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on Financial Markets |
| By: | Agrawal, Sonakshi; Liu, Lisa Yao; Rajgopal, Shivaram; Sridharan, Suhas A.; Yan, Yifan; Yohn, Teri Lombardi |
| Abstract: | We explore the role of ESG raters' business models in the production of their ratings, noting that increasingly ESG raters not only produce ESG ratings but also construct and sell index products based on their ESG ratings. We examine whether deriving revenue from ESG rating-based indices is associated with inflated ESG ratings for firms with higher stock returns. Consistent with this notion, we find that raters with strong index licensing incentives issue higher ESG ratings for firms with better stock return performance and those added to their ESG indices, compared to raters with weaker licensing incentives. By comparing ESG ratings for a firm across raters with high versus low index licensing incentives, we control for the firm's fundamental ESG performance. We find that the results hold after accounting for different rating methodologies. Overall, our findings suggest that ESG ratings are associated with index construction incentives, highlighting the need for greater transparency in the incentives of producers of ESG ratings. |
| Keywords: | ESG, index providers, rating agencies, sustainability, disclosure |
| JEL: | G24 M14 M40 M41 M48 Q56 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cbscwp:324653 |
| By: | Bingyang Wang; Grant Johnson; Maria Hybinette; Tucker Balch |
| Abstract: | This paper investigates whether artificial intelligence can enhance stock clustering compared to traditional methods. We consider this in the context of the semi-strong Efficient Markets Hypothesis (EMH), which posits that prices fully reflect all public information and, accordingly, that clusters based on price information cannot be improved upon. We benchmark three clustering approaches: (i) price-based clusters derived from historical return correlations, (ii) human-informed clusters defined by the Global Industry Classification Standard (GICS), and (iii) AI-driven clusters constructed from large language model (LLM) embeddings of stock-related news headlines. At each date, each method provides a classification in which each stock is assigned to a cluster. To evaluate a clustering, we transform it into a synthetic factor model following the Arbitrage Pricing Theory (APT) framework. This enables consistent evaluation of predictive performance in a roll forward, out-of-sample test. Using S&P 500 constituents from from 2022 through 2024, we find that price-based clustering consistently outperforms both rule-based and AI-based methods, reducing root mean squared error (RMSE) by 15.9% relative to GICS and 14.7% relative to LLM embeddings. Our contributions are threefold: (i) a generalizable methodology that converts any equity grouping: manual, machine, or market-driven, into a real-time factor model for evaluation; (ii) the first direct comparison of price-based, human rule-based, and AI-based clustering under identical conditions; and (iii) empirical evidence reinforcing that short-horizon return information is largely contained in prices. These results support the EMH while offering practitioners a practical diagnostic for monitoring evolving sector structures and provide academics a framework for testing alternative hypotheses about how quickly markets absorb information. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01590 |
| By: | Haojie Liu; Zihan Lin; Randall R. Rojas |
| Abstract: | This study integrates real-time sentiment analysis from financial news, GPT-2 and FinBERT, with technical indicators and time-series models like ARIMA and ETS to optimize S&P 500 trading strategies. By merging sentiment data with momentum and trend-based metrics, including a benchmark buy-and-hold and sentiment-based approach, is evaluated through assets values and returns. Results show that combining sentiment-driven insights with traditional models improves trading performance, offering a more dynamic approach to stock trading that adapts to market changes in volatile environments. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09739 |
| By: | Marianne Andries; Maxime Bonelli; David Sraer |
| Abstract: | We study an intervention by a brokerage firm providing advisory services to high-net-worth investors. In 2018, the firm changed the information displayed on its internal platform, so that financial advisors could no longer observe which clients’ holdings were in paper gain or loss. Using data on portfolio stock transactions between 2016 and 2021, we show that, while all investors exhibit a significant disposition effect before 2018, i.e., a greater propensity to realize gains than losses, highly-advised investors see their bias significantly reduced afterward. Our paper shows that by appropriately manipulating advisors’ information, financial firms can successfully reduce their clients’ biases. |
