Strategic portfolio rebalancing: Integrating predictive models and adaptive optimization objectives in a dynamic market
-
Author(s)Adeline ClarissaLink to ORCID Index: https://orcid.org/0009-0005-9701-5915
,
Deddy Priatmodjo KoesrindartotoLink to ORCID Index: https://orcid.org/0000-0003-2445-822X
-
DOIhttp://dx.doi.org/10.21511/imfi.21(3).2024.25
-
Article InfoVolume 21 2024, Issue #3, pp. 304-316
- 128 Views
-
27 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Adjusting investment strategy is one of the ways to handle dynamic market conditions. This study proposes a novel portfolio management strategy using appropriate optimization objectives for different stock market trends while also incorporating market trends and stock return predictions The optimization objectives that will be evaluated for different market trends are maximizing the Sharpe ratio, minimizing risk, and minimizing expected shortfall. This study utilizes simulation modelling with various predictive models on building the portfolios. The results show that, in an upward market trend, the strategy is to choose stocks with positive returns, and the objective is to maximize the Sharpe ratio. The portfolio that follows this strategy during upward market trends has greater returns than both the Indonesian Composite Index and LQ45, which serve as stock market benchmarks, with 90% certainty. Meanwhile, during the downward market trend, the strategy is to choose stocks with a negative correlation with the Indonesian Composite Index, and the proper optimization objective is to minimize risk. A portfolio that follows this strategy during downward market trends has greater returns than stock market benchmarks with 95% certainty. Across the evaluation period from 2018 to 2023, the portfolio using the proposed strategy outperforms both stock market benchmarks, with a higher quarterly Sharpe ratio of 0.3047 and cumulative return of 107.90%. The proposed portfolio has a higher quarterly return than the stock market benchmark with 99% certainty. Therefore, the proposed strategy shows a promising result in a dynamic market.
- Keywords
-
JEL Classification (Paper profile tab)G11, G17, C61
-
References39
-
Tables13
-
Figures4
-
- Figure 1. Research methodology framework
- Figure 2. Boxplot of return prediction squared error
- Figure 3. Boxplot of return volatility prediction squared error
- Figure 4. Proposed portfolio value comparison with IHSG and LQ45
-
- Table 1. Final pool of stocks
- Table 2. Time period of the data
- Table 3. Data distribution for training and testing
- Table 4. Hyperparameter list
- Table 5. Data distribution for training and testing volatility prediction
- Table 6. Optimization models
- Table 7. Optimum hyperparameter and accuracy for stock market index prediction
- Table 8. Performance comparison of portfolio with different objectives in upward trend condition
- Table 9. Statistical result on port SR’s return compared to IHSG and LQ45 in upward trend condition
- Table 10. Performance comparison of portfolio with different objectives in downward trend condition
- Table 11. Statistical result on port risk’s return compared to IHSG and LQ45 in downward trend condition
- Table 12. Proposed portfolio comparison with IHSG and LQ45
- Table 13. Statistical result on proposed portfolio’s return compared to IHSG and LQ45
-
- Awalludin, S. A., Ulfah, S., & Soro, S. (2018). Modeling the stock price returns volatility using GARCH(1,1) in some Indonesia stock prices. Journal of Physics: Conference Series, 948(1).
- Bertsimas, D., Lauprete, G. J., & Samarov, A. (2004). Shortfall as a risk measure: properties, optimization and applications. Journal of Economic Dynamics and Control, 28(7), 1353-1381.
- Budiandru, B. (2021). ARCH and GARCH Models on the Indonesian Sharia Stock Index. Jurnal Akuntansi Dan Keuangan Islam [Journal of Islamic Accounting and Finance], 9(1), 27-38.
- Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A Systematic review. Expert Systems with Applications, 156.
- Drakopoulou, V. (2016). A Review of Fundamental and Technical Stock Analysis Techniques. Journal of Stock & Forex Trading, 05(01).
- Eiamkanitchat, N., Moontuy, T., & Ramingwong, S. (2017). Fundamental analysis and technical analysis integrated system for stock filtration. Cluster Computing, 20(1), 883-894.
- Fuad, F., & Yuliadi, I. (2021). Determinants of the Composite Stock Price Index (IHSG) on the Indonesia Stock Exchange. Journal of Economics Research and Social Sciences, 5(1).
- Gao, P., Zhang, R., & Yang, X. (2020). The Application of Stock Index Price Prediction with Neural Network. Mathematical and Computational Applications, 25(3).
