|
on Financial Markets |
| By: | Stefan Nagel |
| Abstract: | Return prediction with Random Fourier Features (RFF)—a very large number, P , of nonlinear trans-formations of a small number, K, of predictor variables—has become popular recently. Surprisingly, this approach appears to yield a successful out-of-sample stock market index timing strategy even when trained in rolling windows as small as T = 12 months with P in the thousands. However, when P ≫ T , the RFF-based forecast becomes a weighted average of the T training sample returns, with weights determined by the similarity between the predictor vectors in the training data and the current predictor vector. In short training windows, similarity primarily reflects temporal proximity, so the forecast reduces to a recency-weighted average of the T return observations in the training data—essentially a momentum strategy. Moreover, because similarity declines with predictor volatility, the result is a volatility-timed momentum strategy. The strong performance of the RFF-based strategy thus stems not from its ability to extract predictive signals from the training data, but from the fact that a volatility-timed momentum strategy happened to perform well in historical data. This point becomes clear when applying the same method to artificial data in which returns exhibit reversals rather than momentum: the RFF approach still constructs the same volatility-timed momentum strategy, which then performs poorly. |
| JEL: | G12 G14 G17 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34104 |
| By: | Imad Talhartit (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Research in Finance, Audit and Governance of Organizations (LARFAGO) - National School of Business and Management – ENCG Settat, Hassan The First University, Settat, Morocco.); Sanae Ait Jillali (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Research in Finance, Audit and Governance of Organizations (LARFAGO) - National School of Business and Management – ENCG Settat, Hassan The First University, Settat, Morocco.); Mounime El Kabbouri (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Research in Finance, Audit and Governance of Organizations (LARFAGO) - National School of Business and Management – ENCG Settat, Hassan The First University, Settat, Morocco.) |
| Abstract: | This study is part of an empirical and quantitative approach aimed at improving stock market fluctuation forecasting through the application of artificial intelligence models. More specifically, it evaluates the performance of two methods based on Long Short-Term Memory (LSTM) neural networks, one of the most powerful algorithms for analyzing financial time series. The first method is grounded in a classic LSTM model, while the second incorporates hyperparameter optimization using the Particle Swarm Optimization (PSO) metaheuristic method, allowing for better convergence and enhanced prediction accuracy. The study is conducted on ten stocks representing the US S&P 500 index, with historical data spanning several decades, collected via the Investing.com and Yahoo Finance platforms. The empirical results demonstrate a clear superiority of the LSTM-PSO model regarding predictive accuracy, with significant reductions in errors (MSE, RMSE, MAE, MSLE, and RMSLE) compared to the traditional model. These findings emphasize the advantages of combining artificial intelligence and algorithmic optimization for handling complex financial data. In the global context of digitization and automation of investment decisions, this research contributes significantly to the development of reliable predictive systems. Finally, the study raises the question of whether this methodological framework could be effectively adapted to emerging markets, such as the Moroccan Stock Market, where financial environments are characterized by lower trading volumes, different volatility patterns, and more limited historical data. This opens up avenues for future research into the challenges and opportunities of applying advanced AI-based forecasting models in less mature financial markets. |
| Keywords: | stock market forecasting, artificial intelligence, LSTM neural networks, Particle Swarm Optimization, financial time series, predictive modeling |
| Date: | 2025–07–18 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05177777 |
| By: | Maciej Wysocki; Pawe{\l} Sakowski |
| Abstract: | This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use these forecasts to optimize portfolios of stocks and cryptocurrencies. Our analysis presents results across different combinations of observation windows and rebalancing periods to compare performances of classical and deep learning variance-covariance estimation methods. The conclusions of the study are that although the strategies (portfolios) performance differed significantly between different combinations of parameters, generally the best results in terms of the information ratio and annualized returns are obtained using the LSTM-RNN models. Moreover, longer observation windows translate into better performance of the deep learning models indicating that these methods require longer windows to be able to efficiently capture the long-term dependencies of the variance-covariance matrix structure. Strategies with less frequent rebalancing typically perform better than these with the shortest rebalancing windows across all considered methods. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.14999 |
| By: | Agarwal, Vikas; Arisoy, Yakup Eser; Trinh, Tri |
| Abstract: | We investigate whether eponymous hedge funds-those named after their founder/manager-signal managerial ability or ethical behavior. While such funds do not outperform non-eponymous peers, they exhibit lower operational and fraud risks. Survey evidence supports these findings. Eponymous funds that violate regulations and breach investors' trust experience reduced investor flows despite strong performance. Offsetting these costs, eponymous fund managers benefit from lower failure rates and better contractual terms such as higher incentive fees and greater share restrictions. These results suggest that eponymy serves as a credible signal of ethical behavior and personal commitment, valued by investors beyond performance alone. |
| Keywords: | Eponymy, hedge funds, performance, signaling, reputation, trust, ethics, integrity |
| JEL: | G23 G40 G41 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfrwps:323936 |
