|
on Financial Markets |
By: | Paige Ehresmann; Juan M. Morelli; Jessie Jiaxu Wang |
Abstract: | In this note, we introduce a factor asset pricing model to analyze risk-adjusted returns on bank stocks. Given their high-frequency availability, bank stock returns offer a valuable lens into the risk exposures and dynamics of the banking sector. |
Date: | 2025–06–06 |
URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:2025-06-06-3 |
By: | Leonardo Baggiani; Martin Herdegen; Leandro S\'anchez-Betancourt |
Abstract: | Automated Market Makers (AMMs) are emerging as a popular decentralised trading platform. In this work, we determine the optimal dynamic fees in a constant function market maker. We find approximate closed-form solutions to the control problem and study the optimal fee structure. We find that there are two distinct fee regimes: one in which the AMM imposes higher fees to deter arbitrageurs, and another where fees are lowered to increase volatility and attract noise traders. Our results also show that dynamic fees that are linear in inventory and are sensitive to changes in the external price are a good approximation of the optimal fee structure and thus constitute suitable candidates when designing fees for AMMs. |
Date: | 2025–06 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.02869 |
By: | Junzhe Jiang; Chang Yang; Xinrun Wang; Bo Li |
Abstract: | Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying interdependencies. To address these limitations, we propose Cubic, a novel end-to-end framework that explicitly models the adaptive fusion of constituent stocks for index price prediction. Our main contributions are threefold. i) Fusion in the latent space: we introduce the fusion mechanism over the latent embedding of the stocks to extract the information from the vast number of stocks. ii) Binary encoding classification: since regression tasks are challenging due to continuous value estimation, we reformulate the regression into the classification task, where the target value is converted to binary and we optimize the prediction of the value of each digit with cross-entropy loss. iii) Confidence-guided prediction and trading: we introduce the regularization loss to address market prediction uncertainty for the index prediction and design the rule-based trading policies based on the confidence. Extensive experiments across multiple stock markets and indices demonstrate that Cubic consistently outperforms state-of-the-art baselines in stock index prediction tasks, achieving superior performance on both forecasting accuracy metrics and downstream trading profitability. |
Date: | 2025–05 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.03153 |
By: | J\k{e}drzej Maskiewicz; Pawe{\l} Sakowski |
Abstract: | The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns. |
Date: | 2025–06 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.04658 |
By: | Matt Brigida |
Abstract: | A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated. |
Date: | 2025–06 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.03287 |
By: | Juan Plazuelo Pascual; Carlos Tardon Rubio; Juan Toro Cebada; Angel Hernando Veciana |
Abstract: | This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book from a liquidity pool in a decentralized setting for comparison with centralized exchanges. Chapter 2 describes the methodological tools used: Hasbrouck's Information Share, Gonzalo and Granger's Permanent-Transitory decomposition, and the Hayashi-Yoshida estimator. These are applied to explore lead-lag dynamics, cointegration, and price discovery across market types. Chapter 3 presents the empirical analysis. For ETH, it compares price dynamics on Binance and Uniswap v2 over a one-year period, focusing on five key events in 2024. For BTC, it analyzes the relationship between spot and futures prices on the CME. The study estimates lead-lag effects and cointegration in both cases. Results show that centralized markets typically lead in ETH price discovery. In futures markets, while they tend to lead overall, high-volatility periods produce mixed outcomes. The findings have key implications for traders and institutions regarding liquidity, arbitrage, and market efficiency. Various metrics are used to benchmark the performance of modified AMMs and to understand the interaction between decentralized and centralized structures. |
Date: | 2025–06 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.08718 |
By: | Roberto Gomez Cram; Howard Kung; Hanno Lustig; David Zeke |
Abstract: | Unfunded fiscal shocks are a significant source of risk premia in Treasury markets when central banks and governments decide to insulate taxpayers and expose bondholders' wealth to government funding needs. We illustrate this bond risk premium mechanism analytically in a two-agent model featuring monetary-fiscal interactions and a fraction of constrained agents. Surprise government transfer spending devalues real Treasury payoffs through fiscal inflation, while fiscal redistribution makes these high marginal utility states for bond investors, leading to risky government debt. We show that this fiscal redistribution mechanism can quantitatively explain the nominal term premium in a TANK framework. |
