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AI, Big Data and Quantitative Methods in Finance
Published by Institut Pertanian Bogor
ISSN : -     EISSN : 31636586     DOI : https://doi.org/10.17358/abq
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AI, Big Data and Quantitative Methods in Finance (ABQ) covers theoretical and empirical research in finance that applies artificial intelligence, big data analytics, and advanced quantitative methods. The journal welcomes studies within, but not limited to, the following areas: Artificial intelligence and machine learning applications in finance Big data analytics for financial decision-making Quantitative finance and financial modeling Econometrics and statistical analysis in finance Risk management, credit risk, and financial stability analysis Financial technology (FinTech), digital banking, and blockchain analytics Algorithmic trading, portfolio optimization, and asset pricing Corporate finance analytics and performance measurement Behavioral finance using quantitative and data-driven approaches Forecasting, simulation, and optimization methods in financial research ABQ prioritizes rigorous methodologies, innovative analytical frameworks, and practical implications that contribute to the advancement of financial theory, policy, and practice in the digital era.
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Articles 5 Documents
 1 
Indonesia’s Financial System Stability: Effects of Domestic and Global Factors Syifa Rifa Rosyadah; Hermanto Siregar; Fahmi Salam Ahmad
AI, Big Data and Quantitative Methods in Finance Vol. 1 No. 1 (2026): ABQ Vol. 1 No. 1, April 2026
Publisher : School of Business, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/abq.1.1.1

Abstract

Background: DFinancial system stability is essential for ensuring efficient financial intermediation and sustainable economic growth. With increasing global financial integration, the stability of a country’s financial system is no longer determined solely by domestic conditions, but is also influenced by global factors through various transmission channels such as capital flows, exchange rates, and financial markets.Purpose: This study aims to examine the effects of both domestic and global factors on Indonesia’s financial system stability and to distinguish their impacts in the short run and the long run.Design/methodology/approach: This study employs monthly time series data from July 2003 to November 2022. Financial system stability is proxied by the Financial Stress Index (FSI). The analysis uses the Auto Regressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to capture both short-run dynamics and long-run relationships.Findings/Result: The results show that, in the short run, Indonesia’s financial system stability is significantly influenced by domestic lending rates, exchange rates, money supply, U.S. financial stress, and crisis conditions. In the long run, exchange rates, money supply, and foreign exchange reserves are found to have significant effects on financial system stability. The model also indicates a speed of adjustment toward long-run equilibrium of approximately 19.9 percent per period.Conclusion: Indonesia’s financial system stability is jointly determined by domestic and global factors, with distinct roles across time horizons. These findings highlight the importance of policy responses that are not only domestically oriented but also responsive to global economic and financial developments.Originality/value (State of the art): This study contributes to the literature by integrating domestic and global determinants of financial system stability within a unified ARDL-ECM framework. Unlike prior studies that typically focus on either domestic or external factors, this research provides a more comprehensive perspective by demonstrating the combined and time-varying influence of both, particularly in the context of a small open economy like Indonesia. Keywords:ARDL-ECM, domestic factors, financial stress index, financial system stability,  global factors
Spillovers Between Indonesia's Green Index, Conventional Index and Global Stock Index: Which is More Stable? Ade Holis; Edi Sumanto; Roy Hendra Michael Sembel; Adler Haymans Manurung
AI, Big Data and Quantitative Methods in Finance Vol. 1 No. 1 (2026): ABQ Vol. 1 No. 1, April 2026
Publisher : School of Business, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/abq.1.1.12

