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on Financial Markets |
| By: | Keon Vin Park |
| Abstract: | Optimizing portfolio performance is a fundamental challenge in financial modeling, requiring the integration of advanced clustering techniques and data-driven optimization strategies. This paper introduces a comparative backtesting approach that combines clustering-based portfolio segmentation and Sharpe ratio-based optimization to enhance investment decision-making. First, we segment a diverse set of financial assets into clusters based on their historical log-returns using K-Means clustering. This segmentation enables the grouping of assets with similar return characteristics, facilitating targeted portfolio construction. Next, for each cluster, we apply a Sharpe ratio-based optimization model to derive optimal weights that maximize risk-adjusted returns. Unlike traditional mean-variance optimization, this approach directly incorporates the trade-off between returns and volatility, resulting in a more balanced allocation of resources within each cluster. The proposed framework is evaluated through a backtesting study using historical data spanning multiple asset classes. Optimized portfolios for each cluster are constructed and their cumulative returns are compared over time against a traditional equal-weighted benchmark portfolio. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.12074 |
| By: | Konstantinos-Leonidas Bisdoulis |
| Abstract: | Fluctuations in the stock market rapidly shape the economic world and consumer markets, impacting millions of individuals. Hence, accurately forecasting it is essential for mitigating risks, including those associated with inactivity. Although research shows that hybrid models of Deep Learning (DL) and Machine Learning (ML) yield promising results, their computational requirements often exceed the capabilities of average personal computers, rendering them inaccessible to many. In order to address this challenge in this paper we optimize LightGBM (an efficient implementation of gradient-boosted decision trees (GBDT)) for maximum performance, while maintaining low computational requirements. We introduce novel feature engineering techniques including indicator-price slope ratios and differences of close and open prices divided by the corresponding 14-period Exponential Moving Average (EMA), designed to capture market dynamics and enhance predictive accuracy. Additionally, we test seven different feature and target variable transformation methods, including returns, logarithmic returns, EMA ratios and their standardized counterparts as well as EMA difference ratios, so as to identify the most effective ones weighing in both efficiency and accuracy. The results demonstrate Log Returns, Returns and EMA Difference Ratio constitute the best target variable transformation methods, with EMA ratios having a lower percentage of correct directional forecasts, and standardized versions of target variable transformations requiring significantly more training time. Moreover, the introduced features demonstrate high feature importance in predictive performance across all target variable transformation methods. This study highlights an accessible, computationally efficient approach to stock market forecasting using LightGBM, making advanced forecasting techniques more widely attainable. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.07580 |
| By: | António Afonso; José Alves; Wojciech Grabowski; Sofia Monteiro |
| Abstract: | We employ a cross-quantilogram approach to assess relationships between quantiles of stock returns and sovereign yields, in the U.S. and Germany, in the period 1990-2024. Specifically, we focus on the lowest 5% quantile of stock returns and the highest 5% quantile of bond returns, providing insights into tail dependencies, crucial during market downturns and periods of heightened volatility. We also measure causality in volatilities extending well-known approaches analyzing volatility transmission. We find significant cross-market relationships between U.S. and German stock and bond markets, influenced by economic crises, macroeconomic dynamics, and monetary policy interventions, and financial stress play a crucial role. |
| Keywords: | stock returns; sovereign bond returns; stock-bond relationship; crossquantilogram; volatility transmission; US; Germany; monetary policy shocks; fiscal stance. |
| JEL: | C32 F21 F37 F42 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03662025 |
| By: | Jesús Villota (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | Markets do not always efficiently incorporate news, particularly when information is complex or ambiguous. Traditional text analysis methods fail to capture the economic structure of information and its firm-specific implications. We propose a novel methodology that guides LLMs to systematically identify and classify firm-specific economic shocks in news articles according to their type, magnitude, and direction. This economically-informed classification allows for a more nuanced understanding of how markets process complex information. Using a simple trading strategy, we demonstrate that our LLM-based classification significantly outperforms a benchmark based on clustering vector embeddings, generating consistent profits out-of-sample while maintaining transparent and durable trading signals. The results suggest that LLMs, when properly guided by economic frameworks, can effectively identify persistent patterns in how markets react to different types of firm-specific news. Our findings contribute to understanding market efficiency and information processing, while offering a promising new tool for analyzing financial narratives. |
| Keywords: | Large language models, business news, stock market reaction, market efficiency. |
| JEL: | G12 G14 C45 C58 C63 D83 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:cmf:wpaper:wp2025_2501 |
| By: | Yuxi Hong |
| Abstract: | Accurate stock market prediction provides great opportunities for informed decision-making, yet existing methods struggle with financial data's non-linear, high-dimensional, and volatile characteristics. Advanced predictive models are needed to effectively address these complexities. This paper proposes a novel multi-layer hybrid multi-task learning (MTL) framework aimed at achieving more efficient stock market predictions. It involves a Transformer encoder to extract complex correspondences between various input features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture long-term temporal relationships, and a Kolmogorov-Arnold Network (KAN) to enhance the learning process. Experimental evaluations indicate that the proposed learning structure achieves great performance, with an MAE as low as 1.078, a MAPE as low as 0.012, and an R^2 as high as 0.98, when compared with other competitive networks. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.09760 |
| By: | Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Boris Kuznetsov (Swiss Finance Institute); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Teng Andrea Xu (École Polytechnique Fédérale de Lausanne (EPFL)) |
| Abstract: | The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and nonlinearity. We also develop a linear transformer that serves as a simplified surrogate from which we derive an intuitive decomposition of the transformer's asset pricing mechanisms. We find large reductions in pricing errors from our artificial intelligence pricing model (AIPM) relative to previous machine learning models and dissect the sources of these gains. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2508 |
| By: | Di Wu |
| Abstract: | Bitcoin, widely recognized as the first cryptocurrency, has shown increasing integration with traditional financial markets, particularly major U.S. equity indices, amid accelerating institutional adoption. This study examines how Bitcoin exchange-traded funds and corporate Bitcoin holdings affect correlations with the Nasdaq 100 and the S&P 500, using rolling-window correlation, static correlation coefficients, and an event-study framework on daily data from 2018 to 2025.Correlation levels intensified following key institutional milestones, with peaks reaching 0.87 in 2024, and they vary across market regimes. These trends suggest that Bitcoin has transitioned from an alternative asset toward a more integrated financial instrument, carrying implications for portfolio diversification, risk management, and systemic stability. Future research should further investigate regulatory and macroeconomic factors shaping these evolving relationships. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.09911 |
| By: | Aaron J. Black (University of St. Gallen; Swiss Finance Institute); Julian F Kölbel (University of St. Gallen - School of Finance; MIT Sloan; Swiss Finance Institute) |
| Abstract: | This paper documents that ESG funds in the U.S. charge net expense ratios that are 9.5 to 12.7 basis points lower than those of non-ESG funds. This contrasts with the existing literature on investors' willingness to pay for ESG. The fee difference is driven by the use of waivers, which offset the higher gross expense ratios of ESG funds. We explore three explanations consistent with these findings: (1) heightened competition among ESG funds exerts downward pressure on fees, (2) ESG funds exhibit lower expected returns, and (3) fund families strategically use ESG funds with low fees to cross-sell higher-fee funds. |
| Keywords: | ESG, Mutual Funds, Competition |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp24109 |