Forecasting of P/E Ratio for the Indian Equity Market Stock Index NIFTY 50 Using Neural Networks
Article Sidebar
Main Article Content
Abstract
The ratio of present price of an index to its earnings is known as its price to earnings ratio denoted by P/E ratio. A high P/E means that an index’s price is high relative to earnings and overvalued. Its low value means that price is low relative to earnings and undervalued. A potential investor prefers an index with low P/E ratio. Therefore, the movement of the P/E ratio plays a crucial role in understanding the behaviour of the stock market. In this paper the modelling of the P/E ratio for the Indian equity market stock index NIFTY 50 using NNAR, MLP and ELM neural networks models and the traditional ARIMA model with BoxJenkin’s method is carried out. It is found that MLP and NNAR neural networks models performed better than that of ARIMA model.
Downloads
Article Details
How to Cite
References
A. Abhyankar, L. S. Copeland, and W. Wong. Uncovering nonlinear structure in real-time stock-market indexes: the s&p 500, the dax, the Nikkei 225, and the ftse-100. Journal of Business & Economic Statistics, 15(1):1–14, 1997. https://doi.org/10.1080/07350015.1997.10524681
A. A. Adebiyi, A. O. Adewumi, C. K. Ayo, et al. Comparison of arima and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014, 2014. https://doi.org/10.1155/2014/614342
M. U. Ahmad. An analysis of market p/e using auto regression and vector auto regression models. SAMVAD, 10:116–120, 2015.
K. Anderson and C. Brooks. The long-term price-earnings ratio. Journal of Business Finance & Accounting, 33(7-8):1063–1086, 2006. https://doi.org/10.1111/j.1468-5957.2006.00621.x
A. Aslanargun, M. Mammadov, B. Yazici, and S. Yolacan. Comparison of arima, neural networks and hybrid models in time series: tourist arrival forecasting. Journal of Statistical computation and Simulation, 77(1):29–53, 2007. https://doi.org/10.1080/10629360600564874
S. D. Balkin and J. K. Ord. Automatic neural network modeling for univariate time series. International Journal of Forecasting, 16(4):509–515, 2000. https://doi.org/10.1016/S0169-2070(00)00072-8
A. Basistha and A. Kurov. Macroeconomic cycles and the stock market’s reaction to monetary policy. Journal of Banking & Finance, 32(12):2606–2616, 2008. https://doi.org/10.1016/j.jbankfin.2008.05.012
B. S. Bernanke and K. N. Kuttner. What explains the stock market’s reaction to federal reserve policy? The Journal of finance, 60(3):1221–1257, 2005. https://doi.org/10.1111/j.1540-6261.2005.00760.x
L. Bonga-Bonga et al. Equity prices, monetary policy, and economic activities in emerging market economies: The case of south africa. Journal ofApplied Business Research (JABR), 28(6):1217–1228, 2012. https://doi.org/10.19030/jabr.v28i6.7337
H. Bouzgou and C. A. Gueymard. Minimum redundancy–maximum relevance with extreme learning machines for global solar radiation forecasting: Toward an optimized dimensionality reduction for solar time series. Solar Energy, 158:595–609, 2017. https://doi.org/10.1016/j.solener.2017.10.035
E. Box George, M. Jenkins Gwilym, C. Reinsel Gregory, and M. Ljung Greta. Time series analysis: forecasting and control. San Francisco: Holden Bay, 1976.
