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on Financial Markets |
| By: | Abraham Atsiwo |
| Abstract: | This study presents a three-step machine learning framework to predict bubbles in the S&P 500 stock market by combining financial news sentiment with macroeconomic indicators. Building on traditional econometric approaches, the proposed approach predicts bubble formation by integrating textual and quantitative data sources. In the first step, bubble periods in the S&P 500 index are identified using a right-tailed unit root test, a widely recognized real-time bubble detection method. The second step extracts sentiment features from large-scale financial news articles using natural language processing (NLP) techniques, which capture investors' expectations and behavioral patterns. In the final step, ensemble learning methods are applied to predict bubble occurrences based on high sentiment-based and macroeconomic predictors. Model performance is evaluated through k-fold cross-validation and compared against benchmark machine learning algorithms. Empirical results indicate that the proposed three-step ensemble approach significantly improves predictive accuracy and robustness, providing valuable early warning insights for investors, regulators, and policymakers in mitigating systemic financial risks. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16636 |
| By: | Qionghua Chu |
| Abstract: | I identify a new signaling channel in ESG research by empirically examining whether environmental, social, and governance (ESG) investing remains valuable as large institutional investors increasingly shift toward artificial intelligence (AI). Using winsorized ESG scores of S&P 500 firms from Yahoo Finance and controlling for market value of equity, I conduct cross-sectional regressions to test the signaling mechanism. I demonstrate that Environmental, Social, Governance, and composite ESG scores strongly and positively signal higher debt-to-total-capital ratio, both individually and in various combinations. My findings contribute to the growing literature on ESG investing, offering economically meaningful signaling channel with implications for long-term portfolio management amid the rise of AI. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15956 |
| By: | Jiaquan Nicholas Chen; Marcel Ausloos |
| Abstract: | There are many misconceptions around stock prices, stock splits, shareholders, investors, and managers behaviour about such informations due to a number of confounding factors. This paper tests hypotheses with a selected database, about the question ''is stock split attractive for companies?'' in another words, ''why companies split their stock?'', ''why managers split their stock?'', sometimes for no benefit, and ''why shareholders agree with such decisions?''. We contribute to the existing knowledge through a discussion of nine events in recent (selectively chosen) years, observing the role of information asymmetries, the returns and traded volumes before and after the event. Therefore, calculating the beta for each sample, it is found that stock splits (i) affect the market and slightly enhance the trading volume in a short-term, (ii) increase the shareholder base for its firm, (iii) have a positive effect on the liquidity of the market. We concur that stock split announcements can reduce the level of information asymmetric. Investors readjust their beliefs in the firm, although most of the firms are mispriced in the stock split year. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15879 |
| By: | Tamoghna Mukherjee |
| Abstract: | The stock market is heavily influenced by investor sentiment, which can drive buying or selling behavior. Sentiment analysis helps in gauging the overall sentiment of market participants towards a particular stock or the market as a whole. Positive sentiment often leads to increased buying activity and vice versa. Granger causality can be applied to ascertain whether changes in sentiment precede changes in stock prices.The study is focused on this aspect and tries to understand the relationship between close price index and sentiment score with the help of Granger causality inference. The study finds a positive response through hypothesis testing. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15915 |
| By: | Qing-Yu Lan; Zhan-He Wang; Jun-Qian Jiang; Yu-Tong Wang; Yun-Song Piao |
| Abstract: | The financial market is known to be highly sensitive to news. Therefore, effectively incorporating news data into quantitative trading remains an important challenge. Existing approaches typically rely on manually designed rules and/or handcrafted features. In this work, we directly use the news sentiment scores derived from large language models, together with raw price and volume data, as observable inputs for reinforcement learning. These inputs are processed by sequence models such as recurrent neural networks or Transformers to make end-to-end trading decisions. We conduct experiments using the cryptocurrency market as an example and evaluate two representative reinforcement learning algorithms, namely Double Deep Q-Network (DDQN) and Group Relative Policy Optimization (GRPO). The results demonstrate that our news-aware approach, which does not depend on handcrafted features or manually designed rules, can achieve performance superior to market benchmarks. We further highlight the critical role of time-series information in this process. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.19173 |