| JEL: | G0 G02 G2 G20 G4 G41 G5 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34130 |
| By: | Valentin Haddad; Tyler Muir |
| Abstract: | Intermediary asset pricing posits that financial institutions are important players in financial markets, and that their decisions shape asset prices beyond simply reflecting the preferences of the average household in the economy. We explain how the intermediary-asset pricing approach helps make sense of empirical patterns in the data: the excess volatility of asset prices, differences in price movements across asset classes, the cross section of expected returns within asset classes, and specific arbitrages and price dislocations. We also review how this view of price fluctuations has important implications for macroeconomic dynamics, international economics, and policy. In particular, the role of financial regulation and monetary policy in alleviating constraints or removing risk from intermediary balance sheets during periods of stress is central in this approach. We highlight both existing progress and gaps for future research. |
| JEL: | E44 G0 G01 G1 G2 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34146 |
| By: | Wenhao Li; Yiming Ma; Yang Zhao |
| Abstract: | We demonstrate the passthrough of Treasury supply to bank deposits through bank market power. We show that a larger Treasury supply crowds out deposits with disproportionate effects in more competitive deposit markets. A larger Treasury supply further curtails bank lending and affects bank funding structure. The explanatory power of Treasury supply is not driven by other shocks to deposit demand and supply. In comparison, monetary policy rate hikes have a larger impact on deposit funding in more concentrated markets, consistent with the deposits channel of monetary policy. Our empirical findings are rationalized in a model of imperfect deposit competition. |
| JEL: | E50 G21 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34154 |
| By: | Carlo Nicolini; Matteo Manzi; Hugo Delatte |
| Abstract: | Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python library for portfolio construction and risk management that seamlessly integrates with the scikit-learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean-variance optimization to modern clustering-based methods, state-of-the-art financial estimators with native interfaces, and advanced cross-validation techniques tailored for financial time series. By adhering to scikit-learn's fit-predict-transform paradigm, the library enables researchers and practitioners to leverage machine learning workflows for portfolio optimization, promoting reproducibility and transparency in quantitative finance. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04176 |
| By: | Kuntal K. Das (University of Canterbury); Mona Yaghoubi (University of Canterbury) |
| Abstract: | This paper examines the impact of firm-specific climate change sentiment on stock liquidity using a novel dataset derived from earnings call transcripts of U.S. publicly listed firms. We find that negative sentiment related to regulatory transition risks significantly impairs stock liquidity, while sentiment related to physical risks or green opportunities has limited effects. The impact of negative sentiment is amplified in firms with higher information asymmetry and greater regulatory oversight, such as high litigation risk or substantial government funding. These findings highlight the asymmetric nature of market responses to climate risks and underscore the critical role of institutional context and informational frictions in shaping financial market reactions to climate-related developments. |
| Keywords: | Climate change sentiment, Stock liquidity, Asymmetric information, Regulatory environment |
| JEL: | G10 G40 Q54 Q58 |
| Date: | 2025–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:cbt:econwp:25/12 |
| By: | Shanyan Lai |
| Abstract: | This study investigates the pretrained RNN attention models with the mainstream attention mechanisms such as additive attention, Luong's three attentions, global self-attention (Self-att) and sliding window sparse attention (Sparse-att) for the empirical asset pricing research on top 420 large-cap US stocks. This is the first paper on the large-scale state-of-the-art (SOTA) attention mechanisms applied in the asset pricing context. They overcome the limitations of the traditional machine learning (ML) based asset pricing, such as mis-capturing the temporal dependency and short memory. Moreover, the enforced causal masks in the attention mechanisms address the future data leaking issue ignored by the more advanced attention-based models, such as the classic Transformer. The proposed attention models also consider the temporal sparsity characteristic of asset pricing data and mitigate potential overfitting issues by deploying the simplified model structures. This