Pub Date : 2025-09-01DOI: 10.1016/j.jempfin.2025.101649
Matteo Bagnara, Benoit Vaucher
We examine how active risk- and holdings-based diversification of equity portfolios affect performance and vulnerability to large losses. Conducting a comprehensive empirical study of US-based funds, we find that risk-based and sector-based diversification significantly reduce active tail risk and the likelihood of extreme losses, without substantially diminishing portfolio performance. These effects are nonlinear and decreasing, suggesting that investors need not minimizing the concentration of their portfolios. We also examine these relationships on an unprecedented large sample of portfolios using a novel methodology that allows the production of portfolios with similar levels of risk, and find that they are robust to several definitions of extreme risk. Our results highlight the practical value of diversification in managing portfolio risk while maintaining competitive performance.
{"title":"Risk diversification and extreme risk mitigation","authors":"Matteo Bagnara, Benoit Vaucher","doi":"10.1016/j.jempfin.2025.101649","DOIUrl":"10.1016/j.jempfin.2025.101649","url":null,"abstract":"<div><div>We examine how active risk- and holdings-based diversification of equity portfolios affect performance and vulnerability to large losses. Conducting a comprehensive empirical study of US-based funds, we find that risk-based and sector-based diversification significantly reduce active tail risk and the likelihood of extreme losses, without substantially diminishing portfolio performance. These effects are nonlinear and decreasing, suggesting that investors need not minimizing the concentration of their portfolios. We also examine these relationships on an unprecedented large sample of portfolios using a novel methodology that allows the production of portfolios with similar levels of risk, and find that they are robust to several definitions of extreme risk. Our results highlight the practical value of diversification in managing portfolio risk while maintaining competitive performance.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101649"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-13DOI: 10.1016/j.jempfin.2025.101642
Deshui Yu , Difang Huang , Mingtao Zhou
This article examines the time-series predictive ability of the monthly option-implied idiosyncratic skewness () for the aggregate stock market. We find that is a strong predictor of the U.S. equity premium using both in-sample and out-of-sample tests at forecast horizons up to 36 months over the period from January 1996 to December 2021. In comparison, outperforms the previously used financial and macroeconomic variables. Furthermore, combining information in the transitional predictors with can further improve the forecasting performance than using alone. We provide two explanations for the documented predictability. First, exhibits strong procyclical behavior and consistently declines ahead of economic downturns. Second, acts as a forward-looking signal of investor sentiment and disagreement—positive shocks to significantly increase both future investor sentiment and disagreement, with effects that persist over several horizons.
{"title":"Option-implied idiosyncratic skewness and expected returns: Mind the long run","authors":"Deshui Yu , Difang Huang , Mingtao Zhou","doi":"10.1016/j.jempfin.2025.101642","DOIUrl":"10.1016/j.jempfin.2025.101642","url":null,"abstract":"<div><div>This article examines the time-series predictive ability of the monthly option-implied idiosyncratic skewness (<span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span>) for the aggregate stock market. We find that <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> is a strong predictor of the U.S. equity premium using both in-sample and out-of-sample tests at forecast horizons up to 36 months over the period from January 1996 to December 2021. In comparison, <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> outperforms the previously used financial and macroeconomic variables. Furthermore, combining information in the transitional predictors with <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> can further improve the forecasting performance than using <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> alone. We provide two explanations for the documented predictability. First, <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> exhibits strong procyclical behavior and consistently declines ahead of economic downturns. Second, <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> acts as a forward-looking signal of investor sentiment and disagreement—positive shocks to <span><math><mrow><mi>S</mi><mi>k</mi><mi>e</mi><mi>w</mi></mrow></math></span> significantly increase both future investor sentiment and disagreement, with effects that persist over several horizons.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101642"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-05-27DOI: 10.1016/j.jempfin.2025.101634
Jing Zhao
Following Kempf et al. (2017), this study employs an identification strategy that exploits exogenous shocks to unrelated parts of institutional shareholders’ portfolios to measure “distraction.” I find institutional shareholder “distraction” significantly and positively affects future innovation output and input. This positive effect exhibits considerable cross-sectional and intertemporal heterogeneity. Further, the positive effect is stronger in firms where institutional shareholder monitoring is less important or efficient, or firms subject to greater managerial myopia. These include innovative firms, firms with lower product market competition, weaker managerial power and stronger monitoring, and lower institutional ownership such that any given distraction is more impactful. Consequently, distraction enhances shareholder value through its positive impact on innovation. Taken together, the evidence suggests that managers respond to reduced myopic pressures, induced by exogenous shocks to institutional investors’ portfolios that shift their attention away, by pursuing long-term, risky and value-increasing investments such as innovation. Potential limitations of this study and their implications for future research are also thoroughly discussed.
