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on Market Microstructure |
| By: | Alex Nezlobin; Martin Tassy |
| Abstract: | Although modern blockchains almost universally produce blocks at fixed intervals, existing models still lack an analytical formula for the loss-versus-rebalancing (LVR) incurred by Automated Market Makers (AMMs) liquidity providers in this setting. Leveraging tools from random walk theory, we derive the following closed-form approximation for the per block per unit of liquidity expected LVR under constant block time: \[ \overline{\mathrm{ARB}}= \frac{\, \sigma_b^{2}} {\, 2+\sqrt{2\pi}\, \gamma/(|\zeta(1/2)|\, \sigma_b)\, }+O\!\bigl(e^{-\mathrm{const}\tfrac{\gamma}{\sigma_b}}\bigr)\;\approx\; \frac{\sigma_b^{2}}{\, 2 + 1.7164\, \gamma/\sigma_b}, \] where $\sigma_b$ is the intra-block asset volatility, $\gamma$ the AMM spread and $\zeta$ the Riemann Zeta function. Our large Monte Carlo simulations show that this formula is in fact quasi-exact across practical parameter ranges. Extending our analysis to arbitrary block-time distributions as well, we demonstrate both that--under every admissible inter-block law--the probability that a block carries an arbitrage trade converges to a universal limit, and that only constant block spacing attains the asymptotically minimal LVR. This shows that constant block intervals provide the best possible protection against arbitrage for liquidity providers. \end{abstract} |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05113 |
| By: | Buschulte, Sonja Lisa |
| Abstract: | This dissertation examines the informational efficiency in the corporate bond market. More precisely, it shows how new information stemming from announcements of bond issuing firms impacts the prices of these bonds. While the equity market efficiency is well examined, historically limited data quality and the complexity of bonds leave research gaps in the corporate bond market. Composed of three studies, this dissertation uses a comprehensive sample of US corporate bond trading data to close these gaps further. The first study examines the power of event studies in corporate bond markets and thus sets the basis for testing informational efficiency in the bond market around any event. It shows that two market phenomena negatively impact the informative value of results: Test power decreases rapidly in the presence of event induced variance. Moreover, bond market illiquidity is problematic with samples focused on above average maturities and credit risks. The second study specifically tests informational efficiency around dividend announcements that imply dividend payout ratio changes. It provides insights into significant, negative reactions that bondholders exhibit and highlights the complex effects that drive these bond market reactions. Additionally, it shows the explanatory power of signaling and wealth transfer hypotheses, while the information content hypothesis is refuted. The third study analyzes the interaction effect between earnings and dividend announcements. The results of this study suggest that a strong interaction effect is present in the corporate bond market and that it is robust across several specifications. Consequently, the interaction effect of dividend and earnings announcements has to be considered when evaluating the overall contained information for the bond market. |
| Date: | 2025–05–12 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:154753 |
| By: | Zihao Guo; Hanqing Jin; Jiaqi Kuang; Zhongmin Qian; Jinghan Wang |
| Abstract: | Quantitative trading strategies based on medium- and high-frequency data have long been of significant interest in the futures market. The advancement of statistical arbitrage and deep learning techniques has improved the ability of processing high-frequency data, but also reduced arbitrage opportunities for traditional methods, yielding strategies that are less interpretable and more unstable. Consequently, the pursuit of more stable and interpretable quantitative investment strategies remains a key objective for futures market participants. In this study, we propose a novel pairs trading strategy by leveraging the mathematical concept of path signature which serves as a feature representation of time series data. Specifically, the path signature is decomposed to create two new indicators: the path interactivity indicator segmented signature and the change direction indicator path difference product. These indicators serve as double filters in our strategy design. Using minute-level futures data, we demonstrate that our strategy significantly improves upon traditional pairs trading with increasing returns, reducing maximum drawdown, and enhancing the Sharpe ratio. The method we have proposed in the present work offers greater interpretability and robustness while ensuring a considerable rate of return, highlighting the potential of path signature techniques in financial trading applications. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05332 |
| By: | Phillip An; Karlye Dilts Stedman; Amaze Lusompa |
| Abstract: | This paper investigates the utility of daily data in measuring high-frequency monetary policy surprises, comparing various announcement-day asset price changes with their intradaily (30-minute) counterparts. We find that both frequencies are similarly distributed and often highly correlated, particularly for longer-horizon measures. Testing daily surprises for systematic contamination from non-monetary policy news, we find no evidence to suggest that contemporaneous news releases bias their measurement. Empirical applications, including high-frequency passthrough to Treasury yields and proxy SVAR models, suggest that daily surprises produce results comparable to those obtained with intradaily data. Our findings suggest that while intradaily data remains invaluable for certain applications, daily data offers a practical and robust alternative for assessing monetary policy surprises, particularly when the event, or the reaction to it, extends beyond a narrow window, or when intradaily data is unavailable. |
| JEL: | E43 E44 E52 E58 G14 |
| Date: | 2025–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:100052 |
| By: | Jędrzej Maskiewicz (Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw); Paweł Sakowski (Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw) |
| 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. |
| Keywords: | Reinforcement Learning, Deep Learning, stock market, algorithmic trading, Double Deep Q-Network, Proximal Policy Optimization |
| JEL: | C4 C14 C45 C53 C58 G13 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-14 |
| By: | Yandiev Magomet (Department of Economics, Lomonosov Moscow State University) |
| Abstract: | The article is devoted to the explanation of the reason of the post-IPO stock undervaluation phenomenon, which is quite widespread in the markets. The author shows that the first day of trading fundamentally differs from other trading days by a very large, uncharacteristic for other days volume of deals, the absolute majority of which are speculative. As a result, the real reason for the phenomenon of undervaluation is not information asymmetry, underwriter’s reputation, taxation, etc., but the profit: speculative investors expect to get the maximum income from speculating on shares whose quotations have not yet settled in the market after the IPO. |
| Keywords: | IPO, underpricing, Russia |
| JEL: | G11 G12 G23 G32 G41 |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:upa:wpaper:0069 |