This study is motivated by the ongoing debate on whether Shariah-compliant investment screening constrains portfolio performance relative to conventional benchmarks, particularly in developed capital markets such as the United States. Although Islamic equity indices have gained prominence, empirical evidence on their risk-adjusted performance remains mixed and often sensitive to market conditions and methodological choices. The study investigates whether Islamic indices constitute a viable investment avenue by empirically comparing the performance of a Shariah-compliant exchange-traded fund (SPUS) with its conventional counterpart (SPY) in the US equity market. This study employs monthly return data for both ETFs covering the period from January 2020 to December 2023, along with market, size, value, and momentum factors obtained from the F. French Data Library. Risk-adjusted performance is evaluated using Sharpe, Treynor, Information, and M-squared ratios, while return attribution is examined using the Carhart four-factor model supported by sub-period and bootstrap robustness analyses. The results show that SPUS generated a higher annualized return of 16.31% compared to 13.37% for SPY, with comparable volatility levels, and achieved a higher Sharpe ratio (0.7238 versus 0.5829). However, the estimated alpha of 0.0028 is statistically insignificant (p-value = 0.1048), and the 95% bootstrap confidence interval includes zero. The findings of this study suggest that the observed outperformance of the Islamic index is quantitatively explained by its significant exposure to large-cap growth factors, rather than by a statistically significant abnormal return.

ABSTRACT This paper examines the impact of regulatory controls on Bitcoin's excess returns and volatility. The paper innovates by proxying changes in the regulatory environment using global Google search volume intensity data. The generated regulatory indices accurately identify episodes of regulatory tightening within cryptocurrency markets. A three‐factor model—incorporating market, momentum, and size factors—is employed to evaluate the effects of regulation on Bitcoin returns. The study also assesses the influence of changes in the regulatory environment on volatility using additional controls. Findings reveal that increased regulation significantly reduces monthly Bitcoin returns and increases return volatility. These effects are both statistically and economically significant, robust across multiple proxies for regulatory activity, and persist even when accounting for the effects of the COVID‐19 pandemic. The results highlight the real regulatory risks associated with Bitcoin investments, particularly for risk‐averse investors, and underscore the importance of policy developments in shaping cryptocurrency market dynamics.

Momentum-based investment strategies face persistent challenges from noise contamination in financial time series, particularly within emerging markets such as China's ChiNext board. Traditional enhancement approaches typically address symptoms rather than underlying causes, resulting in continued vulnerability to market regime changes and performance deterioration. This study develops and evaluates a dual-denoising framework that integrates wavelet analysis for temporal noise reduction with isolation forest algorithms for cross-sectional anomaly detection. Our methodology employs comprehensive analysis of 1,200-1,300 ChiNext stocks spanning the 2015-2025 period, utilizing multiple machine learning architectures to assess portfolio performance across both long-only and long-short implementations. Key empirical findings demonstrate that: denoised momentum factors substantially improve predictive accuracy and portfolio performance; wavelet-based temporal denoising achieves remarkable effectiveness for turnover data with mean signal-to-noise ratio improvements of 6.4 dB; isolation forest cross-sectional anomaly detection provides critical risk management benefits by systematically eliminating stocks characterized by excessive trading activity and poor returns; and single-layer neural networks with isolation forest denoising achieve superior performance metrics, including 0.0199 monthly returns and a 0.2189 Sharpe ratio, outperforming more complex architectural alternatives. Addressing noise contamination at the data level represents a more fundamental solution than conventional enhancement techniques for momentum strategy limitations. Our findings establish systematic denoising as an effective approach for enhancing momentum-based investment strategies while maintaining practical implementability, with significant implications for both quantitative portfolio management and retail investor applications in emerging markets.