继Kempf et al.（2017）之后，本研究采用了一种识别策略，利用对机构股东投资组合中不相关部分的外生冲击来衡量“分心”。我发现机构股东的“分心”显著且正向地影响未来的创新产出和投入。这种积极效应表现出相当大的横断面和跨期异质性。此外，在机构股东监督不太重要或效率较低的公司，或管理近视程度较高的公司，积极效应更强。这些企业包括创新型企业、产品市场竞争程度较低的企业、较弱的管理权力和较强的监督、较低的机构所有权，因此任何给定的分散注意力都更有影响力。因此，分散注意力通过其对创新的积极影响来提高股东价值。综上所述，证据表明，管理者通过追求创新等长期、高风险和增值的投资，来应对机构投资者投资组合受到的外源性冲击所导致的短视压力减轻。本研究的潜在局限性及其对未来研究的启示也进行了深入的讨论。
{"title":"(In)Attention: distracted shareholders and corporate innovation","authors":"Jing Zhao","doi":"10.1016/j.jempfin.2025.101634","DOIUrl":"10.1016/j.jempfin.2025.101634","url":null,"abstract":"<div><div>Following Kempf et al. (2017), this study employs an identification strategy that exploits exogenous shocks to unrelated parts of institutional shareholders’ portfolios to measure “distraction.” I find institutional shareholder “distraction” significantly and positively affects future innovation output and input. This positive effect exhibits considerable cross-sectional and intertemporal heterogeneity. Further, the positive effect is stronger in firms where institutional shareholder monitoring is less important or efficient, or firms subject to greater managerial myopia. These include innovative firms, firms with lower product market competition, weaker managerial power and stronger monitoring, and lower institutional ownership such that any given distraction is more impactful. Consequently, distraction enhances shareholder value through its positive impact on innovation. Taken together, the evidence suggests that managers respond to reduced myopic pressures, induced by exogenous shocks to institutional investors’ portfolios that shift their attention away, by pursuing long-term, risky and value-increasing investments such as innovation. Potential limitations of this study and their implications for future research are also thoroughly discussed.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101634"},"PeriodicalIF":2.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-07DOI: 10.1016/j.jempfin.2025.101644
Xiangzhen Li , Shida Liu , Hao Wang
This paper estimates real-time probabilities of default (PDs) for Chinese firms and assigns PD-implied ratings benchmarked to the historical default rates of S&P rating categories. PD-implied ratings tend to be lower and more granular than those issued by domestic credit rating agencies (DCRAs). They outperform DCRA ratings in predicting defaults and offer complementary information in credit price discovery. In terms of information content, PD-implied ratings incorporate richer and more persistent cashflow information than DCRA ratings do. Contributing factors such as implicit government guarantees and the moral hazard inherent in the issuer-pays business model play a significant role in elevating DCRA ratings, leading to greater divergence from PD-implied ratings and, consequently, differences in default prediction performance.