| By: | Yuntao Wu; Lynn Tao; Ing-Haw Cheng; Charles Martineau; Yoshio Nozawa; John Hull; Andreas Veneris |
| Abstract: | News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal transport, identifying semantically similar content across languages. We apply this method to align more than 140, 000 pairs of Bloomberg English and Japanese news articles covering around 3500 stocks in Tokyo exchange over 2012-2024. Aligned sentences are sparser, more interpretable, and exhibit higher semantic similarity. Return scores constructed from aligned sentences show stronger correlations with realized stock returns, and long-short trading strategies based on these alignments achieve 10\% higher Sharpe ratios than analyzing the full text sample. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.19203 |
| By: | Domenica Mino; Cillian Williamson |
| Abstract: | Artificial intelligence techniques have increasingly been applied to understand the complex relationship between public sentiment and financial market behaviour. This study explores the relationship between the sentiment of news related to the Russia-Ukraine war and the volatility of the stock market. A comprehensive dataset of news articles from major US platforms, published between January 1 and July 17, 2024, was analysed using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model adapted for financial language. We extracted sentiment scores and applied a Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, enhanced with a Student-t distribution to capture the heavy-tailed nature of financial returns data. The results reveal a statistically significant negative relationship between negative news sentiment and market stability, suggesting that pessimistic war coverage is associated with increased volatility in the S&P 500 index. This research demonstrates how artificial intelligence and natural language processing can be integrated with econometric modelling to assess real-time market dynamics, offering valuable tools for financial risk analysis during geopolitical crises. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16503 |
| By: | Torsten Ehlers; Mathias Hoffmann; Alexander Raabe |
| Abstract: | Non-US global banks are an important driver of the international synchronization of house price growth. A loosening (tightening) of US dollar funding conditions leads non-US global banks to expand (contract) their international lending, which is largely denominated in US dollars. This induces a synchronization of lending across borrowing countries, which translates into an international synchronization of house price growth. Borrowing country pairs whose joint exposure to US dollar funding conditions via their non-US creditor banks (dollar co-dependence) is higher, exhibit a higher synchronization of house price growth. Our results identify a novel international spillover channel of US dollar funding conditions, which is not related to common-lender exposures. We show theoretically and empirically that the exposure of non-US global banks to dollar funding conditions is captured by the bilateral treasury basis between the currency of the non-US global creditor banks' headquarters and the US dollar. As these conditions vary over time, borrowing country pairs whose non-US global creditor banks are more exposed to US dollar funding variations exhibit higher house price synchronization. |
| Keywords: | house prices, synchronization, US dollar funding, global US dollar cycle, US treasury basis, convenience yield, global imbalances, capital flows, global banking network |
| JEL: | F34 F36 G15 G21 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-56 |
| By: | Diana Barro (Ca’ Foscari University of Venice); Marco Corazza (Ca’ Foscari University of Venice); Gianni Filograsso (Ca’ Foscari University of Venice) |
| Abstract: | In this contribution, we discuss how to handle financial and sustainable investment goals, focusing on greenness and ESG features. Sustainable investing has attracted increasing interest with an associated growing commitment to take an active part in investment choices. Among thematic investments, green and energy-related ones have emerged, capturing investors' attention. Non-optimized strategies and traditional portfolio allocation models cannot guarantee the necessary flexibility. To answer this demand, ESG tailored-made allocations should be provided, with the aim of representing the preferences and commitments of investors adequately. This contribution introduces a novel ESG-focused tracking error model to optimize portfolio allocation. We consider two reference benchmarks, accounting for a financial target and an ESG one, respectively. The objective function results in a convex linear combination of the two goals where the parameter λ accounts for the investor's financial and ESG preferences. A symmetric tracking error measure is proposed to replicate the financial benchmark passively, while an asymmetric measure is used to track and possibly outperform the thematic ESG benchmark. Identifying the benchmarks for the two components represents a crucial step and, jointly with the choice of the parameter λ, accounts for the portfolio's overall risk-return and ESG profiles. In the model, the sustainability feature is handled not only with the presence of the ESG benchmark but also with the introduction of dedicated constraints. Namely, a desired minimum level of greenness and a maximum amount of carbon intensity can be accounted for. An application to the EUROSTOXX 600 equity market is presented and discussed for different choices of the parameter λ, representing different sustainability preferences and risk-return profiles. Furthermore, a discussion on the choice of the benchmarks is provided. |