This research investigates the influence of momentum and liquidity factors on stock returns in the Iraq Stock Exchange, employing the six-factor Fama-French model and enhancing the analysis with sophisticated machine learning techniques, including Random Forests. This research seeks to address deficiencies in prior studies by implementing an integrated model within the Iraqi market context, which is under-researched in the context of multi-factor asset pricing models. A quantitative analytical approach was applied to a sample of 10 companies listed on the market from 2014 to 2023. The findings indicate that the random forest model markedly outperforms conventional regression models, elucidating 72% of the variance in stock returns and attaining prediction accuracy of up to 85%, as demonstrated. Examination of the significance of variables, the momentum and liquidity factors are the predominant influences in the analysis of stock returns within the Iraqi market. This can be achieved by underscoring the significant impact of non-traditional factors on emerging markets. The research offers significant insights for investors and decision-makers, emphasizing the necessity of incorporating sophisticated risk variables into their investing plans.

The improvement of the digital ability of vocational education teachers (VETs) has become a key link in the modernization of education. To enhance the digital competence of VETs, this study applies the back propagation (BP) Neural Network to improve their digital teaching ability and analyzes the tracking effect through a Keyword Knowledge Tracing (KKT) model based on a keyword structure. Specific research has significantly enhanced the digital capabilities of VETs by improving the BP neural network, optimizing the initial weights, introducing momentum factors, and adopting adaptive learning rates. Meanwhile, the KKT model is used to dynamically track the development trajectory of teachers’ digital capabilities, providing precise support for personalized training. In the experiment, the improved BP shows excellent performance in improving digital ability, with accuracy and recall rates of 0.959 and 0.931, significantly higher than other models. In terms of the improvement rate of digital tool usage, the improved BP is 85.2%. The KKT model also performs well in tracking the development of teachers’ digital abilities, with dynamic adaptability scores ranging from 9.2 to 9.6 and tracking ability stability exceeding 94%. This indicates that the improved BP and KKT models can effectively enhance teachers’ digital abilities. The research results have important practical significance for promoting the digital teaching capacity improvement of VETs and provide a scientific basis for educational institutions to formulate targeted teacher training plans.

This study seeks to evaluate the applicability of the six-factor Fama and French model in elucidating stock returns in the Iraqi stock market, particularly emphasizing momentum and liquidity variables from 2014 to 2023. The research employs a quantitative analytical methodology, utilizing historical financial data gathered from a sample of 10 publicly traded companies, with multiple regression analysis as the primary statistical instrument to assess the correlation between six variables and stock returns. The findings indicated that the six-factor model of Fama and French possesses superior explanatory power for equity returns in the Iraqi market when juxtaposed with the conventional capital asset pricing model (CAPM). Furthermore, the study uncovered distinctive behavior of certain factors within the Iraqi market, revealing a negative and significant impact of the size (SMB) and value (HML) factors, suggesting a preference among investors for large corporations and growth stocks. The liquidity factor (LIQ) revealed that highly liquid stocks yield superior returns, contradicting the conventional theory, which anticipates a risk premium for low-liquid stocks. Conversely, the momentum factor (UMD) proved to be an unreliable predictor of future stock performance. In contrast, the profitability factor (RMW) and investment factor (CMA) aligned with traditional theory, indicating that firms exhibiting high profitability and conservative investment strategies receive enhanced returns. The study advises against exclusive dependence on the beta coefficient as the singular metric for risk and return, advocating for the incorporation of supplementary factors such as profitability, investment strategies, and company size in investment decisions. It also urges the execution of comprehensive future research to elucidate the contradictory behavior of certain factors in the Iraqi market and to enhance liquidity and market efficiency.

Purpose This study examines the relationship between environmental, social and governance risk and asset-specific (idiosyncratic) risk for sustainable investment mutual funds and ETFs. Design/methodology/approach Employing data from Sustainalytics, Bloomberg, Yahoo! Finance and the Forum for Sustainable and Responsible Investment (USSIF), this study applies traditional cross-sectional regressions based on sub-sample analysis. This study estimates idiosyncratic risk for the sustainable test assets through the capital asset pricing model (CAPM) and an augmented asset pricing model that includes the risk factors of Fama and French (2015), a momentum factor and a liquidity factor. Findings This study provides evidence in support of the hypotheses that higher levels of unmanaged environmental and governance risks are associated with higher idiosyncratic risk across our test assets. In contrast, higher levels of unmanaged social risk are associated with lower idiosyncratic risk across the sustainable investment test assets. Approximately 20 percent of the cross-sectional variation in idiosyncratic risk among sustainable investment mutual funds and ETFs is collectively explained by unmanaged environmental, social and governance risks. The results are robust to different aggregate risk factors and time periods. Research limitations/implications While recent work has examined the relationship between idiosyncratic risk and investment assets with varying degrees of ESG performance, there has been an absence of research that investigates the relationship between ESG risk and the idiosyncratic risk of sustainable investment mutual funds and ETFs. Practical implications The findings in this study lend support toward including sustainability into information datasets and decision-making processes. This study yields implications for risk assessment and management. Originality/value This study proposes a novel approach towards examining the role of sustainability reporting for investment assets by examining the relationship between E-pillar, S-pillar and G-pillar risk in relation to idiosyncratic risk.