{"title":"Default-probability-implied credit ratings for Chinese firms","authors":"Xiangzhen Li , Shida Liu , Hao Wang","doi":"10.1016/j.jempfin.2025.101644","DOIUrl":"10.1016/j.jempfin.2025.101644","url":null,"abstract":"<div><div>This paper estimates real-time probabilities of default (PDs) for Chinese firms and assigns PD-implied ratings benchmarked to the historical default rates of S&P rating categories. PD-implied ratings tend to be lower and more granular than those issued by domestic credit rating agencies (DCRAs). They outperform DCRA ratings in predicting defaults and offer complementary information in credit price discovery. In terms of information content, PD-implied ratings incorporate richer and more persistent cashflow information than DCRA ratings do. Contributing factors such as implicit government guarantees and the moral hazard inherent in the issuer-pays business model play a significant role in elevating DCRA ratings, leading to greater divergence from PD-implied ratings and, consequently, differences in default prediction performance.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101644"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-29DOI: 10.1016/j.jempfin.2025.101647
Ying Yuan , Yong Qu , Tianyang Wang
This research introduces a novel constraint-based model framework for predicting risk premiums, thoroughly examining the mechanism and limitations of existing models in the literature and leveraging advanced machine learning techniques. The proposed framework effectively captures the regime-dependent forecasting characteristics. It incorporates the information content of predictive regression, “naive” historical average model, and zero value model, significantly reducing model uncertainty and parameter instability across univariate and multivariate predictions. Empirical analysis demonstrates the superiority of our strategy in terms of out-of-sample forecasting performance over a variety of competing models and under different market conditions, highlighting the robustness of our results. We further substantiate the validity of considering the market regime as an economic state variable and justify the rationality of our constraint-based model in elucidating the source of the improved predictability. Our study holds significant implications for financial and economic research, as well as practical applications in portfolio management and risk assessment.
{"title":"Predicting risk premiums: A constraint-based model","authors":"Ying Yuan , Yong Qu , Tianyang Wang","doi":"10.1016/j.jempfin.2025.101647","DOIUrl":"10.1016/j.jempfin.2025.101647","url":null,"abstract":"<div><div>This research introduces a novel constraint-based model framework for predicting risk premiums, thoroughly examining the mechanism and limitations of existing models in the literature and leveraging advanced machine learning techniques. The proposed framework effectively captures the regime-dependent forecasting characteristics. It incorporates the information content of predictive regression, “naive” historical average model, and zero value model, significantly reducing model uncertainty and parameter instability across univariate and multivariate predictions. Empirical analysis demonstrates the superiority of our strategy in terms of out-of-sample forecasting performance over a variety of competing models and under different market conditions, highlighting the robustness of our results. We further substantiate the validity of considering the market regime as an economic state variable and justify the rationality of our constraint-based model in elucidating the source of the improved predictability. Our study holds significant implications for financial and economic research, as well as practical applications in portfolio management and risk assessment.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101647"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-29DOI: 10.1016/j.jempfin.2025.101648
Gonçalo Faria , Fabio Verona
This paper explores the out-of-sample forecasting performance of 25 equity premium predictors over a sample period from 1973 to 2023. While conventional time-series methods reveal that only one predictor demonstrates significant out-of-sample predictive power, frequency-domain analysis uncovers additional predictive information hidden in the time series. Nearly half of the predictors exhibit statistically and economically meaningful predictive performance when decomposed into frequency components. The findings suggest that frequency-domain techniques can extract valuable insights that are often missed by traditional methods, enhancing the accuracy of equity premium forecasts.
