Causal Inference

Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets ArXiv ID: 2404.07225 “View on arXiv” Authors: Unknown Abstract This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund’s return to decrease by -11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors ...

Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk ArXiv ID: 2402.06032 “View on arXiv” Authors: Unknown Abstract Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR’s key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model’s superior resilience and invariant predictive power, essential for navigating the complexities of today’s ever-evolving financial markets. ...

Linear and nonlinear causality in financial markets ArXiv ID: 2312.16185 “View on arXiv” Authors: Unknown Abstract Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper we present a much more general framework for assessing co-dependencies by identifying and interpreting linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management. ...

INTAGS: Interactive Agent-Guided Simulation ArXiv ID: 2309.01784 “View on arXiv” Authors: Unknown Abstract In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production, to avoid unexpected losses in the real-world. Such a simulator acts as the environmental background (BG) agent(s), called agent-based simulator (ABS), aiming to replicate the complex real MAS. However, developing realistic ABS remains challenging, mainly due to the sequential and dynamic nature of such systems. To fill this gap, we propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents to explicitly account for the systems’ sequential nature. Specifically, we characterize the system/environment by studying the effect of a sequence of BG agents’ responses to the environment state evolution and take such effects’ differences as MAS distance metric; The effect estimation is cast as a causal inference problem since the environment evolution is confounded with the previous environment state. Importantly, we propose the Interactive Agent-Guided Simulation (INTAGS) framework to build a realistic ABS by optimizing over this novel metric. To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning. Moreover, INTAGS utilizes the policy gradient update to bypass differentiating the proposed metric such that it can support non-differentiable operations of multi-agent environments. Through extensive experiments, we demonstrate the effectiveness of INTAGS on an equity stock market simulation example. We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach. ...

Causal Inference for Banking Finance and Insurance A Survey ArXiv ID: 2307.16427 “View on arXiv” Authors: Unknown Abstract Causal Inference plays an significant role in explaining the decisions taken by statistical models and artificial intelligence models. Of late, this field started attracting the attention of researchers and practitioners alike. This paper presents a comprehensive survey of 37 papers published during 1992-2023 and concerning the application of causal inference to banking, finance, and insurance. The papers are categorized according to the following families of domains: (i) Banking, (ii) Finance and its subdomains such as corporate finance, governance finance including financial risk and financial policy, financial economics, and Behavioral finance, and (iii) Insurance. Further, the paper covers the primary ingredients of causal inference namely, statistical methods such as Bayesian Causal Network, Granger Causality and jargon used thereof such as counterfactuals. The review also recommends some important directions for future research. In conclusion, we observed that the application of causal inference in the banking and insurance sectors is still in its infancy, and thus more research is possible to turn it into a viable method. ...

Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships ArXiv ID: 2306.08157 “View on arXiv” Authors: Unknown Abstract Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. Six popular cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether are studied in this work. The proposed model’s performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, and support vector machines. The results show that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models. ...

How Much Should We Trust Staggered Difference-In-Differences Estimates? ArXiv ID: ssrn-3794018 “View on arXiv” Authors: Unknown Abstract We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These bi Keywords: Difference-in-Differences (DiD), Policy Evaluation, Econometric Bias, Causal Inference, Staggered Adoption, Multi-Asset (Quantitative Research) Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper involves advanced econometric theory on staggered difference-in-differences and discusses complex estimator derivations, but it is primarily a theoretical/methodological critique without original backtesting or heavy data implementation. flowchart TD A["Research Question:<br>How much should we trust staggered<br>DID estimates?"] --> B["Methodology: Simulation & Analytical Framework"] B --> C{"Data / Inputs"} C --> C1["Multi-Asset Dataset"] C --> C2["Policy Adoption<br>Staggered Design"] C --> C3["Treatment Effects<br>(Heterogeneity)"] C --> C4["Distributional Assumptions"] C1 & C2 & C3 & C4 --> D["Computational Process:<br>Estimation of Staggered DID"] D --> D1["Standard TWFE Estimator"] D --> D2["New (Robust) Estimators"] D1 --> E{"Analysis"} D2 --> E E --> F["Key Findings / Outcomes"] F --> F1["Bias Detection:<br>Standard TWFE often biased"] F --> F2["Solution:<br>Use robust estimators<br>e.g., Callaway & Sant'Anna"] F --> F3["Conclusion:<br>Trust estimates only after<br>robustness checks"]

Does Corporate Governance Predict Firms’ Market Values? Evidence from Korea ArXiv ID: ssrn-1098690 “View on arXiv” Authors: Unknown Abstract We report strong OLS and instrumental variable evidence that an overall corporate governance index is an important and likely causal factor in explaining the ma Keywords: corporate governance index, OLS regression, instrumental variable, causal inference, firm value, Equities (Corporate Governance) Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper relies on standard OLS and IV econometric models (moderate math) and emphasizes causal identification using Korean governance data, indicating strong empirical testing. It is data-intensive but does not involve advanced mathematical derivations. flowchart TD A["Research Goal:<br>Does Corporate Governance<br>Predict Firm Value?"] --> B["Data Sources"] B --> C["Key Methodologies"] subgraph B ["Data/Inputs"] B1["Korean Firm Data"] B2["Corporate Governance Index"] B3["Market Value Metrics"] end subgraph C ["Methodology"] C1["OLS Regression"] C2["Instrumental Variable<br>Estimation"] end C --> D["Computational Process:<br>Causal Inference Analysis"] D --> E["Key Findings"] subgraph E ["Outcomes"] E1["Strong OLS Evidence"] E2["Instrumental Variable<br>Validation"] E3["Governance Index<br>Significantly Predicts<br>Firm Value"] end