J. Y. Campbell and R. J. Shiller. Stock prices, earnings, and expected dividends. the Journal of Finance, 43(3):661–676, 1988. https://doi.org/10.1111/j.1540-6261.1988.tb04598.x
J. Faraway and C. Chatfield. Time series forecasting with neural networks: a comparative study using the airline data. Journal of the Royal Statistical Society Series C: Applied Statistics, 47(2):231–250, 1998. https://doi.org/10.1111/1467-9876.00109
J. A. Fill and D. E. Fishkind. The moore–penrose generalized inverse for sums of matrices. SIAM Journal on Matrix Analysis and Applications, 21(2):629–635, 2000. https://doi.org/10.1137/S0895479897329692
M. Gori, A. Tesi, et al. On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(1):76–86, 1992. https://doi.org/10.1109/34.107014
R. G. Goud and M. K. Reddy. Forecasting of p/e ratio for the Indian equity market stock index nifty 50. International Journal of Agricultural & Statistical Sciences, 16(2), 2020. https://doi.org/10.2139/ssrn.606263
C. S. Hansen and B. Tuypens. Proxying for expected returns with price earnings ratios. Available at SSRN 606263, 2004.
E. Hjalmarsson. Predicting global stock returns. Journal of Financial and Quantitative Analysis, 45(1):49–80, 2010. https://doi.org/10.1017/S0022109009990469
G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew. Extreme learning machine: theory and applications. Neurocomputing, 70(1-3):489–501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
K.-j. Kim and I. Han. Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with Applications, 19(2):125–132, 2000. https://doi.org/10.1016/S0957-4174(00)00027-0
S. H. Kim and S. H. Chun. Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index. International Journal of Forecasting, 14(3):323–337, 1998. https://doi.org/10.1016/S0169-2070(98)00003-X
R.-J. Li and Z.-B. Xiong. Forecasting stock market with fuzzy neural networks. In 2005 International conference on machine learning and cybernetics,volume 6, pages 3475–3479. IEEE, 2005.
H. R. Maier and G. C. Dandy. Neural network models for forecasting univariate time series. 1996.
A. Maleki, S. Nasseri, M. S. Aminabad, and M. Hadi. Comparison of arima and nnar models for forecasting water treatment plant’s influent characteristics. KSCE Journal of Civil Engineering, 22:3233–3245, 2018. https://doi.org/10.1007/s12205-018-1195-z
K. N. Pantazopoulos, L. H. Tsoukalas, N. G. Bourbakis, M. J. Brun, and E. N. Houstis. Financial prediction and trading strategies using neurofuzzy approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 28(4):520–531, 1998. https://doi.org/10.1109/3477.704291
A.-A. RE. Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Computers & Industrial Engineering, 54(4):903–917, 2008. https://doi.org/10.1016/j.cie.2007.10.020
A. N. Refenes, M. Azema-Barac, L. Chen, and S. Karoussos. Currency exchange rate prediction and neural network design strategies. Neural Computing & Applications, 1:46–58, 1993. https://doi.org/10.1007/BF01411374
S. Siekmann, R. Kruse, J. Gebhardt, F. Van Overbeek, and R. Cooke. Information fusion in the context of stock index prediction. International journal of intelligent systems, 16(11):1285–1298, 2001. https://doi.org/10.1002/int.1060
Satish*, P., Srinivasulu, S., & Swathi, Dr. R. (2019). A Hybrid Genetic Algorithm Based Rainfall Prediction Model Using Deep Neural Network. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 12, pp. 5370–5373). https://doi.org/10.35940/ijitee.l3777.1081219 https://doi.org/10.35940/ijitee.L3777.1081219
Radhamani, V., & Dalin, G. (2019). Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review. In International Journal of Soft Computing and Engineering (Vol. 9, Issue 3, pp. 1–7). https://doi.org/10.35940/ijsce.c3265.099319
Behera, D. K., Das, M., & Swetanisha, S. (2019). A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 10809–10814). https://doi.org/10.35940/ijrte.d4362.118419
Sharan, V., & Kaur, Dr. A. (2019). Detection of Counterfeit Indian Currency Note Using Image Processing. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 2440–2447). https://doi.org/10.35940/ijeat.a9972.109119
Velani, J., & Patel, Dr. S. (2023). A Review: Fraud Prospects in Cryptocurrency Investment. In International Journal of Innovative Science and Modern Engineering (Vol. 11, Issue 6, pp. 1–4). https://doi.org/10.35940/ijisme.e4167.0611623