- Hidayat, A., Liliana, L., & Andaiyani, S. (2021). Factors Affecting the Composite Stock Price Index during Covid-19 Pandemic Crisis. Jurnal Ekonomi dan Kebijakan [Journal of Economics and Policy], 14(2), 333-344.
- Hwang, S., & Satchell, S. E. (2010). How loss averse are investors in financial markets? Journal of Banking and Finance, 34(10), 2425-2438.
- Jadhav, D., & Ramanathan, T. V. (2019). Portfolio optimization based on modified expected shortfall. Studies in Economics and Finance, 36(3), 440-463.
- Jiao, Y., & Jakubowicz, J. (2017). Predicting stock movement direction with machine learning: An extensive study on S&P 500 stocks. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018-January.
- Kara, Y., Acar Boyacioglu, M., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311-5319.
- Kuo, C. H., Chen, C. T., Lin, S. J., & Huang, S. H. (2021). Improving generalization in reinforcement learning-based trading by using a generative adversarial market model. IEEE Access, 9, 50738-50754.
- Levy, M. (2016). Measuring Portfolio Performance: Sharpe, Alpha, or the Geometric Mean? SSRN Electronic Journal.
- Liu, Y., Zhou, G., & Zhu, Y. (2021). Maximizing the Sharpe Ratio: A Genetic Programming Approach. SSRN Electronic Journal.
- Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165.
- Maji, G., Mondal, D., Dey, N., Debnath, N. C., & Sen, S. (2021). Stock prediction and mutual fund portfolio management using curve fitting techniques. Journal of Ambient Intelligence and Humanized Computing, 12(10), 9521-9534.
- Markowitz, H. (1952). PORTFOLIO SELECTION. The Journal of Finance, 7(1), 77-91.
- Meoqui, L. M., & Pedraza, J. M. (2011). The Importance of Adopting a Good Management Strategy. Journal of Current Issues in Finance, Business and Economics, 4, 221-253.
- Milovidov, V. (2021). Investors Behavior Under Growing Financial Market Uncertainty. SSRN Electronic Journal.
- Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
- Putri, T., Sugiharto, B., & Salsabila, Z. (2021). The Effect Of The Asian Stock Price Index On The Jakarta Composite Index Before And During Covid-19. JASS (Journal of Accounting for Sustainable Society), 3(02).
- Rasyid, A. F., Agushinta, D., & Ediraras, D. T. (2021). Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG). International Journal of Computer and Information Technology (2279-0764), 10(5), 209-217.
- Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21-42.
- Schultz, H. D. (2002). Bear market investing strategies. Wiley.
- Sharpe, W. F. (1994). The Sharpe Ratio. The Journal of Portfolio Management, 21(1), 49-58.
- Sokolowska, J., & Makowiec, P. (2017). Risk preferences of individual investors: The role of dispositional tendencies and market trends. Journal of Behavioral and Experimental Economics, 71, 67-78.
- Solares, E., De-León-Gómez, V., Salas, F. G., & Díaz, R. (2022). A comprehensive decision support system for stock investment decisions. Expert Systems with Applications, 210.
- Ta, V. D., Liu, C. M., & Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2).
- van Staden, P. M., Forsyth, P. A., & Li, Y. (2024). Across-time risk-aware strategies for outperforming a benchmark. European Journal of Operational Research, 313(2), 766-800.
- Wang, L., Ahmad, F., Luo, G., Umar, M., & Kirikkaleli, D. (2022). Portfolio optimization of financial commodities with energy futures. Annals of Operations Research, 313(1), 401-439.
- 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.
- Wu, M. E., Syu, J. H., Lin, J. C. W., & Ho, J. M. (2021). Portfolio management system in equity market neutral using reinforcement learning. Applied Intelligence, 51(11), 8119-8131.
- Yang, F., Chen, Z., Li, J., & Tang, L. (2019). A novel hybrid stock selection method with stock prediction. Applied Soft Computing Journal, 80, 820-831.
- Yang, H., Liu, X. Y., & Wu, Q. (2018). A Practical Machine Learning Approach for Dynamic Stock Recommendation. Proceedings – 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018.
- Yollanda, M., Devianto, D., & Yozza, H. (2018). Nonlinear Modeling of IHSG with Artificial Intelligence. Proceedings of ICAITI 2018 - 1st International Conference on Applied Information Technology and Innovation: Toward A New Paradigm for the Design of Assistive Technology in Smart Home Care.
