Navigating Inflationary Stress Through Adaptive Investment Strategies
Article Sidebar
Main Article Content
Abstract
Inflation is a central determinant of stock market behavior, yet its effects vary across economies and periods. While moderate inflation may sustain growth, high inflation combined with systemic shocks can destabilize markets and erode investor confidence. This study adopted a comparative analysis of historical data from the United States and India between 2001 and 2023, focusing on inflation trends, stock market indices (S&P 500 and NIFTY), and policy responses. The analysis combined quantitative market data with qualitative assessments of policy interventions and sectoral performance. Findings reveal that moderate inflation of 2–4% supported consistent S&P 500 growth from 2010 to 2019, whereas extreme inflation, 12.7% in India (2013) and 9.1% in the US (2022) led to market volatility and portfolio reallocation. Severe contractions were observed during crises, including a -51.79% decline in India’s NIFTY (2009) and a -38.49% fall in the US S&P 500 (2008). Defensive sectors such as utilities and consumer staples provided stability, while technology and growth sectors were highly vulnerable. Policy responses strongly influenced market outcomes. Gradual interventions, such as US interest rate hikes in 2022–2023, moderated risks, while abrupt reforms like India’s GST in 2017 intensified instability. Diversification, macroeconomic monitoring, AI-driven predictive analytics, and ESG integration emerged as key strategies to mitigate inflationary stress. The results highlight that inflation’s impact is context-dependent and amplified by crises, but adaptive strategies can strengthen resilience. Limitations stem from reliance on historical data, suggesting future research should explore AI forecasting and ESG frameworks across broader global markets.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
How to Cite
References
Abaidoo, R., & Anyigba, H. (2020). Bank performance variability and strands of inflationary conditions. European Journal of Management and Business Economics, 29(3). https://doi.org/10.1108/EJMBE-09-2018-0100
Ahmad, N., Jaysawal, M. K., Sangal, I., Kumar, S., & Alam, K. (2023). Carbon Tax and Inflationary Conditions under Learning Effects: A Green EOQ Inventory Model. Macromolecular Symposia, 407(1). https://doi.org/10.1002/masy.202200117
Barko, T., Cremers, M., & Renneboog, L. (2022). Shareholder Engagement on Environmental, Social, and Governance Performance. Journal of Business Ethics, 180(2). https://doi.org/10.1007/s10551-021-04850-z
Barrdear, J., & Kumhof, M. (2022). The macroeconomics of central bank digital currencies. Journal of Economic Dynamics and Control, 142. https://doi.org/10.1016/j.jedc.2021.104148
Beer, F. M., & Lin, F. (2019). Sports sentiment and stock returns: The Bombay stock exchange. ACRN Journal of Finance and Risk Perspectives, 8(1). https://doi.org/10.35944/jofrp.2019.8.1.003
Begum, S., Ashfaq, M., Luu, T. T., & Wang, D. (2025). Does green training foster green service innovation? The influence of organizational learning capability and human capital. International Journal of Hospitality Management, 130, 104266. https://doi.org/10.1016/j.ijhm.2025.104266
Ben Romdhane, Y., Kammoun, S., & Loukil, S. (2024). The impact of Fintech on inflation and unemployment: the case of Asia. Arab Gulf Journal of Scientific Research, 42(1). https://doi.org/10.1108/AGJSR-08-2022-0146
Bhalla, N., Sharma, R. K., & Kaur, I. (2023). Effect of Goods and Service Tax System on Business Performance of Micro, Small and Medium Enterprises. SAGE Open, 13(2). https://doi.org/10.1177/21582440231177210
Chen, J., & Wang, Y. (2021). Social media use for health purposes: Systematic review. In Journal of Medical Internet Research (Vol. 23, Issue 5). https://doi.org/10.2196/17917
Creswell, J. W. (2003). Research design Qualitative quantitative and mixed methods approaches. Research Design Qualitative Quantitative and Mixed Methods Approaches. https://doi.org/10.3109/08941939.2012.723954
Dananjoyo, R., Cahaya, F. R., & Riyadh, H. A. (2020). The Prominence of Financial Considerations on Housing Investors’ Purchase Decisions. Journal of Asian Finance, Economics and Business, 7(12). https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.869
Elton, E. J., & Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of Banking and Finance, 21(11–12). https://doi.org/10.1016/S0378-4266(97)00048-4
Fama, E. F. (1981). Stock Returns, Real Activity, Inflation, and Money. American Economic Association Stock Returns The American Economic Review, 71(4).
