Predicting the Stock Price using Historical Price, Volatility and Downside Risks
DOI:
https://doi.org/10.56225/gjbesd.v2i1.29Keywords:
Stock price, Volatility risk, Downside riskAbstract
This study examines and analyses the influence of historical stock prices, volatility risk, and downside risk on stock prices in the rapidly growing Infrastructure sector on the Indonesian Stock Exchange (IDX) from 2014 to 2022. Using a nine-year dataset, we integrated standard deviation and Ulcer Index as volatility and downside risk measures, respectively, employing Structural Equation Modeling (SEM). Results show a significant positive effect of historical stock prices on future prices in this dynamic market. However, this positive trend is counteracted by the negative effect of volatility risk on stock prices, emphasising the importance of risk management strategies in the Infrastructure sector. We have also identified a positive effect for historical volatility risk (t) on future volatility risk (t+1), indicating a potential connection between past and future market volatility. Also, downside risk is established as a negative effect on stock prices, emphasising risk mitigation in historical stock performance. In addition to indirect effects, we found that historical stock price (t) plays a mediating role, affirming the influence of downside risk (t) on future stock price (t+1). This research provides valuable insights into predicting future stock prices, offering a comprehensive understanding of the interplay among historical performance, volatility, and downside risk. These findings contribute to developing effective strategies for investors and policymakers to navigate the complexities of the stock market.
References
Ali, H. (2019). Does downside risk matter more in asset pricing? Evidence from China. Emerging Markets Review, 39, 154–174. https://doi.org/10.1016/J.EMEMAR.2019.05.001
Borochin, P., & Zhao, Y. (2019). Belief heterogeneity in the option markets and the cross-section of stock returns. Journal of Banking & Finance, 107, 105591. https://doi.org/10.1016/J.JBANKFIN.2019.07.011
Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A Systematic review. Expert Systems with Applications, 156, 113464. https://doi.org/10.1016/J.ESWA.2020.113464
Chen, W., Jiang, M., Zhang, W. G., & Chen, Z. (2021). A novel graph convolutional feature based convolutional neural network for stock trend prediction. Information Sciences, 556, 67–94. https://doi.org/10.1016/J.INS.2020.12.068
Chudziak, A. (2023). Predictability of stock returns using neural networks: Elusive in the long term. Expert Systems with Applications, 213, 119203. https://doi.org/10.1016/J.ESWA.2022.119203
Dixit, S., & Soni, N. (2023). Enhancing stock market prediction using three-phase classifier and EM-EPO optimisation with news feeds and historical data. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-17184-x
Ergun, L. M. (2019). Extreme Downside Risk in Asset Returns. www.bank-banque-canada.ca
Freitas, W. B., & Bertini, J. R. (2023). Random walk through a stock network and predictive analysis for portfolio optimisation. Expert Systems with Applications, 218, 119597. https://doi.org/10.1016/J.ESWA.2023.119597
Gil-Alana, L. A., Gupta, R., Shittu, O. I., & Yaya, O. O. S. (2018). Market efficiency of Baltic stock markets: A fractional integration approach. Physica A: Statistical Mechanics and Its Applications, 511, 251–262. https://doi.org/10.1016/J.PHYSA.2018.07.029
Gong, X. L., Liu, J. M., Xiong, X., & Zhang, W. (2022). Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network. International Review of Financial Analysis, 84, 102359. https://doi.org/10.1016/J.IRFA.2022.102359
Huang, W., Luo, Y., & Zhang, C. (2022). Accounting-based downside risk and stock price crash risk: Evidence from China. Finance Research Letters, 45, 102152. https://doi.org/10.1016/J.FRL.2021.102152
investing.com. (n.d.). IDX Infrastructure (JKINFRA). Retrieved November 1, 2023, from https://id.investing.com/indices/idx-infrastructure
Jaroonchokanan, N., Termsaithong, T., & Suwanna, S. (2022). Dynamics of hierarchical clustering in stocks market during financial crises. Physica A: Statistical Mechanics and Its Applications, 607, 128183. https://doi.org/10.1016/J.PHYSA.2022.128183
Lei, L. (2018). Wavelet Neural Network Prediction Method of Stock Price Trend Based on Rough Set Attribute Reduction. Applied Soft Computing, 62, 923–932. https://doi.org/10.1016/J.ASOC.2017.09.029
Liu, H., & Zhang, Q. (2021). Firm age and realised idiosyncratic return volatility in China: The role of short-sales constraints. International Review of Financial Analysis, 75, 101745. https://doi.org/10.1016/J.IRFA.2021.101745
Liu, L., & Pan, Z. (2020). Forecasting stock market volatility: The role of technical variables. Economic Modelling, 84, 55–65. https://doi.org/10.1016/J.ECONMOD.2019.03.007
Luo, Y., Wang, X., Zhang, C., & Huang, W. (2021). Accounting-based downside risk and expected stock returns: Evidence from China. International Review of Financial Analysis, 78, 101920. https://doi.org/10.1016/J.IRFA.2021.101920
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
Mohrschladt, H. (2021). The ordering of historical returns and the cross-section of subsequent returns. Journal of Banking & Finance, 125, 106064. https://doi.org/10.1016/J.JBANKFIN.2021.106064
Saraf, M., & Kayal, P. (2023). How much does volatility influence stock market returns? Empirical evidence from India. IIMB Management Review, 35(2), 108–123. https://doi.org/https://doi.org/10.1016/j.iimb.2023.05.004
Spence, M. (1974). Competitive and optimal responses to signals: An analysis of efficiency and distribution. Journal of Economic Theory, 7(3), 296–332. https://doi.org/10.1016/0022-0531(74)90098-2
Statista. (2023, May). Government budget for infrastructure in Indonesia from 2014 to 2023. https://www.statista.com/statistics/1147908/indonesia-government-infrastructure-budget/
Traub, R. (2019). Understanding Volatility: An Analysis of the Stock Market Return-Understanding Volatility: An Analysis of the Stock Market Return-Variance Correlation Variance Correlation. https://opencommons.uconn.edu/srhonors_theses
Tsafack, G., Becker, Y., & Han, K. (2023). Earnings announcement premium and return volatility: Is it consistent with risk-return trade-off? Pacific-Basin Finance Journal, 79, 102029. https://doi.org/10.1016/J.PACFIN.2023.102029
Vasudevan, E. V. (2023). Some gains are riskier than others: Volatility changes and the disposition effect. Journal of Economic Behavior & Organization, 214, 68–81. https://doi.org/10.1016/J.JEBO.2023.07.034
Wang, T., Guo, J., Shan, Y., Zhang, Y., Peng, B., & Wu, Z. (2023). A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction. Applied Soft Computing, 145, 110595. https://doi.org/10.1016/J.ASOC.2023.110595
Xie, N., Wang, Z., Chen, S., & Gong, X. (2019). Forecasting downside risk in China’s stock market based on high-frequency data. Physica A: Statistical Mechanics and Its Applications, 517, 530–541. https://doi.org/10.1016/J.PHYSA.2018.11.028
Zhang, Q., Zhang, Y., Bao, F., Liu, Y., Zhang, C., & Liu, P. (2024). Incorporating stock prices and text for stock movement prediction based on information fusion. Engineering Applications of Artificial Intelligence, 127, 107377. https://doi.org/10.1016/J.ENGAPPAI.2023.107377
Zhou, Z., Gao, M., Liu, Q., & Xiao, H. (2020). Forecasting stock price movements with multiple data sources: Evidence from stock market in China. Physica A: Statistical Mechanics and Its Applications, 542, 123389. https://doi.org/10.1016/J.PHYSA.2019.123389
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