Published January 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Forecasting of P/E Ratio for the Indian Equity Market Stock Index NIFTY 50 Using Neural Networks

Creators

  • 1. Research Scholar, Department of Statistics, University College of Science, Osmania University.

Contributors

Contact person:

  • 1. Research Scholar, Department of Statistics, University College of Science, Osmania University
  • 2. (Retd.) Professor, Department of Statistics, University College of Science, Osmania University

Description

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.

Files

F1576029623.pdf

Files (803.9 kB)

Name Size Download all
md5:87436d34bf2abc1d09b9882b04be36db
803.9 kB Preview Download

Additional details

Identifiers

EISSN
2394-0913
DOI
10.35940/ijmh.F1576.10050124

Dates

Accepted
2024-01-15
Manuscript received on 30 January 2023 | Revised Manuscript received on 22 December 2023 | Manuscript Accepted on 15 January 2024 | Manuscript published on 30 January 2024.

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