{"title":"Unlocking predictive potential: The frequency-domain approach to equity premium forecasting","authors":"Gonçalo Faria , Fabio Verona","doi":"10.1016/j.jempfin.2025.101648","DOIUrl":"10.1016/j.jempfin.2025.101648","url":null,"abstract":"<div><div>This paper explores the out-of-sample forecasting performance of 25 equity premium predictors over a sample period from 1973 to 2023. While conventional time-series methods reveal that only one predictor demonstrates significant out-of-sample predictive power, frequency-domain analysis uncovers additional predictive information hidden in the time series. Nearly half of the predictors exhibit statistically and economically meaningful predictive performance when decomposed into frequency components. The findings suggest that frequency-domain techniques can extract valuable insights that are often missed by traditional methods, enhancing the accuracy of equity premium forecasts.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101648"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-25DOI: 10.1016/j.jempfin.2025.101651
Yangyang Chen , Jeffrey Ng , Emmanuel Ofosu , Xin Yang
Using the SEC’s 2016 Tick Size Pilot Program (TSPP) as a natural experiment, we investigate the effects of a tick size increase on firms’ choice of equity versus debt financing. We find that after the program’s implementation, TSPP-affected firms show a significant increase in equity issuance relative to that of debt. This finding is consistent with a reduction in adverse selection in equity financing due to more acquisition of fundamental information by these firms’ investors. In support of this inference, we show that the increase is concentrated among firms with investors that increase their information acquisition. We also find that the effect is more pronounced for firms that, prior to the program, have a higher level of concern about adverse selection in equity financing. Our study offers the novel insight that a tick size increase can affect firms’ financing choices because the increased tick size generates incentives for investors to acquire more fundamental information.
利用美国证券交易委员会（SEC） 2016年Tick Size Pilot Program （TSPP）作为自然实验，我们研究了Tick Size增加对公司选择股权融资与债务融资的影响。我们发现，在该计划实施后，受tspp影响的企业发行的股票相对于债券有显著的增加。这一发现与股权融资中逆向选择的减少是一致的，因为这些公司的投资者获得了更多的基本信息。为了支持这一推论，我们表明这种增长集中在有投资者的公司中，这些公司增加了他们的信息获取。我们还发现，对于那些在实施该计划之前对股权融资中的逆向选择有较高关注程度的公司来说，这种影响更为明显。我们的研究提供了一个新颖的见解，即滴答大小的增加会影响公司的融资选择，因为滴答大小的增加会激励投资者获取更多的基本信息。
{"title":"Tick size and firm financing decisions: Evidence from a natural experiment","authors":"Yangyang Chen , Jeffrey Ng , Emmanuel Ofosu , Xin Yang","doi":"10.1016/j.jempfin.2025.101651","DOIUrl":"10.1016/j.jempfin.2025.101651","url":null,"abstract":"<div><div>Using the SEC’s 2016 Tick Size Pilot Program (TSPP) as a natural experiment, we investigate the effects of a tick size increase on firms’ choice of equity versus debt financing. We find that after the program’s implementation, TSPP-affected firms show a significant increase in equity issuance relative to that of debt. This finding is consistent with a reduction in adverse selection in equity financing due to more acquisition of fundamental information by these firms’ investors. In support of this inference, we show that the increase is concentrated among firms with investors that increase their information acquisition. We also find that the effect is more pronounced for firms that, prior to the program, have a higher level of concern about adverse selection in equity financing. Our study offers the novel insight that a tick size increase can affect firms’ financing choices because the increased tick size generates incentives for investors to acquire more fundamental information.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101651"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-29DOI: 10.1016/j.jempfin.2025.101639
Jue Gong, Gang-Jin Wang, Yang Zhou, Chi Xie
We propose a cross-market volatility forecasting framework by applying attention-based spatial–temporal graph convolutional network model (ASTGCN) to forecast future volatility of stock indices in 18 financial markets. In our work, we construct cross-market volatility networks to integrate interrelations among financial markets and the corresponding features of each market. ASTGCN combines the spatial–temporal attention mechanisms with the spatial–temporal convolutions to simultaneously capture the dynamic spatial–temporal characteristics of global volatility data. Compared with competitive models, ASTGCN exhibits superiority in multivariate predictive accuracies under multiple forecasting horizons. Our proposed framework demonstrates outstanding stability through several robustness checks. We also inspect the training process of ASTGCN by extracting spatial attention matrices and find that interrelations among global financial markets perform differently in tranquil and turmoil periods. Our study levitates empirical findings in financial networks to practical application with a novel forecasting method in the deep learning community.