JEL: | E62 G12 |
Date: | 2025–05 |
URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33769 |
By: | Arishi Orra; Aryan Bhambu; Himanshu Choudhary; Manoj Thakur; Selvaraju Natarajan |
Abstract: | Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. Incorporating the investor's preference in pre-selecting assets for a portfolio is essential in refining their investment strategies. This study proposes a volatility-guided DRL-based portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is utilized for volatility forecasting of stocks and categorizes them based on their volatility as aggressive, moderate, and conservative. The DRL agent is then employed to learn an optimal investment policy by interacting with the historical market data. The efficacy of the proposed methodology is established using stocks from the Dow $30$ index. The proposed investor-specific DRL-based portfolios outperformed the baseline strategies by generating consistent risk-adjusted returns. |
Date: | 2025–04 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.03760 |
By: | Rui Fan (Xian Jiaotong University); Alex Nikolsko-Rzhevskyy (Lehigh University); Oleksandr Talavera (University of Birmingham) |
Abstract: | We examine the link between the U.S. stock market and foreign investor attention using SEC EDGAR log files and the MaxMind database to connect non-U.S. IP addresses with characteristics of S&P 500 stocks. A 10% increase in foreign EDGAR searches is associated with a 0.6% rise in abnormal daily returns. The effect is stronger for firms with lower sales, limited analyst coverage, lower institutional ownership, or high exposure to China. Our findings suggest that foreign attention, as reflected in EDGAR activity, shapes stock outcomes and that non-U.S. government policies may also influence U.S. market performance. |
Keywords: | EDGAR system; MaxMind; stock market; investor attention; information acquisition |
JEL: | G12 G14 |
Date: | 2025–03 |
URL: | https://d.repec.org/n?u=RePEc:bir:birmec:25-02 |
By: | Matt Brigida; Kathleen Maceyka |
Abstract: | In this analysis we determine factors driving the cross-sectional variation in uninsured deposits during the interest rate raising cycle of 2022 to 2023. The goal of our analysis is to determine whether banks proactively managed deposit run risk prior to the hiking cycle which produced the 2023 Regional Banking Crisis. We find evidence that interest rate forward, futures, and swap use affected the change in a bank uninsured deposits over the period. Interest rate option use, however, has no effect on the change in uninsured deposits. Similarly, bank equity levels were uncorrelated with uninsured deposit changes. We conclude we find no evidence of banks managing run risk via their balance sheet prior to the 2023 Regional Banking Crisis. |
Date: | 2025–06 |
URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.03344 |
By: | Imad Talhartit (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research); Sanae Ait Jillali (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research); Mounime El Kabbouri (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research) |
Abstract: | In today's data-driven economy, predicting stock market behavior has become a key focus for both finance professionals and academics. Traditionally reliant on historical and economic data, stock price forecasting is now being enhanced by AI technologies, especially Deep Learning and Natural Language Processing (NLP), which allow the integration of qualitative data like news sentiment and investor opinions. Deep Learning uses multi-layered neural networks to analyze complex patterns, while NLP enables machines to interpret human language, making it useful for extracting sentiment from media sources. Though most research has focused on developed markets, emerging economies like Morocco offer a unique context due to their evolving financial systems and data limitations. This study takes a theoretical and exploratory approach, aiming to conceptually examine how macroeconomic indicators and sentiment analysis can be integrated using deep learning models to enhance stock price prediction in Morocco. Rather than building a model, the paper reviews literature, evaluates data sources, and identifies key challenges and opportunities. Ultimately, the study aims to bridge AI techniques with financial theory in an emerging market setting, providing a foundation for future empirical research and interdisciplinary collaboration. |
Keywords: | Stock Price Prediction, Deep Learning, Natural Language Processing (NLP), Sentiment Analysis, Macroeconomic Indicators, Emerging Markets, Moroccan Financial Market |
Date: | 2025–05 |
URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05094029 |