Do the FAMA and FRENCH Five-Factor model forecast well using ANN?
Abstract
Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.
Keyword : artificial neural networks, forecasting, Fama and French 5 Factors CAPM, asset pricing models, stock markets
How to Cite
Jan, M. N., & Ayub, U. (2019). Do the FAMA and FRENCH Five-Factor model forecast well using ANN?. Journal of Business Economics and Management, 20(1), 168-191. https://doi.org/10.3846/jbem.2019.8250
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ahmad, H., & Javid, A. (2009). Dynamics and determinants of dividend policy in Pakistan: Evidence from Karachi stock exchange non-financial listed firms. International Research Journal of Finance and Economics, 25, 148-171.
Armano, G., Marchesi, M., & Murru, A. (2005). A hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences, 170(1), 3-33. https://doi.org/10.1016/j.ins.2003.03.023
Ayub, U., Shah, S. Z. A., & Abbas, Q. (2015). Robust analysis for downside risk in portfolio management for a volatile stock market. Economic Modelling, 44, 86-96. https://doi.org/10.1016/j.econmod.2014.10.001
Bekaert, G., Harvey, C. R., & Lundblad, C. (2005). Does financial liberalization spur growth? Journal of Financial Economics, 77(1), 3-55. https://doi.org/10.1016/j.jfineco.2004.05.007
Black, F., Jensen, M. C., Scholes, M., & Jensen, M. C. (1972). Studies in the Theory of Capital Markets. The Capital Asset Pricing Model: Some Empirical Tests.
Blume, M. E., & Friend, I. (1973). A new look at the capital asset pricing model. The Journal of Finance, 28(1), 19-34. https://doi.org/10.1111/j.1540-6261.1973.tb01342.x
Bonfiglioli, A. (2008). Financial integration, productivity and capital accumulation. Journal of International Economics, 76(2), 337-355. https://doi.org/10.1016/j.jinteco.2008.08.001
Burney, S. M. A., Jilani, T. A., & Ardil, C. (2005). Levenberg-Marquardt algorithm for Karachi Stock Exchange share rates forecasting. International Journal of Computational Intelligence, 1(3), 144-149.
Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512. https://doi.org/10.1016/j.cor.2004.03.015
Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: predicting stock price movement in China. Annals of Operations Research, 185(1), 25-44. https://doi.org/10.1007/s10479-009-0618-0
Carvalhal, A., & Ribeiro, T. (2008). Do artificial neural networks provide better forecasts? Evidence from Latin American stock indexes. Latin American Business Review, 8(3), 92-110. https://doi.org/10.1080/10978520802035463
Catik, A. N., & Karaçuka, M. (2012). A comparative analysis of alternative univariate time series models in forecasting Turkish inflation. Journal of Business Economics and Management, 13(2), 275-293. https://doi.org/10.3846/16111699.2011.620135
Chan, K. S., & Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 7(3), 179-190. https://doi.org/10.1111/j.1467-9892.1986.tb00501.x
Chiah, M., Chai, D., Zhong, A., & Li, S. (2016). A better model? An empirical investigation of the Fama–French five-factor model in Australia. International Review of Finance, 16(4), 595-638. https://doi.org/10.1111/irfi.12099
Danial, S. N., Noor, S. R., Usmani, B. A., & Zaidi, S. J. H. (2008). A dynamical system and neural network perspective of Karachi stock exchange. In International Multi Topic Conference (pp. 88-99). Springer. https://doi.org/10.1007/978-3-540-89853-5_11
Dixit, G., & Roy, D. (2013). Predicting India Volatility Index: An Application of Artificial Neural Network. International Journal of Computer Applications, 70(4), 22-30. https://doi.org/10.5120/11950-7768
Donaldson, R. G., & Kamstra, M. (1997). An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance, 4(1), 17-46. https://doi.org/10.1016/S0927-5398(96)00011-4
Encke, D. (2008). Neural network-based stock market return forecasting using data mining for variable reduction. In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 2476-2493). IGI Global. https://doi.org/10.4018/978-1-59904-951-9.ch151
Fadlalla, A., & Amani, F. (2014). Predicting next trading day closing price of Qatar exchange index using technical indicators and artificial neural networks. Intelligent Systems in Accounting, Finance and Management, 21(4), 209-223. https://doi.org/10.1002/isaf.1358