- Yu, J. R., Paul Chiou, W. J., Lee, W. Y., & Lin, S. J. (2020). Portfolio models with return forecasting and transaction costs. International Review of Economics and Finance, 66, 118-130.
- Yuan, X., Yuan, J., Jiang, T., & Ain, Q. U. (2020). Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market. IEEE Access, 8.
-
Perceived health risk, online retail ethics, and consumer behavior within online shopping during the COVID-19 pandemic
Yuniarti Fihartini ,
Arief Helmi ,
Meydia Hassan ,
Yevis Marty Oesman
doi: http://dx.doi.org/10.21511/im.17(3).2021.02
Innovative Marketing Volume 17, 2021 Issue #3 pp. 17-29 Views: 4420 Downloads: 1703 TO CITE АНОТАЦІЯThe risk of virus contracting during the COVID-19 pandemic has changed consumer preference for online shopping to meet their daily needs than shopping in brick-and-mortar stores. Online shopping presents a different environment, atmosphere, and experience. The possibility of ethical violations is higher during online than face-to-face transactions. Therefore, this study was conducted to investigate the influence of perceived health risk and customer perception of online retail ethics on consumer online shopping behavior during the COVID-19 pandemic, involving seven variables, namely perceived health risk, security, privacy, non-deception, reliability fulfillment, service recovery, and online shopping behavior. The data were collected through an online survey by employing the purposive sampling technique to a consumer who has shopped online during the COVID-19 pandemic in Indonesia. 315 valid responses were obtained and analyzed through quantitative method using SEM-Amos. The results showed that perceived health risk and four variables of online retail ethics including security, privacy, reliability fulfillment, and service recovery affected online shopping behavior. Meanwhile, non-deception was found to have an insignificant effect. The coefficient value proved perceived health risk to be more dominant in influencing online shopping behavior than the variables of online retail ethics. Thus, consumers pay more concern for their health during online shopping. However, positive consumer perceptions of the behavior of online retail websites in providing services also can encourage consumers to shop online during this pandemic.
-
Human resource management in promoting innovation and organizational performance
I Gede Riana ,
Gede Suparna ,
I Gusti Made Suwandana ,
Sebastian Kot 
,
Ismi Rajiani
doi: http://dx.doi.org/10.21511/ppm.18(1).2020.10
Problems and Perspectives in Management Volume 18, 2020 Issue #1 pp. 107-118 Views: 3445 Downloads: 811 TO CITE АНОТАЦІЯHuman resource management (HRM) is one of the elements enabling an organization to remain competitive in turbulence conditions. The effective practice of HRM makes competent and innovative employees contributing to the achievement of organizational objectives. This study aims to analyze HRM practices in creating innovation and organizational performance. The questionnaire was used to measure the respondents’ perceptions of variables used by a Likert scale. A survey of 126 manager samples and middle managers at export-oriented short and medium enterprises (SMEs) in Bali, Indonesia, was conducted to test the model. The analysis has shown that the proposed model was proven to be compliant with the research hypotheses. HRM significantly affects organizational performance and innovation, and it was found out that innovation can improve organizational performance. However, in the process of simultaneous testing, it was found out that innovation cannot improve organizational performance. The lack of attention to investments in human resources became one of the barriers to SMEs in creating innovation.
-
Positive contribution of the good corporate governance rating to stability and performance: evidence from Indonesia
RR. Iramani , Muazaroh Muazaroh , Abdul Mongid
doi: http://dx.doi.org/10.21511/ppm.16(2).2018.01
Problems and Perspectives in Management Volume 16, 2018 Issue #2 pp. 1-11 Views: 3142 Downloads: 303 TO CITE АНОТАЦІЯThis paper aims to examine the impact of Good Corporate Governance (GCG) practice on bank stability and performance. Governance is measured using the GCG rating that covers eleven aspects. The authors apply instrumental regression to link governance to performance and stability. The study covers a sample of 150 banks. The result shows that bank stability can mediate bank governance and bank performance. On the determinant of bank performance, it can be concluded that the GCG rating is positive and directly influences bank performance. Bank stability is also positive for bank performance indicating the indirect contribution of the GCG rating to bank performance. NPL, LDR, CAR and bank’s size (LASSET) are all negative and significant. The aim of this paper is to provide strong empirical evidence on the importance of governance and stability for performance. The limitations of this paper are the size of the sample and that it only covers public banks which are theoretically required to apply better governance in all aspects of their business by the Capital Market Authority.
