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All Journal International Journal of Financial, Accounting, and Management IJBAE
Takaliuang, Noldy
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Investor Reactions to Russia-Ukraine War Years 2022 Using a Bayesian Analysis Approach Morozov, Dmitry Alekseyevich Morozov; Takaliuang, Noldy; Hasanah, Yulia Nur Hasanah
International Journal Business and Entrepreneurship Vol 2 No 1 (2025): March
Publisher : ICON Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71154/465nvw26

Abstract

The purpose of this study was to determine the market reaction to changes in stock prices on the Russian-Ukraine war event on the Indonesia Stock Exchange in 2022. This research using Bayesian analysis Approach on banking stocks. The results show that the AAR and CAR Before-After of the three models do not show significant results or market anomaly. The implications of the results of this research for investors and researchers that the events of the Russian-Ukraine war in 2022 can be taken into consideration for investors to be more rational in responding to political events in making investments in both the financial sector or other sectors, and for other researchers the EMH theory is no longer relevant to recent world developments. The limitations in this study are only in the financial sector in banking stocks, and the future big agenda will be expanded in several sectors, namely manufacturing, infrastructure and technology on the Indonesia stock exchange. The novelty in this research is that it uses the Bayesian analysis approach to the three models of approaches that have been and are often used, but in this case it is very different from previous researchers who have never used the Bayesian analysis approach
Modeling Market Reactions with Bayesian Return Adjustment in Financial Event Studies Takaliuang, Noldy; Makhfudi, Makhfudi; Taroreh , Stefen
International Journal of Financial, Accounting, and Management Vol. 7 No. 4 (2026): March
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijfam.v7i4.3222

Abstract

Purpose: This study introduces the Bayesian Mean Adjusted Return Model (BMARM) to expand financial event study analysis and address the limitations of frequentist approaches during geopolitical crises. The model is applied to assess Indonesian banking stock reactions to the Russia–Ukraine war, focusing on abnormal returns, volatility shifts, and market stabilization. Research Methodology: Using daily stock returns of 44 IDX-listed banking issuers from July 2021 to March 2022, this study combines event study methodology with Bayesian inference. Bayesian Paired Sample t-tests, prior distribution selection, abnormal return estimation (BAAR and BCAR), and robustness checks were conducted using JASP. Expected returns were generated using Bayesian mean-adjusted, market-adjusted, and market models. Results: The findings show short-term negative shifts in BAAR immediately after the event, indicating increased volatility and declining investor sentiment. However, BCAR reflects a gradual improvement, suggesting a partial market recovery. Bayesian tests show weak evidence of differences between pre- and post-event abnormal returns, and robustness checks reveal sensitivity to prior assumptions. Conclusions: BMARM offers a more adaptive and probabilistic assessment of market reactions than classical models, capturing uncertainty during geopolitical shocks. It supports market efficiency patterns in which short-term disruptions eventually transition to stabilization. Limitations: The model is computationally intensive, dependent on prior selection, and limited by sample size. Further adaptation is required for nonfinancial market applications. Contributions: This study advances Bayesian empirical finance by introducing BMARM as a novel framework for event studies in emerging markets.
Co-Authors Hasanah, Yulia Nur Hasanah Makhfudi, Makhfudi Morozov, Dmitry Alekseyevich Morozov Taroreh , Stefen