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. https://doi.org/10.1016/j.jfineco.2014.10.010
Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636. https://doi.org/10.1086/260061
Fatima, S., & Hussain, G. (2008). Statistical models of KSE100 index using hybrid financial systems. Neurocomputing, 71(13-15), 2742-2746. https://doi.org/10.1016/j.neucom.2007.11.044
Fescioglu-Unver, N., & Tanyeri, B. (2013). A comparison of artificial neural network and multinomial logit models in predicting mergers. Journal of Applied Statistics, 40(4), 712-720. https://doi.org/10.1080/02664763.2012.750717
Franses, P. H., & Van Dijk, D. (2000). Non-linear time series models in empirical finance. Cambridge University Press. https://doi.org/10.1017/CBO9780511754067
Ghufran, B., Awan, H. M., Khakwani, A. K., & Qureshi, M. A. (2016). What causes stock market volatility in Pakistan? Evidence from the field. Economics Research International, 2016. https://doi.org/10.1155/2016/3698297
Gibbons, M. R., Ross, S. A., & Shanken, J. (1989). A test of the efficiency of a given portfolio. Econometrica: Journal of the Econometric Society, 1121-1152. https://doi.org/10.2307/1913625
Gokgoz, F., & Sezgin-Alp, O. (2014). Estimating the Turkish sectoral market returns via arbitrage pricing model under neural network approach. International Journal of Economics and Finance, 7(1), 154. https://doi.org/10.5539/ijef.v7n1p154
Gonzalez Miranda, F., & Burgess, N. (1997). Modelling market volatilities: the neural network perspective. The European Journal of Finance, 3(2), 137-157. https://doi.org/10.1080/135184797337499
Gray, A., Steinfort, R., & McIntosh, R. (2012). Myths and misconceptions about indexing. Retrieved from https://static.vgcontent.info/crp/intl/auw/docs/literature/Myths-Misconceptions-About-Indexing.pdf?20181009|121330
Guan, H., Dai, Z., Zhao, A., & He, J. (2018). A novel stock forecasting model based on High-order-fuzzy-fluctuation trends and back propagation neural network. PloS One, 13(2). https://doi.org/10.1371/journal.pone.0192366
Guotai, C., Abedin, M. Z., & Moula, F.-E. (2017). Modeling credit approval data with neural networks: an experimental investigation and optimization. Journal of Business Economics and Management, 18(2), 224-240. https://doi.org/10.3846/16111699.2017.1280844
Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. https://doi.org/10.1109/72.329697
Haider, S., & Nishat, M. (2009). On testing efficiency of Karachi Stock Exchange using computational intelligence. In Information and Financial Engineering, 2009. ICIFE 2009. International Conference on (pp. 32-36). IEEE. https://doi.org/10.1109/ICIFE.2009.31
Hossain, A., & Nasser, M. (2011). Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns. Journal of Applied Statistics, 38(3), 533-551. https://doi.org/10.1080/02664760903521435
Huang, C.-J., Yang, D.-X., & Chuang, Y.-T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications, 34(4), 2870-2878. https://doi.org/10.1016/j.eswa.2007.05.035
Iqbal, Z. (2013). Efficient machine learning techniques for stock price prediction. International Journal of Engineering Research and Applications, 3(6), 855-867.
Jabbari, E., & Fathi, Z. (2014). Prediction of stock returns using financial ratios based on historical cost, compared with adjusted prices (accounting for inflation) with neural network approach. Indian Journal of Fundamental and Applied Life Sciences 4(4), 1064-1078.
Jasic, T., & Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285-297. https://doi.org/10.1080/0960310042000201228
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215-236. https://doi.org/10.1016/0925-2312(95)00039-9
Kamruzzaman, J. (2006). Artificial neural networks in finance and manufacturing. IGI Global. https://doi.org/10.4018/978-1-59140-670-9
Kanas, A., & Yannopoulos, A. (2001). Comparing linear and nonlinear forecasts for stock returns. International Review of Economics & Finance, 10(4), 383-398. https://doi.org/10.1016/S1059-0560(01)00092-2
Karaban, S., & Maguire, G. (2012). S&P Indices Versus Active Funds Scorecard (SPIVA Australia Scorecard). S&P Dow Jones Indices. Australia: S&P Dow Jones Indices LLC.
Kim, N., & Mangi, F. (2016). What’s next for Asia’s best-performing stock market. Bloomberg Markets. Retrieved from https://Www. Bloomberg. Com Google Scholar.