| Keywords: | Tracking Error, Portfolio optimization, Green sustainability, ESG, GAN |
| JEL: | C53 C61 G11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:21 |
| By: | Lucas Eduardo Pereira Teles; Carlos M. S. Figueiredo |
| Abstract: | This article presents a comparative study of large language models (LLMs) in the task of sentiment analysis of financial market news. This work aims to analyze the performance difference of these models in this important natural language processing task within the context of finance. LLM models are compared with classical approaches, allowing for the quantification of the benefits of each tested model or approach. Results show that large language models outperform classical models in the vast majority of cases. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15929 |
| By: | Kefan Chen; Hussain Ahmad; Diksha Goel; Claudia Szabo |
| Abstract: | Large Language Models (LLMs) have recently gained popularity in stock trading for their ability to process multimodal financial data. However, most existing methods focus on single-stock trading and lack the capacity to reason over multiple candidates for portfolio construction. Moreover, they typically lack the flexibility to revise their strategies in response to market shifts, limiting their adaptability in real-world trading. To address these challenges, we propose 3S-Trader, a training-free framework that incorporates scoring, strategy, and selection modules for stock portfolio construction. The scoring module summarizes each stock's recent signals into a concise report covering multiple scoring dimensions, enabling efficient comparison across candidates. The strategy module analyzes historical strategies and overall market conditions to iteratively generate an optimized selection strategy. Based on this strategy, the selection module identifies and assembles a portfolio by choosing stocks with higher scores in relevant dimensions. We evaluate our framework across four distinct stock universes, including the Dow Jones Industrial Average (DJIA) constituents and three sector-specific stock sets. Compared with existing multi-LLM frameworks and time-series-based baselines, 3S-Trader achieves the highest accumulated return of 131.83% on DJIA constituents with a Sharpe ratio of 0.31 and Calmar ratio of 11.84, while also delivering consistently strong results across other sectors. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.17393 |
| By: | Sina Molavipour; Alireza M. Javid; Cassie Ye; Bj\"orn L\"ofdahl; Mikhail Nechaev |
| Abstract: | Robust yield curve estimation is crucial in fixed-income markets for accurate instrument pricing, effective risk management, and informed trading strategies. Traditional approaches, including the bootstrapping method and parametric Nelson-Siegel models, often struggle with overfitting or instability issues, especially when underlying bonds are sparse, bond prices are volatile, or contain hard-to-remove noise. In this paper, we propose a neural networkbased framework for robust yield curve estimation tailored to small mortgage bond markets. Our model estimates the yield curve independently for each day and introduces a new loss function to enforce smoothness and stability, addressing challenges associated with limited and noisy data. Empirical results on Swedish mortgage bonds demonstrate that our approach delivers more robust and stable yield curve estimates compared to existing methods such as Nelson-Siegel-Svensson (NSS) and Kernel-Ridge (KR). Furthermore, the framework allows for the integration of domain-specific constraints, such as alignment with risk-free benchmarks, enabling practitioners to balance the trade-off between smoothness and accuracy according to their needs. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.21347 |
| By: | Mr. Rabah Arezki; Patrick A. Imam; Mr. Kangni R Kpodar; Dao Le-Van |
| Abstract: | We study how sovereign bond markets respond to earthquakes in emerging markets, using data from 96 countries between 2012 and 2023. While earthquakes raise spreads on average, the effect depends critically on state capacity. In low-capacity countries, spreads rise sharply and persist; in high-capacity states, they remain stable or fall. These effects appear immediately, last several months, and are robust to multiple controls and placebo tests. Our findings suggest that markets interpret disasters not simply as economic shocks but as institutional stress tests, penalizing fragile states. Institutional quality, in this context, acts as disaster insurance. |
