Predictive modeling of return volatility in sustainable investments: An in-depth analysis of ARIMA, GARCH, and ARCH techniques
-
Author(s)Srihari G.Link to ORCID Index: https://orcid.org/0000-0002-0233-4958
,
Kusuma T.Link to ORCID Index: https://orcid.org/0000-0001-7084-0775
,
Chetanraj D. B.Link to ORCID Index: https://orcid.org/0000-0002-3504-4009
,
J. P. Senthil KumarLink to ORCID Index: https://orcid.org/0000-0001-5104-1185
,
Ravi AluvalaLink to ORCID Index: https://orcid.org/0000-0003-3862-5009
-
DOIhttp://dx.doi.org/10.21511/imfi.21(1).2024.17
-
Article InfoVolume 21 2024, Issue #1, pp. 213-228
- 454 Views
-
107 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This paper aims to forecast the stock price and analyze the return volatility of India’s top three socially responsible companies. This study used ARIMA and GARCH models to forecast the stock price and analyze return volatility. For the analysis, the required time series data are collected from Yahoo Finance from 01-08-2012 to 29-07-2022 of the companies’ Monthly and daily closing stock prices. The socially responsible companies are selected based on India’s sustainability indices. The findings of the study show that the ARIMA (9,1,9) model for HDFC Ltd, ARIMA (10,1,7) for Reliance Industries Ltd, and ARIMA (2,1,2) are suitable models to forecast the stock price. Also, the study’s findings forecasted stock prices from August 2022 to July 2023. The forecasted stock price for July 2023 of HDFC Ltd is INR 2,613.78, Reliance industries Ltd is INR 3,073.75, and ICICI Bank Ltd is INR 857.73. Reliance Industries Ltd (σ2t = 0.9270586) is less volatile, and HDFC Ltd (σ2t = 0.9665041) is more volatile among the three companies, ICICI Bank Ltd (σ2t = 0.9507527) is the second high volatile company. The present study is limited to the top three companies that were selected from the three sustainability indices of BSE. The study is also limited to analysis of past volatility of stock price returns.
- Keywords
-
JEL Classification (Paper profile tab)G11, G17
-
References32
-
Tables10
-
Figures8
-
- Figure 1. Non-stationarity and stationarity of observed data series
- Figure 2. Correlogram of observed data at first difference of three companies
- Figure 3. Past and forecasted stock price of HDFC Ltd
- Figure 4. Past and forecasted stock price of Reliance Industries Ltd
- Figure 5. Past and forecasted stock price of ICICI Bank Ltd
- Figure 6. Stock price returns of three companies
- Figure 7. Correlogram of observed data squared residuals of three companies
- Figure 8. Stock price return volatility of three companies
-
- Table 1. Top three companies falling under all the sustainability indices
- Table 2. ADF test results of observed data
- Table 3. ADF test results of data at first difference
- Table 4. Selected ARIMA (p,d,q) model parameter estimations
- Table 5. HDFC Ltd, Forecast results
- Table 6. Reliance Industries Ltd, Forecast results
- Table 7. ICICI Bank Ltd, Forecast results
- Table 8. Comparing ARCH and GARCH models
- Table 9. GARCH model estimation coefficients of three companies
- Table А1. Summary of literature review
-
- Abdullahi, A. A., Lasisi Kazeem, E., Umar Farouk Abbas, & Hassan, M. (2023). Robust order identification of arima and garch models: stationary and non- stationary process. Fudma Journal of Sciences (FJS), 4(1), 88-100.
- Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014, 106-112.
- Almansour, B. Y., Alshater, M. M., Marashdeh, H., Dhiaf, M., & Atayah, O. F. (2022). The return volatility and shock transmission patterns of chosen S&P and Dow Jones sustainability indices and their conventional counterpart. Competitiveness Review.
- Bhuvaneskumar, A., Sivakumar, V. J., & Pushparaj, N. (2022). Performance assessment and ranking of socially responsible companies in India using FAHP, TOPSIS and Altman Z-score. Benchmarking.
- Challa, M. L., Malepati, V., & Kolusu, S. N. R. (2020). S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA. Financial Innovation, 6(1).
- Costa, F. J. M. (2017). Forecasting volatility using GARCH models. University of Minho Franciso Costa, April, 1-58.