ABSTRACTRecent literature explores the profitability of various cryptocurrency momentum trading strategies and proposes cryptocurrency momentum as a pricing factor (Liu et al.). How risky is this factor‐based investment strategy for crypto‐investments? We answer this question by examining the distributional characteristics (hence, riskiness) of six cryptocurrency momentum trading strategies. The empirical evidence suggests that the realised variances of cryptocurrency momentum strategies are governed by power laws. The statistical tests derived from block bootstraps indicate that the population mean and variance of the momentum factor realised variances are statistically not defined. Contrary to the belief that cryptocurrency momentum trading strategies produce generous payoffs, our results imply that, in real life, we might not be able to realise these risk premiums. We conclude that the performance metrics evaluating the profitability of cryptocurrency momentum strategies, using variance as an input, are not informative. We also find cross‐sectional dependence amongst the tail risk of momentum strategies based on different formation periods.

ABSTRACTThis paper examines the factor momentum in commodity futures markets. Based on the US and UK data from 1985 to 2022, we first show that a commodity factor's past returns positively predict its future returns. This predictability is at its strongest over the 1‐month horizon, and could be explained by mispricing. The factor momentum suggests mean‐variance inefficient commodity factors and negatively impacts the efficiency of pricing models. We then construct the time‐series efficient factors, which exhibit higher Sharpe ratios and improve the performance of pricing models. These findings are robust across international commodity futures markets, but the transaction costs erode the economic gains of factor momentum and efficient factor strategies due to high portfolio turnover. Overall, our results point to the potential to time commodity factors and highlight the importance of conditional asset pricing in commodity futures markets.

This study proposes a risk-adjusted momentum strategy based on the STARR (Stable Tail-adjusted Return Ratio) indicator and investigates its performance across different industry sectors in the U.S. and Japanese equity markets. Using monthly data from 2010 to 2025, the strategy constructs Sharpe- and STARR-based momentum factors and applies mean-variance optimization to industry-level ETFs from the S&P 500 and Nikkei 225. Empirical results show that the STARR-based strategy offers superior downside risk control, particularly under extreme market conditions such as the COVID-19 crisis. Moreover, performance varies significantly across sectors and volatility regimes, confirming the presence of industry heterogeneity. The strategy demonstrates robust performance through various parameter configurations and cross-market validation. These findings suggest that incorporating downside-sensitive metrics like CVaR into momentum signal construction can enhance risk-adjusted returns and improve portfolio stability in diverse market environments. This research aims to evaluate the STARR-based momentum strategy's effectiveness across heterogeneous industries under uncertain market conditions.

ABSTRACTIn an increasingly knowledge‐based economy, intangible assets may be an important driver of firm performance and stock returns. We introduce an intangibles intensity factor (INT), distinct from the organization capital factor, and show that exposure to this factor strongly predicts stock returns, outperforming traditional factors. Integrating INT into the Fama–French five‐factor (FF5) and q‐factor models significantly enhances explanatory power across multiple tests and renders the investment factor redundant. An INT‐augmented five‐factor model (comprising market, size, profitability, momentum, and intangibles factors) outperforms the FF5 and q‐factor models in explaining a broad set of anomalies, highlighting the diminishing relevance of the book‐to‐market and investment factors.

