Forecasting of P/E Ratio for the Indian Equity Market Stock Index NIFTY 50 Using Neural Networks
R Gautham Goud1, M. Krishna Reddy2
1R Gautham Goud, Research Scholar, Department of Statistics, University College of Science, Osmania University.
2Prof. M. Krishna Reddy, (Retd.) Professor, Department of Statistics, University College of Science, Osmania University.
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. | PP: 1-9 | Volume-10 Issue-5, January 2024 | Retrieval Number: 100.1/ijmh.F1576029623 | DOI: 10.35940/ijmh.F1576.10050124
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 Box-Jenkin’s method is carried out. It is found that MLP and NNAR neural networks models performed better than that of ARIMA model.
Keywords: Forecasting, Stock Market, P/E Ratio, Neural Networks, Box-Jenkins Methodology
Scope of the Article: Business and Marketing