Ko, P.-C., & Lin, P.-C. (2008). Resource allocation neural network in portfolio selection. Expert Systems with Applications, 35(1-2), 330-337. https://doi.org/10.1016/j.eswa.2007.07.031
Lendasse, A., de Bodt, E., Wertz, V., & Verleysen, M. (2000). Non-linear financial time series forecasting-Application to the Bel 20 stock market index. European Journal of Economic and Social Systems, 14(1), 81-91. https://doi.org/10.1051/ejess:2000110
Levine, R. (2008). Finance and the Poor. The Manchester School, 76, 1-13. https://doi.org/10.1111/j.1467-9957.2008.01078.x
Liang, H., Yang, C., & Cai, C. (2017). Beauty contest, bounded rationality, and sentiment pricing dynamics. Economic Modelling, 60, 71-80. https://doi.org/10.1016/j.econmod.2016.09.010
Majhi, R., Panda, G., & Sahoo, G. (2009). Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications, 36(3), 6800-6808. https://doi.org/10.1016/j.eswa.2008.08.008
Maknickienė, N., & Maknickas, A. (2013a). Financial market prediction system with Evolino neural network and Delphi method. Journal of Business Economics and Management, 14(2), 403-413. https://doi.org/10.3846/16111699.2012.729532
Maknickienė, N., & Maknickas, A. (2013b). Financial market prediction system with Evolino neural network and Delphi method. Journal of Business Economics and Management, 14(2), 403-413. https://doi.org/10.3846/16111699.2012.729532
Malkiel, B. G. (2011). The efficient-market hypothesis and the financial crisis. In Rethinking finance: perspectives on the crisis (Proceedings of a conference). Russel Sage Foundation.
Masters, T. (1993). Practical Neural Network Recipies in C++. 24Morgan Kaufmann.
McMillan, D. G. (2003). Non-linear predictability of UK stock market returns. Oxford Bulletin of Economics and Statistics, 65(5), 557-573. https://doi.org/10.1111/j.1468-0084.2003.00061.x
McNelis, P. D. (2005). Neural networks in finance: gaining predictive edge in the market. Academic Press.
Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Predicción del índice del mercado bursátil utilizando una red neuronal artificial. Journal of Economics, Finance and Administrative Science, 21(41), 89-93. https://doi.org/10.1016/j.jefas.2016.07.002
Munir, S., Chaudhry, I. S., & Akhtar, M. H. (2013). Financial Liberalization and Economic Growth in Pakistan: Empirical Evidence from Co-integration Analysis. Pakistan Journal of Social Sciences (PJSS), 33(2).
Nelson, M. M., & Illingworth, W. T. (1991). A practical guide to neural nets (July 1, 1991). United States: Prentice Hall PTR; Har/Dskt.
O’Connor, N., & Madden, M. G. (2006). A neural network approach to predicting stock exchange movements using external factors. In Applications and Innovations in Intelligent Systems XIII (pp. 64-77). Springer. https://doi.org/10.1007/1-84628-224-1_6
Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465. https://doi.org/10.1016/S0169-2070(02)00058-4
Oztekin, A., Kizilaslan, R., Freund, S., & Iseri, A. (2016). A data analytic approach to forecasting daily stock returns in an emerging market. European Journal of Operational Research, 253(3), 697-710. https://doi.org/10.1016/j.ejor.2016.02.056
Pyo, S., Lee, J., Cha, M., & Jang, H. (2017). Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets. PloS One, 12(11). https://doi.org/10.1371/journal.pone.0188107
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS One, 11(5). https://doi.org/10.1371/journal.pone.0155133
Quah, T.-S. (2008). DJIA stock selection assisted by neural network. Expert Systems with Applications, 35(1-2), 50-58. https://doi.org/10.1016/j.eswa.2007.06.039
Racicot, F.-E., & Rentz, W. F. (2016). Testing Fama–French’s new five-factor asset pricing model: evidence from robust instruments. Applied Economics Letters, 23(6), 444-448.
Rechenthin, M. D. (2014). Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction (Doctoral dissertation). The University of Iowa.
Rojas, R. (2013). Neural networks: a systematic introduction. Springer Science & Business Media.