provides some insights for future empirical economic research. All models are examined in three periods, which cover pre-COVID-19 (mild uptrend), COVID-19 (steep uptrend with a large drawdown) and one year post-COVID-19 (sideways movement with high fluctuations), for testing the stability of these models under extreme market conditions. The study finds that in value-weighted portfolio back testing, Model Self-att and Model Sparse-att exhibit great capabilities in deriving the absolute returns and hedging downside risks, while they achieve an annualized Sortino ratio of 2.0 and 1.80 respectively in the period with COVID-19. And Model Sparse-att performs more stably than Model Self-att from the perspective of absolute portfolio returns with respect to the size of stocks' market capitalization. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.19006 |
| By: | Qizhao Chen; Hiroaki Kawashima |
| Abstract: | This paper proposes a reinforcement learning framework that employs Proximal Policy Optimization (PPO) to dynamically optimize the weights of multiple large language model (LLM)-generated formulaic alphas for stock trading strategies. Formulaic alphas are mathematically defined trading signals derived from price, volume, sentiment, and other data. Although recent studies have shown that LLMs can generate diverse and effective alphas, a critical challenge lies in how to adaptively integrate them under varying market conditions. To address this gap, we leverage the deepseek-r1-distill-llama-70b model to generate fifty alphas for five major stocks: Apple, HSBC, Pepsi, Toyota, and Tencent, and then use PPO to adjust their weights in real time. Experimental results demonstrate that the PPO-optimized strategy achieves strong returns and high Sharpe ratios across most stocks, outperforming both an equal-weighted alpha portfolio and traditional benchmarks such as the Nikkei 225, S&P 500, and Hang Seng Index. The findings highlight the importance of reinforcement learning in the allocation of alpha weights and show the potential of combining LLM-generated signals with adaptive optimization for robust financial forecasting and trading. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01393 |
| By: | Yufei Sun (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | This paper investigates the profitability and robustness of pairs trading strategies based on non-parametric technical chart constructions—Renko and Kagi—across the U.S. and Chinese equity markets. Within a market-neutral, mean-reversion framework, the study examines strategy performance under varying market regimes, including the Global Financial Crisis (GFC) and the COVID-19 period. Using historical data from indices such as the S&P 500 and the CSI series, pairs are selected via statistical patterns in Renko and Kagi charts. Robustness checks consider varying trading horizons, the number of pairs, and transaction costs. Results show that both chart-based strategies generate significant excess returns and exhibit strong Sharpe ratios before costs. While trading frictions reduce profitability, Renko-based strategies remain resilient, especially during crises. The findings highlight that adaptive and non-parametric charting methods can effectively capture transient mispricings and provide viable alternatives for statistical arbitrage in turbulent markets. |
| Keywords: | Pairs trading; Quantitative strategies; Statistical arbitrage; Kagi Chart; Renko Chart; H-Strategy |
| JEL: | C22 C63 G11 G14 G17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-20 |
| By: | Gonzalo Ramirez-Carrillo; David Ortiz-Mora; Alex Aguilar-Larrotta |
| Abstract: | This study applies the Hierarchical Risk Parity (HRP) portfolio allocation methodology to the NUAM market, a regional holding that integrates the markets of Chile, Colombia and Peru. As one of the first empirical analyses of HRP in this newly formed Latin American context, the paper addresses a gap in the literature on portfolio construction under cross-border, emerging market conditions. HRP leverages hierarchical clustering and recursive bisection to allocate risk in a manner that is both interpretable and robust--avoiding the need to invert the covariance matrix, a common limitation in the traditional mean-variance optimization. Using daily data from 54 constituent stocks of the MSCI NUAM Index from 2019 to 2025, we compare the performance of HRP against two standard benchmarks: an equally weighted portfolio (1/N) and a maximum Sharpe ratio portfolio. Results show that while the Max Sharpe portfolio yields the highest return, the HRP portfolio delivers a smoother risk-return profile, with lower drawdowns and tracking error. These findings highlight HRP's potential as a practical and resilient asset allocation framework for investors operating in the integrated, high-volatility markets like NUAM. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.03712 |