|
on Financial Markets |
| By: | Jos\'e \'Angel Islas Anguiano; Andr\'es Garc\'ia-Medina |
| Abstract: | In this note, we compare Bitcoin trading performance using two machine learning models-Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM)-and two technical analysis-based strategies: Exponential Moving Average (EMA) crossover and a combination of Moving Average Convergence/Divergence with the Average Directional Index (MACD+ADX). The objective is to evaluate how trading signals can be used to maximize profits in the Bitcoin market. This comparison was motivated by the U.S. Securities and Exchange Commission's (SEC) approval of the first spot Bitcoin exchange-traded funds (ETFs) on 2024-01-10. Our results show that the LSTM model achieved a cumulative return of approximately 65.23% in under a year, significantly outperforming LightGBM, the EMA and MACD+ADX strategies, as well as the baseline buy-and-hold. This study highlights the potential for deeper integration of machine learning and technical analysis in the rapidly evolving cryptocurrency landscape. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00665 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | This paper studies the role of banking supervision in anticipating, monitoring, and disciplining failing banks. We document that supervisors anticipate most bank failures with a high degree of accuracy. Supervisors play an important role in requiring troubled banks to recognize losses, taking enforcement actions, and ultimately closing failing banks. To establish causality, we exploit exogenous variation in supervisory strictness during the Global Financial Crisis. Stricter supervision leads to more loss recognition, reduced dividend payouts, and an increase in the likelihood and speed of closure. Increased strictness entails a trade-off between a lower resolution cost to the FDIC and reduced credit. |
| Keywords: | financial institutions and regulation |
| JEL: | G01 N20 N24 G28 K23 E44 |
| Date: | 2025–10–15 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:102048 |
| By: | Yuji Sakurai |
| Abstract: | This paper presents a comprehensive framework for determining haircuts on collateral used in central bank operations, quantifying residual uncollateralized exposures, and validating haircut models using machine learning. First, it introduces four haircut model types tailored to asset characteristics—marketable or non-marketable—and data availability. It proposes a novel model for setting haircuts in data-limited environment using a satallite cross-country model. Key principles guiding haircut calibration include non-procyclicality, data-drivenness, conservatism, and the avoidance of arbitrage gaps. The paper details model inputs such as Value-at-Risk (VaR) percentiles, volatility measures, and time to liquidation. Second, it proposes a quantitative framework for estimating expected uncollateralized exposures that remain after haircut application, emphasizing their importance in stress scenarios. Illustrative simulations using dynamic Nelson-Siegel yield curve models demonstrate how volatility impacts exposure. Third, the paper explores the use of Variational Autoencoders (VAEs) to simulate stress scenarios for bond yields. Trained on U.S. Treasury data, VAEs capture realistic yield curve distributions, offering an altenative tool for validating VaR-based haircuts. Although interpretability and explainability remain concerns, machine learning models enhance risk assessment by uncovering potential model vulnerabilities. |
| Keywords: | Haircuts; Uncollateralized Exposure; Machine Learning |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/225 |
| By: | E. Benhamou; JJ. Ohana; B. Guez; E. Setrouk; T. Jacquot |
| Abstract: | In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. Despite historically delivering robust returns, private equity's inherent illiquidity and lack of transparency raise significant concerns regarding investor trust and systemic stability, particularly in periods of heightened market volatility. Our method uses advanced graphical models to decode liquid PE proxies and incorporates asymmetric risk adjustments that emulate private equity's unique performance dynamics. The result is a liquid, scalable solution that aligns closely with traditional quarterly PE benchmarks like Cambridge Associates and Preqin. This approach enhances portfolio resilience and contributes to the ongoing discourse on safe asset innovation, supporting market stability and investor confidence. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.23201 |
| By: | Francesco Bianchi; Do Q. Lee; Sydney C. Ludvigson; Sai Ma |
| Abstract: | How rational is the stock market and how efficiently does it process information? We use machine learning to establish a practical measure of rational and efficient expectation formation while identifying distortions and inefficiencies in the subjective beliefs of market participants. The algorithm independently learns, stays attentive to fundamentals, credit risk, and sentiment, and makes abrupt course-corrections at critical junctures. By contrast, the subjective beliefs of investors, professionals, and equity analysts do little of this and instead contain predictable mistakes–prestakes–that are especially prevalent in times of market turbulence. Trading schemes that bet against prestakes deliver defensive strategies with large CAPM and Fama-French 5-factor alphas. |
| JEL: | G1 G17 G40 G41 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34420 |
| By: | Aryan Ranjan |
| Abstract: | We present a systematic trading framework that forecasts short-horizon market risk, identifies its underlying drivers, and generates alpha using a hybrid machine learning ensemble built to trade on the resulting signal. The framework integrates neural networks with tree-based voting models to predict five-day drawdowns in the S&P 500 ETF, leveraging a cross-asset feature set spanning equities, fixed income, foreign exchange, commodities, and volatility markets. Interpretable feature attribution methods reveal the key macroeconomic and microstructural factors that differentiate high-risk (crash) from benign (non-crash) weekly regimes. Empirical results show a Sharpe ratio of 2.51 and an annualized CAPM alpha of +0.28, with a market beta of 0.51, indicating that the model delivers substantial systematic alpha with limited directional exposure during the 2005--2025 backtest period. Overall, the findings underscore the effectiveness of hybrid ensemble architectures in capturing nonlinear risk dynamics and identifying interpretable, potentially causal drivers, providing a robust blueprint for machine learning-driven alpha generation in systematic trading. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22348 |
| By: | Nikolas Anic; Andrea Barbon; Ralf Seiz; Carlo Zarattini |
| Abstract: | This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard long-only momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model's pre-training cut-off. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.26228 |
| By: | Juan Cortina; Claudio Raddatz; Sergio Schmukler; Tomas Williams |
| Abstract: | This paper investigates how firms use green versus conventional debt and the associated firm- and aggregate-level environmental consequences. Employing a dataset of 127, 711 global bond and syndicated loan issuances by non-financial firms across 85 countries during 2012-23, the paper documents a sharp rise in green debt issuances relative to conventional issuances since 2018. This increase is particularly pronounced among large firms with high carbon dioxide emissions. Local projections difference-in-differences estimates show that, compared to conventional debt, green bond and loan issuances are systematically followed by sustained reductions in carbon intensity (emissions over income) of up to 50 percent. These reductions correspond to as much as 15 percent of global annual emissions. Green bonds contribute to reducing emissions by providing financing to large, high-emitting firms, whose improvements in carbon intensity have significant aggregate consequences. Syndicated loans do so by channeling a larger volume of financing to a wider set of firms. |
| Keywords: | carbon emissions; corporate bonds; firm growth; green debt; green transition; sustainability; syndicated loans. |
| JEL: | F33 G00 G01 G15 G21 G23 G31 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2025-012 |
| By: | Yudi Yang; Fan Yang; Xiajie Yi; Dongwei He |
| Abstract: | This paper investigates the impact of financial technology (FinTech) on the financial sustainability (FS) of commercial banks. We employ a three-stage network DEA-Malmquist model to evaluate the FS performance of 104 Chinese commercial banks from 2015 to 2023. A two-way fixed effects model is utilized to examine the effects of FinTech on FS, revealing a significant negative relationship. Further mechanistic analysis indicates that FinTech primarily undermines FS by eroding banks' loan efficiency and profitability. Notably, banks with more patents or listed status demonstrate greater resilience to FinTech disruptions. These findings help banks identify external risks stemming from FinTech development, and by elucidating the mechanisms underlying FS, enhance their capacity to monitor and manage FS in the era of rapid FinTech advancement. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02608 |