Ruxanda, G., & Badea, L. M. (2014). Configuring artificial neural networks for stock market predictions. Technological and Economic Development of Economy, 20(1), 116-132. https://doi.org/10.3846/20294913.2014.889051
Sargent, T. J. (1993). Bounded rationality in macroeconomics: The Arne Ryde memorial lectures. OUP Catalogue.
Sgroi, D., & Zizzo, D. J. (2007). Neural networks and bounded rationality. Physica A: Statistical Mechanics and Its Applications, 375(2), 717-725. https://doi.org/10.1016/j.physa.2006.10.026
Sonsino, D., & Shavit, T. (2014). Return prediction and stock selection from unidentified historical data. Quantitative Finance, 14(4), 641-655. https://doi.org/10.1080/14697688.2012.712210
Stansell, S. R., & Eakins, S. G. (2004). Forecasting the direction of change in sector stock indexes: An application of neural networks. Journal of Asset Management, 5(1), 37-48. https://doi.org/10.1057/palgrave.jam.2240126
State Bank of Pakistan. (2015). Financial Stability Review (SBP Annual Publication No. 2015) (pp. 109-111). Pakistan. Retrieved from http://www.sbp.org.pk/FSR/2015/pdf/Chapter-07.pdf
Staub, S., Karaman, E., Kaya, S., Karapınar, H., & Güven, E. (2015). Artificial neural network and agility. Procedia-Social and Behavioral Sciences, 195, 1477-1485. https://doi.org/10.1016/j.sbspro.2015.06.448
Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: two decades of research. Applied Soft Computing, 38, 788-804. https://doi.org/10.1016/j.asoc.2015.09.040
Tong, H. (1978). On a threshold model. In Pattern Recognition and Signal Processing (pp. 575–586). Sijthoff & Noordhoff, Netherlands. https://doi.org/10.1007/978-94-009-9941-1_24
Tong, H., & Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 245-292. https://doi.org/10.1111/j.2517-6161.1980.tb01126.x
Vortelinos, D. I. (2017). Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH. Research in International Business and Finance, 39, 824-839. https://doi.org/10.1016/j.ribaf.2015.01.004
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems, 17(4), 203-222. https://doi.org/10.1080/07421222.2001.11045659
Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346-14355. https://doi.org/10.1016/j.eswa.2011.04.222
Wang, L., Wang, Z., Zhao, S., & Tan, S. (2015). Stock market trend prediction using dynamical Bayesian factor graph. Expert Systems with Applications, 42(15-16), 6267-6275. https://doi.org/10.1016/j.eswa.2015.01.035
Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 438, 178-187. https://doi.org/10.1016/j.physa.2015.06.033
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7
Armano, G., Marchesi, M., & Murru, A. (2005). A hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences, 170(1), 3-33. https://doi.org/10.1016/j.ins.2003.03.023
Ayub, U., Shah, S. Z. A., & Abbas, Q. (2015). Robust analysis for downside risk in portfolio management for a volatile stock market. Economic Modelling, 44, 86-96. https://doi.org/10.1016/j.econmod.2014.10.001
Bekaert, G., Harvey, C. R., & Lundblad, C. (2005). Does financial liberalization spur growth? Journal of Financial Economics, 77(1), 3-55. https://doi.org/10.1016/j.jfineco.2004.05.007
Black, F., Jensen, M. C., Scholes, M., & Jensen, M. C. (1972). Studies in the Theory of Capital Markets. The Capital Asset Pricing Model: Some Empirical Tests.
Blume, M. E., & Friend, I. (1973). A new look at the capital asset pricing model. The Journal of Finance, 28(1), 19-34. https://doi.org/10.1111/j.1540-6261.1973.tb01342.x
Bonfiglioli, A. (2008). Financial integration, productivity and capital accumulation. Journal of International Economics, 76(2), 337-355. https://doi.org/10.1016/j.jinteco.2008.08.001
Burney, S. M. A., Jilani, T. A., & Ardil, C. (2005). Levenberg-Marquardt algorithm for Karachi Stock Exchange share rates forecasting. International Journal of Computational Intelligence, 1(3), 144-149.
Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512. https://doi.org/10.1016/j.cor.2004.03.015
Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: predicting stock price movement in China. Annals of Operations Research, 185(1), 25-44. https://doi.org/10.1007/s10479-009-0618-0
Carvalhal, A., & Ribeiro, T. (2008). Do artificial neural networks provide better forecasts? Evidence from Latin American stock indexes. Latin American Business Review, 8(3), 92-110. https://doi.org/10.1080/10978520802035463
Catik, A. N., & Karaçuka, M. (2012). A comparative analysis of alternative univariate time series models in forecasting Turkish inflation. Journal of Business Economics and Management, 13(2), 275-293. https://doi.org/10.3846/16111699.2011.620135
Chan, K. S., & Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 7(3), 179-190. https://doi.org/10.1111/j.1467-9892.1986.tb00501.x
Chiah, M., Chai, D., Zhong, A., & Li, S. (2016). A better model? An empirical investigation of the Fama–French five-factor model in Australia. International Review of Finance, 16(4), 595-638. https://doi.org/10.1111/irfi.12099
Danial, S. N., Noor, S. R., Usmani, B. A., & Zaidi, S. J. H. (2008). A dynamical system and neural network perspective of Karachi stock exchange. In International Multi Topic Conference (pp. 88-99). Springer. https://doi.org/10.1007/978-3-540-89853-5_11
Dixit, G., & Roy, D. (2013). Predicting India Volatility Index: An Application of Artificial Neural Network. International Journal of Computer Applications, 70(4), 22-30. https://doi.org/10.5120/11950-7768
Donaldson, R. G., & Kamstra, M. (1997). An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance, 4(1), 17-46. https://doi.org/10.1016/S0927-5398(96)00011-4
Encke, D. (2008). Neural network-based stock market return forecasting using data mining for variable reduction. In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 2476-2493). IGI Global. https://doi.org/10.4018/978-1-59904-951-9.ch151
Fadlalla, A., & Amani, F. (2014). Predicting next trading day closing price of Qatar exchange index using technical indicators and artificial neural networks. Intelligent Systems in Accounting, Finance and Management, 21(4), 209-223. https://doi.org/10.1002/isaf.1358
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. https://doi.org/10.1016/j.jfineco.2014.10.010
Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636. https://doi.org/10.1086/260061
Fatima, S., & Hussain, G. (2008). Statistical models of KSE100 index using hybrid financial systems. Neurocomputing, 71(13-15), 2742-2746. https://doi.org/10.1016/j.neucom.2007.11.044
Fescioglu-Unver, N., & Tanyeri, B. (2013). A comparison of artificial neural network and multinomial logit models in predicting mergers. Journal of Applied Statistics, 40(4), 712-720. https://doi.org/10.1080/02664763.2012.750717
Franses, P. H., & Van Dijk, D. (2000). Non-linear time series models in empirical finance. Cambridge University Press. https://doi.org/10.1017/CBO9780511754067
Ghufran, B., Awan, H. M., Khakwani, A. K., & Qureshi, M. A. (2016). What causes stock market volatility in Pakistan? Evidence from the field. Economics Research International, 2016. https://doi.org/10.1155/2016/3698297
Gibbons, M. R., Ross, S. A., & Shanken, J. (1989). A test of the efficiency of a given portfolio. Econometrica: Journal of the Econometric Society, 1121-1152. https://doi.org/10.2307/1913625
Gokgoz, F., & Sezgin-Alp, O. (2014). Estimating the Turkish sectoral market returns via arbitrage pricing model under neural network approach. International Journal of Economics and Finance, 7(1), 154. https://doi.org/10.5539/ijef.v7n1p154
Gonzalez Miranda, F., & Burgess, N. (1997). Modelling market volatilities: the neural network perspective. The European Journal of Finance, 3(2), 137-157. https://doi.org/10.1080/135184797337499
Gray, A., Steinfort, R., & McIntosh, R. (2012). Myths and misconceptions about indexing. Retrieved from https://static.vgcontent.info/crp/intl/auw/docs/literature/Myths-Misconceptions-About-Indexing.pdf?20181009|121330
Guan, H., Dai, Z., Zhao, A., & He, J. (2018). A novel stock forecasting model based on High-order-fuzzy-fluctuation trends and back propagation neural network. PloS One, 13(2). https://doi.org/10.1371/journal.pone.0192366
Guotai, C., Abedin, M. Z., & Moula, F.-E. (2017). Modeling credit approval data with neural networks: an experimental investigation and optimization. Journal of Business Economics and Management, 18(2), 224-240. https://doi.org/10.3846/16111699.2017.1280844
Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. https://doi.org/10.1109/72.329697
Haider, S., & Nishat, M. (2009). On testing efficiency of Karachi Stock Exchange using computational intelligence. In Information and Financial Engineering, 2009. ICIFE 2009. International Conference on (pp. 32-36). IEEE. https://doi.org/10.1109/ICIFE.2009.31
Hossain, A., & Nasser, M. (2011). Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns. Journal of Applied Statistics, 38(3), 533-551. https://doi.org/10.1080/02664760903521435
Huang, C.-J., Yang, D.-X., & Chuang, Y.-T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications, 34(4), 2870-2878. https://doi.org/10.1016/j.eswa.2007.05.035
Iqbal, Z. (2013). Efficient machine learning techniques for stock price prediction. International Journal of Engineering Research and Applications, 3(6), 855-867.