Fama, E. F., & Schwert, G. W. (1977). Asset returns and inflation. Journal of Financial Economics, 5(2), 115–146. https://doi.org/10.1016/0304-405X(77)90014-9
Fernández-Villaverde, J., Sanches, D., Schilling, L., & Uhlig, H. (2021). Central bank digital currency: Central banking for all? Review of Economic Dynamics, 41. https://doi.org/10.1016/j.red.2020.12.004
Green, D., Huang, Y., & Shen, C. H. (2023). Inflationary Adler conditions. Physical Review D, 107(4). https://doi.org/10.1103/PhysRevD.107.043534
Hesse-Biber, S. (2010). Qualitative approaches to mixed methods practice. Qualitative Inquiry, 16(6). https://doi.org/10.1177/1077800410364611
Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE Stock Market Prediction Using Deep-Learning Models. Procedia Computer Science, 132. https://doi.org/10.1016/j.procs.2018.05.050
Izzeldin, M., Muradoğlu, Y. G., Pappas, V., Petropoulou, A., & Sivaprasad, S. (2023). The impact of the Russian-Ukrainian war on global financial markets. International Review of Financial Analysis, 87. https://doi.org/10.1016/j.irfa.2023.102598
Jain, T., & Singh, S. P. (2022). Impact of Domestic Institutional Investors on Foreign Institutional Investors in India: An Analysis. Management Dynamics, 19(2). https://doi.org/10.57198/2583-4932.1020
Krajnakova, E., Navickas, V., & Kontautiene, R. (2018). Effect of macroeconomic business environment on the development of corporate social responsibility in Baltic Countries and Slovakia. Oeconomia Copernicana, 9(3). https://doi.org/10.24136/oc.2018.024
Krohn, W. B., Elowe, K. D., & Boone, R. B. (1995). Relations among fishers, snow, and martens: development and evaluation of two hypotheses. Forestry Chronicle, 71(1). https://doi.org/10.5558/tfc71097-1
Mandeya, S. M. T., & Ho, S. Y. (2021). Inflation, inflation uncertainty and the economic growth nexus: An impact study of South Africa. MethodsX, 8. https://doi.org/10.1016/j.mex.2021.101501
Muharam, H., Robiyanto, R., Pangestuti, I. R. D., & Mawardi, W. (2020). Measuring asian stock market integration by using orthogonal generalized autoregressive conditional heteroscedasticity. Montenegrin Journal of Economics, 16(1). https://doi.org/10.14254/1800-5845/2020.16-1.8
Musarat, M. A., Alaloul, W. S., & Liew, M. S. (2021). Impact of inflation rate on construction projects budget: A review. In Ain Shams Engineering Journal (Vol. 12, Issue 1). https://doi.org/10.1016/j.asej.2020.04.009
Peillex, J., Erragragui, E., Bitar, M., & Benlemlih, M. (2019). The contribution of market movements, asset allocation and active management to Islamic equity funds’ performance. Quarterly Review of Economics and Finance, 74. https://doi.org/10.1016/j.qref.2018.03.013
Prodan, S., & Dabija, D. C. (2023). Adapting the Digital Servuction Model to Central Banks: Case Study: European Central Bank. Administrative Sciences, 13(10). https://doi.org/10.3390/admsci13100217
Reinhart, C. M., & Rogoff, K. S. (2009). This time is different: Eight centuries of financial folly. In This Time is Different: Eight Centuries of Financial Folly. https://doi.org/10.1080/09585206.2010.512722
Ruzima, M., Boachie, M. K., Põlajeva, T., & Iddrisu, A. A. (2023). Does the Fisher effect hold in Rwanda? Quality and Quantity, 57(3). https://doi.org/10.1007/s11135-022-01479-6
Sahoo, A. P. (2023). Macroeconomic Impression and Performance of stock market in India. International Journal of Finance, Entrepreneurship & Sustainability. https://doi.org/10.56763/ijfes.vi.160
Saydaliev, H. B., & Chin, L. (2023). Role of green financing and financial inclusion to develop the cleaner environment for macroeconomic stability: Inter-temporal analysis of ASEAN economies. Economic Change and Restructuring, 56(6). https://doi.org/10.1007/s10644-022-09419-y
Simionescu, M. (2025). Machine Learning vs. Econometric Models to Forecast Inflation Rate in Romania? The Role of Sentiment Analysis. Mathematics, 13(1), 168. https://doi.org/10.3390/math13010168
Sirola, N., & Pitesa, M. (2018). The macroeconomic environment and the psychology of work evaluation. Organizational Behavior and Human Decision Processes, 144. https://doi.org/10.1016/j.obhdp.2017.09.003
Surtee, T. G. H., & Alagidede, I. P. (2023). A novel approach to using modern portfolio theory. Borsa Istanbul Review, 23(3). https://doi.org/10.1016/j.bir.2022.12.005
Takona, J. P. (2024). Research design: qualitative, quantitative, and mixed methods approaches / sixth edition. In Quality and Quantity (Vol. 58, Issue 1). https://doi.org/10.1007/s11135-023-01798-2
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Confer. Series on Public Policy, 39(C). https://doi.org/10.1016/0167-2231(93)90009-L
Tse, Y. K., & Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics, 20(3). https://doi.org/10.1198/073500102288618496
Valogo, M. K., Duodu, E., Yusif, H., & Baidoo, S. T. (2023). Effect of exchange rate on inflation in the inflation targeting framework: Is the threshold level relevant? Research in Globalization, 6. https://doi.org/10.1016/j.resglo.2023.100119
Zhang, C., & Zhao, S. (2023). The macroeconomic announcement premium and information environment. Journal of Monetary Economics, 139. https://doi.org/10.1016/j.jmoneco.2023.06.005
Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36. https://doi.org/10.1016/j.frl.2020.101528
Zhang, T., & Huang, Z. (2022). Blockchain and central bank digital currency. ICT Express, 8(2). https://doi.org/10.1016/j.icte.2021.09.014