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on Financial Markets |
| By: | Othmane Zarhali; Cecilia Aubrun; Emmanuel Bacry; Jean-Philippe Bouchaud; Jean-Fran\c{c}ois Muzy |
| Abstract: | The Nested factor model was introduced by Chicheportiche et al. to represent non-linear correlations between stocks. Stock returns are explained by a standard factor model, but the (log)-volatilities of factors and residuals are themselves decomposed into factor modes, with a common dominant volatility mode affecting both market and sector factors but also residuals. Here, we consider the case of a single factor where the only dominant log-volatility mode is rough, with a Hurst exponent $H \simeq 0.11$ and the log-volatility residuals are ''super-rough'', with $H \simeq 0$. We demonstrate that such a construction naturally accounts for the somewhat surprising stylized fact reported by Wu et al. , where it has been observed that the Hurst exponents of stock indexes are large compared to those of individual stocks. We propose a statistical procedure to estimate the Hurst factor exponent from the stock returns dynamics together with theoretical guarantees of its consistency. We demonstrate the effectiveness of our approach through numerical experiments and apply it to daily stock data from the S&P500 index. The estimated roughness exponents for both the factor and idiosyncratic components validate the assumptions underlying our model. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.02678 |
| 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: | Nurbanu Bursa |
| Abstract: | Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and T\"urkiye), are gaining influence in global stock markets, although they remain susceptible to the economic conditions of developed countries like the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This interconnectedness and sensitivity of financial markets make understanding these relationships crucial for investors and policymakers to predict stock price movements accurately. To this end, we examined the main stock market indices of G7 and MINT countries from 2012 to 2024, using a recent graph neural network (GNN) algorithm called multivariate time series forecasting with graph neural network (MTGNN). This method allows for considering complex spatio-temporal connections in multivariate time series. In the implementations, MTGNN revealed that the US and Canada are the most influential G7 countries regarding stock indices in the forecasting process, and Indonesia and T\"urkiye are the most influential MINT countries. Additionally, our results showed that MTGNN outperformed traditional methods in forecasting the prices of stock market indices for MINT and G7 countries. Consequently, the study offers valuable insights into economic blocks' markets and presents a compelling empirical approach to analyzing global stock market dynamics using MTGNN. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.01945 |
| By: | Rashad Ahmed; Iñaki Aldasoro |
| Abstract: | This paper examines the impact of dollar-backed stablecoin flows on short-term US Treasury yields using daily data from 2021 to 2025. Estimates from instrumented local projection regressions suggest that a 2-standard deviation inflow into stablecoins lowers 3-month Treasury yields by 2-2.5 basis points within 10 days, with limited to no spillover effects on longer tenors. We also find evidence of asymmetric effects: stablecoin outflows raise yields by two to three times as much as inflows lower them. Decomposing the yield impact by issuer shows that USDT (Tether) has the largest contribution followed by USDC (Circle), consistent with their relative size. Our results highlight stablecoins' growing footprint in safe asset markets, with implications for monetary policy transmission, stablecoin reserve transparency, and financial stability. |
| Keywords: | stablecoins, treasury securities, money market funds, safe assets |
| JEL: | E42 E43 G12 G23 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1270 |
| By: | Shanyan Lai |
| Abstract: | This paper proposes an innovative Transformer model, Single-directional representative from Transformer (SERT), for US large capital stock pricing. It also innovatively applies the pre-trained Transformer models under the stock pricing and factor investment context. They are compared with standard Transformer models and encoder-only Transformer models in three periods covering the entire COVID-19 pandemic to examine the model adaptivity and suitability during the extreme market fluctuations. Namely, pre-COVID-19 period (mild up-trend), COVID-19 period (sharp up-trend with deep down shock) and 1-year post-COVID-19 (high fluctuation sideways movement). The best proposed SERT model achieves the highest out-of-sample R2, 11.2% and 10.91% respectively, when extreme market fluctuation takes place followed by pre-trained Transformer models (10.38% and 9.15%). Their Trend-following-based strategy wise performance also proves their excellent capability for hedging downside risks during market shocks. The proposed SERT model achieves a Sortino ratio 47% higher than the buy-and-hold benchmark in the equal-weighted portfolio and 28% higher in the value-weighted portfolio when the pandemic period is attended. It proves that Transformer models have a great capability to capture patterns of temporal sparsity data in the asset pricing factor model, especially with considerable volatilities. We also find the softmax signal filter as the common configuration of Transformer models in alternative contexts, which only eliminates differences between models, but does not improve strategy-wise performance, while increasing attention heads improve the model performance insignificantly and applying the 'layer norm first' method do not boost the model performance in our case. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01575 |
| By: | Mohammadhossein Rashidi; Mohammad Modarres |
| Abstract: | Predicting the price that has the least error and can provide the best and highest accuracy has been one of the most challenging issues and one of the most critical concerns among capital market activists and researchers. Therefore, a model that can solve problems and provide results with high accuracy is one of the topics of interest among researchers. In this project, using time series prediction models such as ARIMA to estimate the price, variables, and indicators related to technical analysis show the behavior of traders involved in involving psychological factors for the model. By linking all of these variables to stepwise regression, we identify the best variables influencing the prediction of the variable. Finally, we enter the selected variables as inputs to the artificial neural network. In other words, we want to call this whole prediction process the "ARIMA_Stepwise Regression_Neural Network" model and try to predict the price of gold in international financial markets. This approach is expected to be able to be used to predict the types of stocks, commodities, currency pairs, financial market indicators, and other items used in local and international financial markets. Moreover, a comparison between the results of this method and time series methods is also expressed. Finally, based on the results, it can be seen that the resulting hybrid model has the highest accuracy compared to the time series method, regression, and stepwise regression. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01402 |
| By: | Frost, Jon; Rochet, Jean-Charles; Shin, Huyn Song; Verdier, Marianne |
| Abstract: | We compare three competing digital payment instruments: bank deposits, private stablecoins and central bank digital currencies (CBDCs). A simple theoretical model integrates the theory of two-sided markets and payment economics to assess the benefits of interoperability through a retail fast payment system organised by the central bank. We show an equivalence result between such a fast payment system and a retail CBDC. We find that both can improve financial integration and increase trade volume, but also tend to reduce the market shares of incumbent intermediaries. |
| Keywords: | payments; CBDC; big tech; banks; stablecoins |
| JEL: | E42 E58 G21 L51 O31 |
| Date: | 2025–05–22 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130555 |
| By: | Kendzia, Michael Jan (Zurich University of Applied Sciences (ZHAW)); Diaz de la Rosa, Cyrill (Zurich University of Applied Sciences (ZHAW)); Dela Cruz, Jeremy (Zurich University of Applied Sciences (ZHAW)) |
| Abstract: | This study explores the relationship between the daily habits of S&P 500 CEOs and their financial remuneration. Using a mixed-method approach, the research analyzes time allocation across work, sleep, and exercise among 22 CEOs from leading publicly listed U.S. corporations. Regression analysis evaluates how these habits correlate with annual salaries, supported by a comparative survey of 89 non-S&P 500 CEOs. Our findings reveal a statistically significant positive association between working hours and base salary, suggesting that longer working days may contribute to financial success. Conversely, no significant links were found between salary and duration of sleep or physical exercise duration. The study highlights that while multiple factors shape executive remuneration, work ethic remains the most predictive. The article provides new empirical evidence for the influence of habitual behavior on executive performance and underscores the relevance of structured daily routines in high-level corporate roles. |
| Keywords: | work-life balance, time allocation, executive compensation, CEO habits, behavioral economics |
| JEL: | D91 G11 G34 M12 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17929 |