Jabbari, E., & Fathi, Z. (2014). Prediction of stock returns using financial ratios based on historical cost, compared with adjusted prices (accounting for inflation) with neural network approach. Indian Journal of Fundamental and Applied Life Sciences 4(4), 1064-1078.
Jasic, T., & Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285-297. https://doi.org/10.1080/0960310042000201228
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215-236. https://doi.org/10.1016/0925-2312(95)00039-9
Kamruzzaman, J. (2006). Artificial neural networks in finance and manufacturing. IGI Global. https://doi.org/10.4018/978-1-59140-670-9
Kanas, A., & Yannopoulos, A. (2001). Comparing linear and nonlinear forecasts for stock returns. International Review of Economics & Finance, 10(4), 383-398. https://doi.org/10.1016/S1059-0560(01)00092-2
Karaban, S., & Maguire, G. (2012). S&P Indices Versus Active Funds Scorecard (SPIVA Australia Scorecard). S&P Dow Jones Indices. Australia: S&P Dow Jones Indices LLC.
Kim, N., & Mangi, F. (2016). What’s next for Asia’s best-performing stock market. Bloomberg Markets. Retrieved from https://Www. Bloomberg. Com Google Scholar.
Ko, P.-C., & Lin, P.-C. (2008). Resource allocation neural network in portfolio selection. Expert Systems with Applications, 35(1-2), 330-337. https://doi.org/10.1016/j.eswa.2007.07.031
Lendasse, A., de Bodt, E., Wertz, V., & Verleysen, M. (2000). Non-linear financial time series forecasting-Application to the Bel 20 stock market index. European Journal of Economic and Social Systems, 14(1), 81-91. https://doi.org/10.1051/ejess:2000110
Levine, R. (2008). Finance and the Poor. The Manchester School, 76, 1-13. https://doi.org/10.1111/j.1467-9957.2008.01078.x
Liang, H., Yang, C., & Cai, C. (2017). Beauty contest, bounded rationality, and sentiment pricing dynamics. Economic Modelling, 60, 71-80. https://doi.org/10.1016/j.econmod.2016.09.010
Majhi, R., Panda, G., & Sahoo, G. (2009). Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications, 36(3), 6800-6808. https://doi.org/10.1016/j.eswa.2008.08.008
Maknickienė, N., & Maknickas, A. (2013a). Financial market prediction system with Evolino neural network and Delphi method. Journal of Business Economics and Management, 14(2), 403-413. https://doi.org/10.3846/16111699.2012.729532
Maknickienė, N., & Maknickas, A. (2013b). Financial market prediction system with Evolino neural network and Delphi method. Journal of Business Economics and Management, 14(2), 403-413. https://doi.org/10.3846/16111699.2012.729532
Malkiel, B. G. (2011). The efficient-market hypothesis and the financial crisis. In Rethinking finance: perspectives on the crisis (Proceedings of a conference). Russel Sage Foundation.
Masters, T. (1993). Practical Neural Network Recipies in C++. 24Morgan Kaufmann.
McMillan, D. G. (2003). Non-linear predictability of UK stock market returns. Oxford Bulletin of Economics and Statistics, 65(5), 557-573. https://doi.org/10.1111/j.1468-0084.2003.00061.x
McNelis, P. D. (2005). Neural networks in finance: gaining predictive edge in the market. Academic Press.
Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Predicción del índice del mercado bursátil utilizando una red neuronal artificial. Journal of Economics, Finance and Administrative Science, 21(41), 89-93. https://doi.org/10.1016/j.jefas.2016.07.002
Munir, S., Chaudhry, I. S., & Akhtar, M. H. (2013). Financial Liberalization and Economic Growth in Pakistan: Empirical Evidence from Co-integration Analysis. Pakistan Journal of Social Sciences (PJSS), 33(2).