Abstract

Background:  The increasing global concern over climate change has accelerated the growth of sustainable investment, including the development of green stock indices such as Indonesia’s SRI-KEHATI Index. However, despite their potential resilience and attractiveness, green indices remain exposed to volatility and interconnectedness with conventional and global financial markets. Therefore, understanding the dynamics of volatility and spillover effects between green, conventional, and global stock indices becomes crucial to assess their relative stability and investment potential. Purpose: This study aimed to measure the level volatility, connectedness and spillovers between Indonesia green index, Indonesia conventional stock index, and some global stock price indices in the last decade. Design/methodology/approach: Using daily return data from 2 February 2012 to 31 August 2022, this study used a rolling-samples of descriptive statistics approach and framework developed by Diebold and Yilmaz (2012) to measure the levels of volatility and spillovers between Indonesia's green index, Indonesia LQ45 index and several global stock indexes.Findings: The results of the analysis showed that volatility and spillovers that have occurred between variables are dynamic over time. When the volatility values of the variables tend to be low, Indonesia return green index tends to be higher, and on the other hand, when volatility is high, Indonesian return green index tends to be lower than the conventional return index volatility. In addition, during the analysis period, the spillovers that occurred between variables experienced a significant increase several times and then decreased again after a certain period of time. In the long run, the returns of Indonesia's green index tend to experience negative spillovers where the spillovers caused by the returns of the green stock index to all variables tend to be smaller than the spillovers received by the returns of the green stock index. In addition, the return movement of Indonesia's green stock index is generally more explained by the return movement of global stock price indexes compared to conventional stock indexes of Indonesia.Conclusion: The findings indicate that Indonesia’s green stock index delivers higher average returns compared to the conventional index, although it exhibits slightly higher volatility over the study period. Furthermore, the results reveal that volatility spillovers are dynamic and largely driven by global market movements, with the green index tending to receive more spillovers than it transmits in the long runOriginality/value: This study compares the volatility and connectedness between Indonesia green index and Indonesia conventional stock index with several global stock indexes. As an index that was just launched about ten years ago, research on the Indonesian green index was still limited. In addition, the rolling sample method that measures the dynamics of parameter changes over time such as time varying volatility and the Diebold and Yilmaz (2012) framework provide something new in the time series research method.  Keywords:green stock index, spillover, rolling samples, diebold-yielmaz, level volatility, descriptive statistics
Asymmetric Effects of Macroeconomic Variables on Stock Market Indices: Evidence From Developed and Emerging Economies Imatul Hamza; Linda Karlina Sari; Sendy Watazawwadu’Ilmi Watazawwadu’Ilmi; Fuad Wahdan Muhibuddin
AI, Big Data and Quantitative Methods in Finance Vol. 1 No. 1 (2026): ABQ Vol. 1 No. 1, April 2026
Publisher : School of Business, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/abq.1.1.25

Abstract

Background: Global financial markets are increasingly integrated, yet the response of stock indices to macroeconomic shocks remains poorly understood in its nonlinear dimensions. Most existing studies adopt symmetric linear frameworks that may fail to capture the differential market reactions to positive and negative macroeconomic changes.Purpose: This study investigates the asymmetric effects of key macroeconomic variables, namely exchange rates, gross domestic product (GDP), and interest rates, on stock market indices across 56 countries classified into 28 developed and 28 emerging economies over the period 2016Q1 to 2024Q3.Design/Methodology/Approach: This study employs panel Autoregressive Distributed Lag (ARDL) and panel Nonlinear Autoregressive Distributed Lag (NARDL) models estimated through Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effect (DFE) estimators. The Hausman test is applied to determine the optimal estimator. Asymmetry is formally tested using the Wald test. Unit root analysis uses the Augmented Dickey Fuller (ADF) test, and cointegration is verified via the Kao panel cointegration test.Findings/Result: The NARDL model consistently outperforms the symmetric ARDL specification across all country groups based on Akaike Information Criterion (AIC). Significant long-run asymmetric effects are identified for all three macroeconomic variables. Currency depreciation exerts a larger negative impact on stock indices than appreciation across both country groups. GDP growth positively drives stock markets in developed economies but has no significant effect in emerging markets. Interest rate cuts generate larger stock market responses than equivalent rate increases. In the short run, GDP and interest rate movements display asymmetric effects in emerging economies, while developed markets are more resilient to short-term macroeconomic fluctuations.Conclusion: Asymmetric quantitative modeling significantly enriches the understanding of macroeconomic transmission mechanisms in financial markets, with critical implications for monetary policy design and investment risk management in both developed and emerging economies.Originality/Value: This study provides one of the first comprehensive panel NARDL analyses spanning 56 countries across both developed and emerging markets simultaneously over a post-2016 dataset, explicitly testing directional asymmetry in the GDP, exchange rate, and interest rate transmission to stock markets. The findings advance the limited literature that treats these relationships as symmetric. Keywords:asymmetric effects, macroeconomic variables, panel ARDL, panel NARDL, stock market index  
Ownership Structure as a Resilience Moderator: Phase-Dependent Asymmetric Drawdown Dynamics in Blue-Chip Versus Broad-Market Indices During a Geopolitical Crisis Muchammad Bachtiar; Fithriyyah Shalihati; Agustina Widi Palupiningrum; Anny Ratnawati
AI, Big Data and Quantitative Methods in Finance Vol. 1 No. 1 (2026): ABQ Vol. 1 No. 1, April 2026
Publisher : School of Business, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/abq.1.1.39