| By: | Holm-Hadulla, Fédéric; Leombroni, Matteo |
| Abstract: | This paper studies the role of financial intermediaries in the transmission of central bank corporate bond purchases to bond yields. Contrary to standard expectations, we find that mutual funds—typically viewed as price-elastic investors—amplify, rather than dampen, the effects of these interventions on bond spreads. Following the ECB’s corporate bond purchase announcements in 2016 and 2020, bonds predominantly held by mutual funds experienced significantly larger and more persistent declines in spreads compared to those held by price-inelastic investors such as insurance companies, even after controlling for a broad set of bond characteristics. Drawing on additional empirical evidence and an equilibrium asset pricing model, we show that the state-contingent nature of the policy reduces perceived market risk for procyclical investors like mutualfunds, thereby boosting demand and compressing risk premia. JEL Classification: E52, E58, G11, G23 |
| Keywords: | central bank asset purchases, corporate bonds, non-bank financial institutions |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253101 |
| By: | Abiodun Finbarrs Oketunji |
| Abstract: | This research presents a framework for quantitative risk management in volatile markets, specifically focusing on expectile-based methodologies applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk (VaR) have demonstrated significant limitations during periods of market stress, as evidenced during the 2008 financial crisis and subsequent volatile periods. This study develops an advanced expectile-based framework that addresses the shortcomings of conventional quantile-based approaches by providing greater sensitivity to tail losses and improved stability in extreme market conditions. The research employs a dataset spanning two decades of FTSE 100 returns, incorporating periods of high volatility, market crashes, and recovery phases. Our methodology introduces novel mathematical formulations for expectile regression models, enhanced threshold determination techniques using time series analysis, and robust backtesting procedures. The empirical results demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms traditional VaR measures across various confidence levels and market conditions. The framework exhibits superior performance during volatile periods, with reduced model risk and enhanced predictive accuracy. Furthermore, the study establishes practical implementation guidelines for financial institutions and provides evidence-based recommendations for regulatory compliance and portfolio management. The findings contribute significantly to the literature on financial risk management and offer practical tools for practitioners dealing with volatile market environments. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.13391 |
| By: | Joshua Aslett; Thomas Cantens; François Chastel; Emmanuel A Crown; Stuart Hamilton |
| Abstract: | This technical note provides an introduction to generative artificial intelligence (GenAI) and its potential to support compliance risk analysis in tax and customs administration. Written primarily for a technical audience, it seeks to raise awareness of GenAI by explaining and demonstrating its capabilities. The note opens with a brief conceptual overview of GenAI technology. It then describes four generalized use cases where GenAI can augment the work of risk analysts. As experimental proofs of concept, a selection of worked examples is presented. Having demonstrated GenAI’s potential, the note then provides basic guidelines to help administrations that may be considering implementing the technology in an operational setting. It concludes with forward-looking statements on likely developments. |
| Keywords: | Tax administration; customs administration; artificial intelligence |
| Date: | 2025–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2025/013 |
| By: | Akash Deep; Chris Monico; W. Brent Lindquist; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | We propose a machine learning-based extension of the classical binomial option pricing model that incorporates key market microstructure effects. Traditional models assume frictionless markets, overlooking empirical features such as bid-ask spreads, discrete price movements, and serial return correlations. Our framework augments the binomial tree with path-dependent transition probabilities estimated via Random Forest classifiers trained on high-frequency market data. This approach preserves no-arbitrage conditions while embedding real-world trading dynamics into the pricing model. Using 46, 655 minute-level observations of SPY from January to June 2025, we achieve an AUC of 88.25% in forecasting one-step price movements. Order flow imbalance is identified as the most influential predictor, contributing 43.2% to feature importance. After resolving time-scaling inconsistencies in tree construction, our model yields option prices that deviate by 13.79% from Black-Scholes benchmarks, highlighting the impact of microstructure on fair value estimation. While computational limitations restrict the model to short-term derivatives, our results offer a robust, data-driven alternative to classical pricing methods grounded in empirical market behavior. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16701 |
| By: | Syeda Tasnim Fabiha; Rubaiyat Jahan Mumu; Farzana Aktar; B M Mainul Hossain |
| Abstract: | Share market is one of the most important sectors of economic development of a country. Everyday almost all companies issue their shares and investors buy and sell shares of these companies. Generally investors want to buy shares of the companies whose market liquidity is comparatively greater. Market liquidity depends on the average price of a share. In this paper, a thorough linear regression analysis has been performed on the stock market data of Dhaka Stock Exchange. Later, the linear model has been compared with random forest based on different metrics showing better results for random forest model. However, the amount of individual significance of different factors on the variability of stock price has been identified and explained. This paper also shows that the time series data is not capable of generating a predictive linear model for analysis. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18643 |