Nelson, M. M., & Illingworth, W. T. (1991). A practical guide to neural nets (July 1, 1991). United States: Prentice Hall PTR; Har/Dskt.
O’Connor, N., & Madden, M. G. (2006). A neural network approach to predicting stock exchange movements using external factors. In Applications and Innovations in Intelligent Systems XIII (pp. 64-77). Springer. https://doi.org/10.1007/1-84628-224-1_6
Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465. https://doi.org/10.1016/S0169-2070(02)00058-4
Oztekin, A., Kizilaslan, R., Freund, S., & Iseri, A. (2016). A data analytic approach to forecasting daily stock returns in an emerging market. European Journal of Operational Research, 253(3), 697-710. https://doi.org/10.1016/j.ejor.2016.02.056
Pyo, S., Lee, J., Cha, M., & Jang, H. (2017). Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets. PloS One, 12(11). https://doi.org/10.1371/journal.pone.0188107
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS One, 11(5). https://doi.org/10.1371/journal.pone.0155133
Quah, T.-S. (2008). DJIA stock selection assisted by neural network. Expert Systems with Applications, 35(1-2), 50-58. https://doi.org/10.1016/j.eswa.2007.06.039
Racicot, F.-E., & Rentz, W. F. (2016). Testing Fama–French’s new five-factor asset pricing model: evidence from robust instruments. Applied Economics Letters, 23(6), 444-448.
Rechenthin, M. D. (2014). Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction (Doctoral dissertation). The University of Iowa.
Rojas, R. (2013). Neural networks: a systematic introduction. Springer Science & Business Media.
Ruxanda, G., & Badea, L. M. (2014). Configuring artificial neural networks for stock market predictions. Technological and Economic Development of Economy, 20(1), 116-132. https://doi.org/10.3846/20294913.2014.889051
Sargent, T. J. (1993). Bounded rationality in macroeconomics: The Arne Ryde memorial lectures. OUP Catalogue.
Sgroi, D., & Zizzo, D. J. (2007). Neural networks and bounded rationality. Physica A: Statistical Mechanics and Its Applications, 375(2), 717-725. https://doi.org/10.1016/j.physa.2006.10.026
Sonsino, D., & Shavit, T. (2014). Return prediction and stock selection from unidentified historical data. Quantitative Finance, 14(4), 641-655. https://doi.org/10.1080/14697688.2012.712210
Stansell, S. R., & Eakins, S. G. (2004). Forecasting the direction of change in sector stock indexes: An application of neural networks. Journal of Asset Management, 5(1), 37-48. https://doi.org/10.1057/palgrave.jam.2240126
State Bank of Pakistan. (2015). Financial Stability Review (SBP Annual Publication No. 2015) (pp. 109-111). Pakistan. Retrieved from http://www.sbp.org.pk/FSR/2015/pdf/Chapter-07.pdf
Staub, S., Karaman, E., Kaya, S., Karapınar, H., & Güven, E. (2015). Artificial neural network and agility. Procedia-Social and Behavioral Sciences, 195, 1477-1485. https://doi.org/10.1016/j.sbspro.2015.06.448
Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: two decades of research. Applied Soft Computing, 38, 788-804. https://doi.org/10.1016/j.asoc.2015.09.040
Tong, H. (1978). On a threshold model. In Pattern Recognition and Signal Processing (pp. 575–586). Sijthoff & Noordhoff, Netherlands. https://doi.org/10.1007/978-94-009-9941-1_24
Tong, H., & Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 245-292. https://doi.org/10.1111/j.2517-6161.1980.tb01126.x
Vortelinos, D. I. (2017). Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH. Research in International Business and Finance, 39, 824-839. https://doi.org/10.1016/j.ribaf.2015.01.004
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems, 17(4), 203-222. https://doi.org/10.1080/07421222.2001.11045659
Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346-14355. https://doi.org/10.1016/j.eswa.2011.04.222
Wang, L., Wang, Z., Zhao, S., & Tan, S. (2015). Stock market trend prediction using dynamical Bayesian factor graph. Expert Systems with Applications, 42(15-16), 6267-6275. https://doi.org/10.1016/j.eswa.2015.01.035
Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 438, 178-187. https://doi.org/10.1016/j.physa.2015.06.033
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7