| Keywords: | Earthquakes; Sovereign Bond Spread; State Capacity |
| Date: | 2025–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/218 |
| By: | Peng Liu |
| Abstract: | Financial networks based on Pearson correlations have been intensively studied. However, previous studies may have led to misleading and catastrophic results because of several critical shortcomings of the Pearson correlation. The local Gaussian correlation coefficient, a new measurement of statistical dependence between variables, has unique advantages including capturing local nonlinear dependence and handling heavy-tailed distributions. This study constructs financial networks using the local Gaussian correlation coefficients between tail regions of stock returns in the Shanghai Stock Exchange. The work systematically analyzes fundamental network metrics including node centrality, average shortest path length, and entropy. Compared with the local Gaussian correlation network among positive tails and the conventional Pearson correlation network, the properties of the local Gaussian correlation network among negative tails are more sensitive to the stock market risks. This finding suggests researchers should prioritize the local Gaussian correlation network among negative tails. Future work should reevaluate existing findings using the local Gaussian correlation method. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.21165 |
| By: | Brian Godwin Lim; Dominic Dayta; Benedict Ryan Tiu; Renzo Roel Tan; Len Patrick Dominic Garces; Kazushi Ikeda |
| Abstract: | The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the econometrics literature to explore the dynamic factor model as an interpretable model with sufficient predictive capabilities for capturing essential market phenomena. Although the model has been extensively applied for predictive purposes, this study focuses on analyzing the extracted loadings and common factors as an alternative framework for understanding stock price dynamics. The results reveal novel insights into traditional market theories when applied to the Philippine Stock Exchange using the Kalman method and maximum likelihood estimation, with subsequent validation against the capital asset pricing model. Notably, a one-factor model extracts a common factor representing systematic or market dynamics similar to the composite index, whereas a two-factor model extracts common factors representing market trends and volatility. Furthermore, an application of the model for nowcasting the growth rates of the Philippine gross domestic product highlights the potential of the extracted common factors as viable real-time market indicators, yielding over a 34% decrease in the out-of-sample prediction error. Overall, the results underscore the value of dynamic factor analysis in gaining a deeper understanding of market price movement dynamics. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15938 |
| By: | Chujun He; Zhonghao Huang; Xiangguo Li; Ye Luo; Kewei Ma; Yuxuan Xiong; Xiaowei Zhang; Mingyang Zhao |
| Abstract: | We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four firm-level agents -- Fundamental, Technical, Report, and News -- that conduct in-depth analyses of individual firms to ensure both breadth and depth of coverage; (iii) a Portfolio agent that uses reinforcement learning to combine the agent outputs into a unified policy to generate the trading strategy; and (iv) a Risk Control agent that adjusts portfolio positions in response to market volatility. We evaluate the system on the constituents by the CSI 300 Index of China's A-share market and find that it consistently outperforms standard benchmarks and a state-of-the-art multi-agent trading system on risk-adjusted returns and drawdown control. Our core contribution is a hierarchical multi-agent design that links top-down macro screening with bottom-up fundamental analysis, offering a robust and extensible approach to factor-based portfolio construction. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.21147 |
| By: | Rajesh ADJ Jeyaprakash; Senthil Arasu Balasubramanian; Vijay Maddikera |
| Abstract: | Investment style groups investment approaches to predict portfolio return variations. This study examines the relationship between investment style, style consistency, and risk-adjusted returns of Indian equity mutual funds. The methodology involves estimating size and style beta coefficients, identifying breakpoints, analysing investment styles, and assessing risk-shifting intensity. Funds transition across styles over time, reflecting rotation, drift, or strengthening trends. Many Mid Blend funds remain in the same category, while others shift to Large Blend or Mid Value, indicating value-oriented strategies or large-cap exposure. Some funds adopt high-return styles like Small Value and Small Blend, aiming for alpha through small-cap equities. Performance changes following risk structure shifts are analyzed by comparing pre- and post-shift metrics, showing that style adjustments can enhance returns based on market conditions. This study contributes to mutual fund evaluation literature by highlighting the impact of style transitions on returns. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.19619 |