Abstract

Background: Geopolitical armed-conflict events generate severe multi-phase disruptions in emerging market equity markets. When concentrated foreign institutional ownership characterises the blue-chip segment, a structural outflow paradox may invert the classical flight-to-quality prediction, rendering fundamentally stronger equities the preferred vehicle for programmatic capital exit.Purpose: This study investigates whether concentrated foreign ownership in LQ45 generates a systematic flight-to-quality inversion across three phases of the 2026 Iran-Israel-US geopolitical crisis on the Indonesia Stock Exchange.Design/methodology/approach: Maximum Drawdown and Cumulative Return are calculated from daily closing prices (n = 24 trading days) for LQ45 and the Jakarta Composite Index across three crisis clusters: K1 Initial Panic, K2 Energy Crisis, and K3 Resolution and Recovery, employing multi-cluster phase analysis to reject the temporal homogeneity assumption of single-window event studies.Findings/Result: LQ45 underperforms IHSG on Cumulative Return across all clusters, with the differential widening from 0,89 to 2,27 percentage points. The K1-to-K2 Maximum Drawdown reversal confirms that foreign rebalancing concentrates selling pressure on the most liquid segment. The K3 asymmetry (IHSG: +2,02%; LQ45: -0,25%) reflects the advantage of a domestically anchored investor base in capturing de-escalation signals.Conclusion: These findings introduce the Phase-Dependent Asymmetric Resilience Model (PDARM) as a conditional framework for predicting flight-to-quality inversion in ownership-heterogeneous emerging markets, with implications for portfolio allocation and market stabilisation.Originality/value (State of the art): This study is the first intra-exchange, multi-phase characterisation of the 2026 Middle East crisis on the Indonesia Stock Exchange, showing that ownership-driven resilience asymmetry is measurable within a single exchange under identical shock conditions. Keywords:geopolitical risk, capital outflow, market resilience, emerging markets, Indonesia Stock Exchange  
Does Financial Development Widen or Reduce Income Inequality? Evidence From Developed and Developing Countries Trincy Nissi; Noer Azam Achsani; Heni Hasanah; Annisa Ramadanti
AI, Big Data and Quantitative Methods in Finance Vol. 1 No. 1 (2026): ABQ Vol. 1 No. 1, April 2026
Publisher : School of Business, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/abq.1.1.52

Abstract

Background: The financial sector has grown rapidly over the past two decades, yet income inequality remains an unresolved issue. This phenomenon raises important questions about the role of financial sector development in shaping inequality, especially given the mixed findings in existing literature depending on the proxies of financial development used.Purpose: This study aims to analyze the relationship between financial sector development and income inequality by comparing developed and developing countries, while incorporating different dimensions of financial development.Design/methodology/approach: The study uses panel data from 44 countries (both developed and developing) over the period 1980–2021. The financial sector is classified into financial institutions and financial markets, and further decomposed into three dimensions: depth, access, and efficiency. The analysis is conducted using a Fixed Effects Model (FEM) regression.Findings/Result: The results show that in developing countries, the relationship between financial development and inequality follows an inverted U-shaped pattern, where financial development initially increases inequality but eventually reduces it as financial access becomes more inclusive. In contrast, in developed countries, the relationship is positively linear, indicating that financial development tends to increase inequality due to the concentration of financial depth and access among wealthier groups.Conclusion: Financial sector development affects income inequality differently across levels of economic development. While it has the potential to reduce inequality in developing countries at later stages, it may exacerbate inequality in developed countries if financial benefits are not distributed more equitably.Originality/value (State of the art): This study contributes to the literature by providing a comparative analysis between developed and developing countries using a multidimensional approach to financial development (depth, access, and efficiency), offering deeper insights into how different aspects of the financial sector influence income inequality. Keywords:income inequality, panel data, financial development, developing countries, financial sector

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