Importance of Financial Econometric Models in Analyzing the Performance of Equity Investments in the Capital Market : A Study
DOI:
https://doi.org/10.17010/ijrcm/2024/v11i2/174179Keywords:
financial econometrics
, risk, return, volatility, stock, and investments.Paper Submission Date
, January 15, 2024, Paper sent back for Revision, March 25, Paper Acceptance Date, April 20, 2024Abstract
The study underlined the various financial econometric techniques to evaluate the performance of financial investments made in capital markets. The present research work studied the market trends in capital markets and analyzed the factors influencing the market movements. The study also evaluated the risk characteristics of investors and the investments made in the capital market. The study aimed to estimate the returns of investments based on the risk characteristics by use of financial econometrics after evaluating the risk characteristics. The study project also looked at investment volatility and identified the elements that affected it, as well as the influence of volatility on investments and the projected effect of volatility on capital market investments. The current research work performed financial modeling using the different applications and techniques of financial econometrics, studied the volatility and risk characteristics of investments, and developed solutions for minimizing the risk of investors. The study assessed the performance of stocks depending on the company and industry and examined the performance of several chosen firm stocks. It recommended that investors use financial econometric modeling to optimize their investment returns.Downloads
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Andersson, O., & Haglund, E. (2015). Financial econometrics: A comparison of GARCH type model performances when forecasting VaR (Thesis, Department of Statistics). Digital Scientific Archive. https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243245
Ashwani, & Sheera, V. P. (2018). Indian stock market volatility and economic fundamentals: MIDAS approach. Indian Journal of Finance, 12(8), 7–21. https://doi.org/10.17010/ijf/2018/v12i8/130741
Babu, A. S., & Reddy, S. K. (2015). Exchange rate forecasting using ARIMA, neural network and fuzzy neuron. Journal of Stock & Forex Trading, 4(3), 1000155. https://doi.org/10.4172/2168-9458.1000155
Balasubramanian, K., Veeramanoharan, G., Kumar, J. H., & Paramasivan, B. (2021). Stock market prediction using machine learning. International Journal of Scientific Research in Science and Technology (IJSRST), 9(1), 916–922.
Bouzinanis, G., & Hughston, L. P. (2020). Optimal hedging in incomplete markets. Applied Mathematical Finance, 27(4), 265–287. https://doi.org/10.1080/1350486X.2020.1819831
De, S., & Chakraborty, T. (2015). Foreign portfolio investment and stock market volatility in India. Indian Journal of Finance, 9(1), 49–59. https://doi.org/10.17010/ijf/2015/v9i1/71535
Deshpande, R. (2017). Semi-strong form of market efficiency: Does all critical information affect stock price valuations? Indian Journal of Research in Capital Markets, 4(2), 15–24. https://doi.org/10.17010/ijrcm/2017/v4/i2/116085
Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273–285. https://doi.org/10.1016/j.eswa.2019.03.029
Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537. https://doi.org/10.1016/j.eswa.2021.115537
Kim, R., So, C. H., Jeong, M., Lee, S., Kim, J., & Kang, J. (2019). HATS: A hierarchical graph attention network for stock movement prediction. arXiv preprint, arXiv:1908.07999. https://doi.org/10.48550/arXiv.1908.07999
Kumar, A., & Khanna, S. (2018). GARCH - BEKK approach to volatility behavior and spillover : Evidence from India, China, Hong Kong, and Japan. Indian Journal of Finance, 12(4), 7–19. https://doi.org/10.17010/ijf/2018/v12i4/122791
Kumar, S., & Singh, G. (2023). The day-of-the-week effect in the Indian stock market revisited. Journal of Commerce & Accounting Research, 12(3), p. 12.
Lakshmanan, M. P. (2019). Arima model in predicting NSE Nifty50 index. Proceedings of five day workshop on financial econometrics from 15th to 19th October (pp. 53–61). Directorate of Collegiate Education, Government of Kerala.
Nelson, D. M., Pereira, A. C., & Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN), 1419–1426. IEEE. https://doi.org/10.1109/IJCNN.2017.7966019
Patil, R. (2024). Pricing of equity investment - An empirical analysis of Indian capital market. International Journal of Advanced Research (IJAR), 12(03), 683–689. https://doi.org/10.21474/ijar01/18438
Qin, Q., Wang, Q.-G., Ge, S. S., & Ramakrishnan, G. (2011). Chinese stock price and volatility predictions with multiple technical indicators. Journal of Intelligent Learning Systems and Applications, 3(4), 209–219. https://doi.org/10.4236/jilsa.2011.34024
Reddy, C. V. (2019). Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE. Indian Journal of Finance, 13(8), 7–25. https://doi.org/10.17010/ijf/2019/v13i8/146301
Shah, J., Vaidya, D., & Shah, M. (2022). A comprehensive review on multiple hybrid deep learning approaches for stock prediction. Intelligent Systems with Applications, 16, 200111. https://doi.org/10.1016/j.iswa.2022.200111
Tripathi, V., & Seth, R. (2014). Stock market performance and macroeconomic factors: The study of Indian equity market. Global Business Review, 15(2), 291–316. https://doi.org/10.1177/0972150914523599
Zhou, Z., Zhang, L., Zha, R., Hao, Q., Xu, T., Wu, D., & Chen, E. (2022). Multi-relational graph convolution network for stock movement prediction. In 2022 International Joint Conference on Neural Networks (IJCNN), (pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892482
