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on Financial Markets |
| By: | Mateusz Wilinski; Juho Kanniainen |
| Abstract: | In this work we show how generative tools, which were successfully applied to limit order book data, can be utilized for the task of imitating trading agents. To this end, we propose a modified generative architecture based on the state-space model, and apply it to limit order book data with identified investors. The model is trained on synthetic data, generated from a heterogeneous agent-based model. Finally, we compare model's predicted distribution over different aspects of investors' actions, with the ground truths known from the agent-based model. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.00982 |
| By: | Luo, Wenwen (University of Bristol); Paczos, Wojtek (Cardiff Business School, Cardiff University; Institute of Economics, Polish Academy of Sciences) |
| Abstract: | Panel regressions for 474 listed Chinese healthcare firms (2020–2022) show that stricter Zero-COVID stringency boosted stock returns, while the vaccination effect switched from positive early on to negative later. Interaction terms reveal stronger stringency effects and weaker vaccine effects when case numbers were high. |
| Keywords: | Stock returns; Zero-COVID; Healthcare sector |
| JEL: | G12 E65 C23 I18 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2025/16 |
| By: | Philippe Bergault; S\'ebastien Bieber; Olivier Gu\'eant; Wenkai Zhang |
| Abstract: | In traditional financial markets, yield curves are widely available for countries (and, by extension, currencies), financial institutions, and large corporates. These curves are used to calibrate stochastic interest rate models, discount future cash flows, and price financial products. Yield curves, however, can be readily computed only because of the current size and structure of bond markets. In cryptocurrency markets, where fixed-rate lending and bonds are almost nonexistent as of early 2025, the yield curve associated with each currency must be estimated by other means. In this paper, we show how mathematical tools can be used to construct yield curves for cryptocurrencies by leveraging data from the highly developed markets for cryptocurrency derivatives. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.03964 |
| By: | Vassilios Babalos (Department of Accounting and Finance, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece); Geoffrey M. Ngene (Department of Accounting and Finance, Deese College of Business and Economics, North Carolina A&T State University. 1601 East Market Street, Greensboro, NC 27411, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | This study examines the sector-level herding behavior and herding spillover across eleven US-listed Real Estate Investment Trusts (REITs) sectors from January 4, 1999 to December 8, 2023. A standard linear model shows no herding behavior for all sectors, except for the lodging and resorts sector; whereas, a more robust quantile regression reveals significant herding in all eleven sectors and for the overall market at the lower tails of the distribution of cross-sectional return dispersion. The time-varying parameter ordinary least squares (TV-OLS) approach demonstrates spasmodic switches between herding and anti-herding behaviors during the sample period across all sectors and the overall market. A spillover analysis highlights significant herding spillover effects across REIT sectors. Evidence of negative spillover effects with portfolio diversification benefits is driven by the stable demand for essential REITs, such as residential and healthcare, and the structure of long-term lease contracts for infrastructural, industrial, office, diversified, and regional malls REITs. Our findings entail implications for the decisions of retail and institutional investors and for the insights of regulatory authorities and policymakers. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202531 |
| By: | Sourish Das |
| Abstract: | This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-linear function, allowing tail risk to respond dynamically to market volatility. We examine four prior choices for Bayesian regularisation of regression coefficients: Cauchy, Lasso (Laplace), Ridge (Gaussian), and Zellner's g-prior. Simulation results suggest that the Cauchy prior delivers the best trade-off between predictive accuracy and model simplicity, achieving the lowest RMSE, AIC, and BIC values. Empirically, we apply the model to large negative returns (exceeding 5%) in the Nifty 50 index. Volatility measures from the Nifty 50, S&P 500, and gold are used as covariates to capture both domestic and global risk drivers. Our findings show that tail risk increases significantly with higher market volatility. In particular, both S&P 500 and gold volatilities contribute meaningfully to crash prediction, highlighting global spillover and flight-to-safety effects. The proposed GPR model offers a robust and interpretable approach for tail risk forecasting in emerging markets. It improves upon traditional EVT-based models by incorporating real-time financial indicators, making it useful for practitioners, policymakers, and financial regulators concerned with systemic risk and stress testing. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.17549 |
| By: | Maciej Wysocki |
| Abstract: | This paper examines systematic put-writing strategies applied to S&P 500 Index options, with a focus on position sizing as a key determinant of long-term performance. Despite the well-documented volatility risk premium, where implied volatility exceeds realized volatility, the practical implementation of short-dated volatility-selling strategies remains underdeveloped in the literature. This study evaluates three position sizing approaches: the Kelly criterion, VIX-based volatility regime scaling, and a novel hybrid method combining both. Using SPXW options with expirations from 0 to 5 days, the analysis explores a broad design space, including moneyness levels, volatility estimators, and memory horizons. Results show that ultra-short-dated, far out-of-the-money options deliver superior risk-adjusted returns. The hybrid sizing method consistently balances return generation with robust drawdown control, particularly under low-volatility conditions such as those seen in 2024. The study offers new insights into volatility harvesting, introducing a dynamic sizing framework that adapts to shifting market regimes. It also contributes practical guidance for constructing short-dated option strategies that are robust across market environments. These findings have direct applications for institutional investors seeking to enhance portfolio efficiency through systematic exposure to volatility premia. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.16598 |
| By: | Igor Halperin |