- Deb, S. (2021). Analyzing airlines stock price volatility during COVID-19 pandemic through internet search data. International Journal of Finance and Economics, August 2020, 1-17.
- Huang, Y. (2022). Prediction of the Stock Price of Shanghai Securities Composite Index by Using Time-series Model, 237-242.
- Joshi, S., Pandey, V., & Ross, R. B. (2017). Asymmetry in Stock Market Reactions to Changes in Membership of the Dow Jones Sustainability Index. The Journal of Business Inquiry, 16(1 Spec), 12-35.
- Kathiravan, C., Selvam, M., Maniam, B., Venkateswar, S., & Sigo, M. O. (2021). Does temperature influence the carbon index? Evidences from India. Journal of Public Affairs, 21(1), 1-8.
- Kolte, A., Mal, H., Pawar, A., Bhosale, T., & Roy, J. K. (2020). Volatility analysis of bse bankex companies in indian banking sector using garch model. Finance India, 34(2), 631-640.
- Koreisha, S. G., & Pukkila, T. M. (1993). New approaches for determining the degree of differencing necessary to induce stationarity in ARIMA models. Journal of Statistical Planning and Inference, 36(2-3), 399-412.
- Kumar, J. P. S., Sundar, R., & Ravi, A. (2021). Comparison of Stock Market Prediction Performance of ARIMA and RNN-LSTM Model – A Case Study on Indian Stock Exchange. AIP Conference Proceedings.
- Kusuma, T., & Kumar, J. P. S. (2018). Predicting NAVs of mutual funds using the ARIMA model. AIP Conference Proceedings, 2875, 010001.
- Ma, L., Hu, C., Lin, R., & Han, Y. (2018). ARIMA model forecast based on EViews software. IOP Conference Series: Earth and Environmental Science, 208(1).
- Mehta, K., Sharma, R., & Vyas, V. (2019). Efficiency and ranking of sustainability index of India using DEA-TOPSIS. Journal of Indian Business Research, 11(2), 179-199.
- Mustapa, F. H., & Ismail, M. T. (2019). Modelling and forecasting S&P 500 stock prices using hybrid Arima-Garch Model. Journal of Physics: Conference Series, 1366(1).
- Novita Sari, & Achmad Hizazi., W. (2021). Comparative Performance of Arima and Garch Models in Modelling and Forecasting Volatility of Kuala Lumpur Composite Index. International Journal of Academic Research in Accounting Finance and Management Sciences, 11(2), 202-221.
- 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.
- Seth, N., & Singh, D. (2022). Are Green Indices and Regular Indices Interrelated? Evidence from BSE. SCMS Journal of Indian Management, XIX, 56-65.
- Setiawati, I., Ardiansyah, & Taufikurohman, R. (2021). Price volatility of staple food using ARCH-GARCH model. IOP Conference Series: Earth and Environmental Science, 653(1).
- Setiyowati, S., Pasaribu, U. S., & Mukhaiyar, U. (2013). Non-stationary model for rice prices in Bandung, Indonesia. Proceedings of 2013 3rd International Conference on Instrumentation, Control and Automation, ICA 2013 (pp. 286-291).
- Sharma, N., Kaur, A., & Rawat, S. (2022). Does commitment to environment and society pays? Evidence from COVID-19 impact on stock volatility. Vilakshan - XIMB Journal of Management.
- Sharma, R. K., & Sharma, A. (2019). Forecasting monthly gold prices using ARIMA model: Evidence from indian gold market. International Journal of Innovative Technology and Exploring Engineering, 8(9 Special Issue 3), 1373-1376.
- Tasnia, M., Syed Jaafar AlHabshi, S. M., & Rosman, R. (2020). The impact of corporate social responsibility on stock price volatility of the US banks: a moderating role of tax. Journal of Financial Reporting and Accounting, 19(1), 77-91.
- Tejesh, H. R., & Bashav, J. V. (2023). Forecasting the Sensex and Nifty Indices using ARIMA and GARCH Models. MUDRA: Journal of Finance and Accounting, 10(1), 57-75.
- Tripathi, V., & Bhandari, V. (2015). Socially responsible stocks: a boon for investors in India. Journal of Advances in Management Research, 12(2), 209-225.