HighlightsWhat are the main findings?The law of the influence of the thickness of the sensor’s buckling electrode and the distance between the sensing electrode and the buckling electrode on the sensor’s sensitivity;The improved FFT-BP method for harmonic noise reduction.What is the implication of the main finding?The electrode parameters of the field mill type electric field sensor have been optimized;The completed mathematical model of the input and output of the field mill type electric field sensor was established;A processing method for the output signal of the field mill type electric field sensor was studied, and the signal processing circuit was designed and optimized.Aiming at the issues that the field mill type electric field sensor lacks an accurate and complete mathematical model, and its signal is weak and contains a large amount of harmonic noise, on the basis of establishing the mathematical model of the sensor’s induction electrode, the finite element method was adopted to analyze the influence laws of parameters such as the thickness of the shielding electrode and the distance between the induction electrode and the shielding electrode on the sensor sensitivity. On this basis, the above parameters were optimized. A signal processing circuit incorporating a pre-integral transformation circuit, a differential amplification circuit, and a bias circuit was investigated, and a completed mathematical model of the input and output of the field mill type electric field sensor was established. An improved harmonic detection method combining fast Fourier transform and back propagation neural network (FFT-BP) was proposed, the learning rate, momentum factor, and excitation function jointly participated in the adjustment of the network, and the iterative search range of the algorithm was limited by the threshold interval, further improving the accuracy and rapidity of the sensor measurement. Experimental results indicate that within the simulated electric field intensity range of 0–20 kV/m in the laboratory, the measurement resolution of this system can reach 18.7 V/m, and the measurement linearity is more than 99%. The designed system is capable of measuring the atmospheric electric field intensity in real time, providing necessary data support for lightning monitoring and early warning.

ABSTRACTWe introduce a novel framework that dynamically optimizes currency factor strategies via trading currency spot and forward. We examine the performance of 24,336 portfolio optimization approaches and find that the optimized currency factors significantly outperform the naïve factors after correcting for data snooping bias. Our framework suits both symmetric factor portfolios, including carry, momentum, and value, and asymmetric factor portfolios, such as time series momentum and return signal momentum. An out‐of‐sample procedure that aggregates all the outperforming optimization approaches validates the economic significance of our optimized factor portfolio.

Purpose The few prior music sentiment papers have used post-2000 data and studied aggregate economic measures. This paper aims to explore how differences in demography and social economic status impact music sentiment’s relationship with financial economics. Further, this paper also investigates time trends of relations between music sentiment and financial economics by extending the sample period. Design/methodology/approach This paper mainly uses regression analysis to test the impact of music sentiment on both consumer sentiment and market returns. Rolling time-series regressions are used to investigate time trends in the consumer sentiment – music sentiment relation, and Fama–Macbeth cross-sectional regressions are used to explore how music sentiment prices cross-sectional expected stock returns. Findings This paper presents three novel results: (1) music sentiment has a differential impact across the consumption sentiment of age and income groups; (2) the impact of music sentiment displays significant time trends over a larger sample period; and (3) music sentiment is priced in the cross-section of expected stock returns, with meaningful correlation to the momentum factor. Originality/value By obtaining the weekly top 100 songs from the Billboard Hot 100 for an extended period of time, this paper highlights the multi-dimensional (demographic and temporal) considerations driving prior results regarding music sentiment.

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions.

In two dimensional magnets, the interplay of thermal fluctuations and spin anisotropy control the existence of long-range magnetic order. In the van der Waals antiferromagnets FePX3, orbital degeneracy in the t2g levels of the Fe2+ ions in octahedral coordination yields strong uniaxial anisotropy, which stabilizes magnetic order up to T≈100 K. Recent inelastic neutron scattering measurements around the magnetic ordering transition have shown the existence of a broad spectrum of magnetic fluctuations with nontrivial momentum dependence, which has been interpreted as evidence for localized entangled cluster excitations. In this paper, we offer an alternative interpretation using classical nonlinear spin dynamics simulations. We present stochastic Landau Lifshitz dynamics simulations that reproduce the neutron scattering measurements of Chen [] on FePSe3. These calculations faithfully explain the dynamical structure factor's momentum and energy dependence and point to a classical origin for the excitations observed in neutron spectroscopy and that the order-disorder transition can be understood in terms of thermal fluctuations overcoming the anisotropy energy. Published by the American Physical Society 2025

