MA, Pearson.
29. Trippi, R. R., and Turban, E. (1992). Neural networks in finance and investing, Using artificial intelligence to improve real world performance. New York, McGraw-Hill
30. Tsay, R. S. (2010. Analysis of financial time series. John wiley & sons.
31. Wang, C. (2011). A comparison study of between fuzzy time series model and ARIMA model for forecasting Taiwan Export. Export system with Applications, 38(8), 9296-9304.
32. Wang, J.-J., et al. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758-766.
33. White, H. (1989). Learning in artificial neural networks, A statistical perspective. Neural computation 1(4), 425-464.
34. Wijaya, Y. B., Kom, S., and Napitupulu, T. A. (2010). Stock price prediction, comparison of Arima and artificial neural network methods-An Indonesia Stock's Case. 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies. IEEE. 176-179.
35. Yao, J., et al. (1999). Neural networks for technical analysis, a study on KLCI. International journal of theoretical and applied finance, 2(2), 221-241.
36. Zhang, D., Jiang, Q., and Li, X. (2005). Application of neural networks in financial data mining. World Academy of Science, Engineering and Technology 1, 392-395.
37. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159-175.
38. Zhang, G., Patuwo, B. E. and Hu, M. Y. (1998). Forecasting with artificial neural networks,, The state of the art. International journal of forecasting 14(1), 35-62.