- Wang, L., Ma, F., Liu, J., & Yang, L. (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. International Journal of Forecasting, 36(2), 684-694.
- Wing-Yi Chio, S., Li, Y., & Jingran Yang, R. (2021). Realized Volatility Prediction. ACM International Conference Proceeding Series, 129-135.
- Yang, M. (2023). Stock Price prediction based on AR-ARCH model: A Case Study of ICBC. BCP Business & Management, 38, 766-774.
- Zhao, Y., & Chen, Z. (2022). Forecasting stock price movement: new evidence from a novel hybrid deep learning model. Journal of Asian Business and Economic Studies, 29(2), 91-104.
- Zili, A. H. A., Hendri, D., & Kharis, S. A. A. (2022). Peramalan Harga Saham Dengan Model Hybrid Arima-Garch dan Metode Walk Forward. Jurnal Statistika Dan Aplikasinya, 6(2), 341-354.
-
The place and perspectives of Ukraine in international integration space
Vitalina Babenko ,
Maryna Pasmor ,
Juliia Pankova ,
Mykhailo Sidorov
doi: http://dx.doi.org/10.21511/ppm.15(1).2017.08
Problems and Perspectives in Management Volume 15, 2017 Issue #1 pp. 80-92 Views: 1545 Downloads: 247 TO CITE АНОТАЦІЯThe resulting indexes for integration processes state development analysis were formed. The integral indexes illustrating integration state were calculated on the basis of member countries of BRICS and Ukraine. It provided the possibility to evaluate socioeconomic state of the countries from the point of view of international integration and create integration processes development scenarios. The strategic directions of integration processes development for Ukraine and member countries of BRICS were formed.
-
Political risk and banking sector performance in Nigeria
Adefemi A. Obalade ,
Babatunde Lawrence ,
Joseph Olorunfemi Akande
doi: http://dx.doi.org/10.21511/bbs.16(3).2021.01
Banks and Bank Systems Volume 16, 2021 Issue #3 pp. 1-12 Views: 1539 Downloads: 1066 TO CITE АНОТАЦІЯPolitical risk is prevalent in Nigeria and tends to influence business outcomes and the stability of the banking system. As a result of this study, it was determined whether political risk matters to the performance of the banking sector in Nigeria. The effect of political risk on different banks’ performance measures, such as return on assets, return on invested capital, credit risk and stock price, were examined in a panel of 12 selected commercial banks for the period 2006–2018. Data was analyzed using a two-stage system of generalized method of moments. The results provided evidence that the effect of political risk on bank performance depends on the performance proxies. Specifically, political risk was found to be negatively related to banks’ returns on invested capital and positively related to deteriorating credit risk. Hence, it can be concluded that political risk induces poor banking system performance in Nigeria. The study provides a critical insight into the management of a country’s political systems in terms of their potential to create unfavorable conditions for banking systems to thrive.
-
IFRS compliance and stock prices influence: evidence from Jordanian banks
Adel K. Almasarwah ,
Ahmad M. Omoush ,
Nizar Alsharari
doi: http://dx.doi.org/10.21511/bbs.13(3).2018.03
Banks and Bank Systems Volume 13, 2018 Issue #3 pp. 24-35 Views: 1344 Downloads: 204 TO CITE АНОТАЦІЯThis study aims to examine the IFRS compliance in Jordanian banks and its relation to stock prices. The impact of compliance with International Financial Reporting Standards (IFRS) on stock prices in Jordanian banks is a rarely researched subject in accounting and finance, but whether IFRS compliance has a serious impact on stock prices, particularly in developing countries, is still unknown. Numerous factors in developing countries, such as cultural, political, and economic circumstances, can create different effects for IFRS compliance from those seen in developed countries.
This paper concludes that IFRS compliance negatively affects stock prices, and firm size has a positive relationship with stock prices in Jordanian banks. The paper has significant implications for IFRS compliance research on stock prices, particularly across Jordanian banks, in responding to recent calls to bridge the gap that has been identified as a result of the revolutions in the Middle East. This study has been carried out in order to attract investors to avoid opposite results compared with prior literature that has studied the same subject. Hence, there are essential implications for the way in which successful IFRS compliance can be positively associated with stock prices in Jordanian banks.