On the basis of reviewing relevant literature research, this paper uses the LDA clustering model to conduct a clustering analysis on the news corpus of China’s A-share market. Three environmental risk factors, namely “climate”, “carbon emissions”, and “ecology”, are extracted and their impact on stock excess returns is empirically tested. The results showed that environmental risk factors could better explain the returns of long-short portfolios based on environmental ratings, while traditional Fama–French five-factors and momentum factors could not explain this portfolio return. This paper further proposes a weighted investment portfolio strategy based on environmental risk factors. Compared with the equally weighted portfolio, the long-short portfolio constructed using this strategy demonstrates a stronger upward trend and lower volatility during the sample period from 2009 to 2024, effectively avoiding the impact of the 2015 A-share market crash. At the individual stock level, the Fama–MacBeth regression results showed that all three environmental risk factors had significant predictive power for future returns.

This study examines the explainability, validity, and applicability of multi-factor models in explaining the returns of Green (eco-friendly), Grey (neutral), and Red (environmentally harmful) EU securities. We apply the Fama–French three-factor and five-factor models, along with the Carhart four-factor model, to analyze changes in risk exposures and adjusted abnormal returns (alphas) before and after the 2009 global financial crisis (GFC). Green and Grey securities exhibit positive SMB loadings, while Grey’s HML shifts from negative to positive over time. Both Green and Red securities show positive SMB and HML factors but negative alphas in the second period, indicating systematic underperformance. Additionally, for Red assets, momentum (MOM), profitability (RMW), and investment (CMA) factors are positive and significant in the first period but become insignificant or negative later. These findings highlight structural shifts in factor exposures and contribute to the ongoing debate on the most suitable classical asset pricing framework for environmentally classified assets, offering insights into the effectiveness of traditional factor models in different classes of environmental assets in finance. Lastly, the three-factor model better captures the common variation in Green and Grey asset returns. Specifically, the 4-factor model and the HML Devil factor prove to be more effective in explaining returns for Red securities.

In view of the strong subjectivity of traditional instructional quality assessment methods and the difficulty in quantifying nonlinear teaching elements, this article proposes an online classroom assessment model of college music based on improved BP neural network (BPNN). The model integrates multi-dimensional data such as the frequency of teacher-student interaction and the utilization rate of teaching resources, and constructs a nonlinear mapping structure with double hidden layers. At the same time, the optimization strategy of dynamic learning rate (initial value 0.01, attenuation coefficient 0.9) and momentum factor (coefficient 0.9) is adopted. After cleaning and standardization, the real data is divided into training set and test set according to the ratio of 7: 3. Through cross-validation, the optimal number of hidden layer nodes is 25, and the normality of error distribution is verified (p > 0.05). The experimental results show that the prediction accuracy of this model reaches 95.2%(95% confidence interval is [93.7%, 96.4%]), which is 20.69% higher than the traditional ID3 algorithm, and the mean absolute error (MAE) is reduced to 0.032. The model is excellent in capturing complex indicators such as emotional interaction between teachers and students and innovation of teaching content (F1 value is 0.93), and it has stable generalization ability for data of different teaching platforms (standard deviation < 1.5). The dynamic learning rate strategy improves the training efficiency by 37% and effectively avoids the local optimization problem. This study confirms the effectiveness of neural network in education assessment and provides practical reference for the digital transformation of music education. In the future, this achievement is expected to be extended to interdisciplinary online teaching scenarios, thus promoting the development of education in the direction of fairness and individuality.

In previous research, the integration of fundamental and technical analysis in predicting the stock price has been proved successful when compared to fundamental model or technical model partially. Meanwhile, considering the rapid growth and diversification of Chinese stock market in recent years, it is promising to apply a hybrid strategy to CSI 300. The strategy implemented in this research is a momentum strategy that combines Technical Momentum and Fundamental Factors. In this paper, this strategy is implemented in CSI 300 data from January 1, 2014 to March 31, 2024 and it turns out that the annualized volatility of portfolio falls to more than half that of CSI 300 index after sector neutralization and ranking filter. Although this strategy does not demonstrate an outstanding annualized return, this research is still a valuable exploration in improving the stability in stock selection and enhancing risk management.