| Abstract: | This work builds upon the long-standing conjecture that linear diffusion models are inadequate for complex market dynamics. Specifically, it provides experimental validation for the author's prior arguments that realistic market dynamics are governed by higher-order (cubic and higher) non-linearities in the drift. As the diffusion drift is given by the negative gradient of a potential function, this means that a non-linear drift translates into a non-quadratic potential. These arguments were based both on general theoretical grounds as well as a structured approach to modeling the price dynamics which incorporates money flows and their impact on market prices. Here, we find direct confirmation of this view by analyzing high-frequency crypto currency data at different time scales ranging from minutes to months. We find that markets can be characterized by either a single-well or a double-well potential, depending on the time period and sampling frequency, where a double-well potential may signal market uncertainty or stress. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.02941 |
| By: | Ayca Topaloglu-Bozkurt; Tuba Pelin Sumer; Suheyla Ozyildirim |
| Abstract: | Using granular data from the Turkish banking system, we exploit a large currency depreciation in 2018 to explore whether, and how, banks’ cross-border FX borrowing evolved after the exchange rate shock. Our bank-level empirical findings show that a currency shock has a significant and negative impact on cross-border interbank FX borrowing, but the large foreign currency buffers held by banks mitigate the negative impact of the crisis on their FX borrowing. Furthermore, we find that the impact differs depending on the characteristics of the banks or the regional diversification of the lender banks. Sub-sample analysis via connectedness shows that the mitigating impact of an FX buffer is lower for strongly connected banks, while the analysis by business types indicates that the impact is higher for development banks. On the other hand, we find that the cross-border FX borrowing of resident banks decreases with the FX shock if the lender bank is located in advanced economies, but it increases if the lender banks are from emerging economies or the Middle East. Our results show the importance of diversification of funding partners to alleviate the negative impacts of an abrupt foreign currency depreciation on FX borrowing of the resident banks, i.e. to have a stable source of foreign exchange funding. |
| Keywords: | Cross-border FX borrowing, Currency shock, Connectedness, Diversification of Funding Partners |
| JEL: | G21 L14 F34 G15 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:wpaper:2513 |
| By: | Jonas Heim; Thomas Nitschka |
| Abstract: | This paper evaluates whether CO2 emission levels or emission intensities are firm characteristics that drive Swiss firms’ stock returns. We show that standard characteristics such as size and the book-to-market equity ratio are more important determinants of firm-level stock returns than are CO2 levels (intensities). Brown firms (high CO2 levels or intensities) tend to be large and exhibit low book-to-market equity ratios, whereas their green counterparts are small and exhibit high book-to-market equity ratios. This explains why return differences between brown and green firms are statistically indistinguishable from zero after controlling for exposures to standard risk factors. |
| Keywords: | Climate change, CO2 emissions, Event study, Risk premium |
| JEL: | G12 Q54 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2025-13 |
| By: | Chandradew Sharma |
| Abstract: | This study presents a unified, distribution-aware, and complexity-informed framework for understanding equity return dynamics in the Indian market, using 34 years (1990 to 2024) of Nifty 50 index data. Addressing a key gap in the literature, we demonstrate that the price to earnings ratio, as a valuation metric, may probabilistically map return distributions across investment horizons spanning from days to decades. Return profiles exhibit strong asymmetry. One-year returns show a 74 percent probability of gain, with a modal return of 10.67 percent and a reward-to-risk ratio exceeding 5. Over long horizons, modal CAGRs surpass 13 percent, while worst-case returns remain negative for up to ten years, defining a historical trapping period. This horizon shortens to six years in the post-1999 period, reflecting growing market resilience. Conditional analysis of the P/E ratio reveals regime-dependent outcomes. Low valuations (P/E less than 13) historically show zero probability of loss across all horizons, while high valuations (P/E greater than 27) correspond to unstable returns and extended breakeven periods. To uncover deeper structure, we apply tools from complexity science. Entropy, Hurst exponents, and Lyapunov indicators reveal weak persistence, long memory, and low-dimensional chaos. Information-theoretic metrics, including mutual information and transfer entropy, confirm a directional and predictive influence of valuation on future returns. These findings offer actionable insights for asset allocation, downside risk management, and long-term investment strategy in emerging markets. Our framework bridges valuation, conditional distributions, and nonlinear dynamics in a rigorous and practically relevant manner. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.00697 |
| By: | Paulo Andr\'e Lima de Castro |
| Abstract: | Autonomous trading strategies have been a subject of research within the field of artificial intelligence (AI) for aconsiderable period. Various AI techniques have been explored to develop autonomous agents capable of trading financial assets. These approaches encompass traditional methods such as neural networks, fuzzy logic, and reinforcement learning, as well as more recent advancements, including deep neural networks and deep reinforcement learning. Many developers report success in creating strategies that exhibit strong performance during simulations using historical price data, a process commonly referred to as backtesting. However, when these strategies are deployed in real markets, their performance often deteriorates, particularly in terms of risk-adjusted returns. In this study, we propose an AI-based strategy inspired by a classical investment paradigm: Value Investing. Financial AI models are highly susceptible to lookahead bias and other forms of bias that can significantly inflate performance in backtesting compared to live trading conditions. To address this issue, we conducted a series of computational simulations while controlling for these biases, thereby reducing the risk of overfitting. Our results indicate that the proposed approach outperforms major Brazilian market benchmarks. Moreover, the strategy, named AlphaX, demonstrated superior performance relative to widely used technical indicators such as the Relative Strength Index (RSI) and Money Flow Index (MFI), with statistically significant results. Finally, we discuss several open challenges and highlight emerging technologies in qualitative analysis that may contribute to the development of a comprehensive AI-based Value Investing framework in the future |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.13429 |