{"title":"Cross-market volatility forecasting with attention-based spatial–temporal graph convolutional networks","authors":"Jue Gong, Gang-Jin Wang, Yang Zhou, Chi Xie","doi":"10.1016/j.jempfin.2025.101639","DOIUrl":"10.1016/j.jempfin.2025.101639","url":null,"abstract":"<div><div>We propose a cross-market volatility forecasting framework by applying attention-based spatial–temporal graph convolutional network model (ASTGCN) to forecast future volatility of stock indices in 18 financial markets. In our work, we construct cross-market volatility networks to integrate interrelations among financial markets and the corresponding features of each market. ASTGCN combines the spatial–temporal attention mechanisms with the spatial–temporal convolutions to simultaneously capture the dynamic spatial–temporal characteristics of global volatility data. Compared with competitive models, ASTGCN exhibits superiority in multivariate predictive accuracies under multiple forecasting horizons. Our proposed framework demonstrates outstanding stability through several robustness checks. We also inspect the training process of ASTGCN by extracting spatial attention matrices and find that interrelations among global financial markets perform differently in tranquil and turmoil periods. Our study levitates empirical findings in financial networks to practical application with a novel forecasting method in the deep learning community.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101639"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-22DOI: 10.1016/j.jempfin.2025.101637
George Bulkley , Richard D.F. Harris , Vivekanand Nawosah
We use expectations of the short rate inferred from the term structure of interest rates to test several well-known models of behavioral biases and information frictions. We classify signals about future short rates by their cost of acquisition and find evidence of overreaction to high-cost signals and underreaction to low-cost signals, providing support for the overconfidence bias. We show that our results are unlikely to be driven by time-varying risk premia. The biases are so large that the market’s forecast errors are larger at all horizons than for forecasts obtained by assuming that the short rate follows a random walk.
{"title":"Behavioral biases, information frictions and interest rate expectations","authors":"George Bulkley , Richard D.F. Harris , Vivekanand Nawosah","doi":"10.1016/j.jempfin.2025.101637","DOIUrl":"10.1016/j.jempfin.2025.101637","url":null,"abstract":"<div><div>We use expectations of the short rate inferred from the term structure of interest rates to test several well-known models of behavioral biases and information frictions. We classify signals about future short rates by their cost of acquisition and find evidence of overreaction to high-cost signals and underreaction to low-cost signals, providing support for the overconfidence bias. We show that our results are unlikely to be driven by time-varying risk premia. The biases are so large that the market’s forecast errors are larger at all horizons than for forecasts obtained by assuming that the short rate follows a random walk.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101637"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-05-13DOI: 10.1016/j.jempfin.2025.101623
Fangquan Shi , Lianjie Shu , Xinhua Gu
Portfolio selection, faced with large volatile data sets of strongly correlated asset returns, is prone to unstable portfolio weights and serious estimation error. To attenuate this problem, our work proposes a new latent factor model equipped with both a suitable robust estimator to deal with cellwise data contamination and a diagonally-dominant (DD) covariance structure to account for cross-sectional dependence among residual returns. The proposed robust DD model is found to compare favorably with various competitors from the literature in terms of out-of-sample portfolio performance across real-world data sets.
{"title":"A robust latent factor model for high-dimensional portfolio selection","authors":"Fangquan Shi , Lianjie Shu , Xinhua Gu","doi":"10.1016/j.jempfin.2025.101623","DOIUrl":"10.1016/j.jempfin.2025.101623","url":null,"abstract":"<div><div>Portfolio selection, faced with large volatile data sets of strongly correlated asset returns, is prone to unstable portfolio weights and serious estimation error. To attenuate this problem, our work proposes a new latent factor model equipped with both a suitable robust estimator to deal with cellwise data contamination and a diagonally-dominant (DD) covariance structure to account for cross-sectional dependence among residual returns. The proposed robust DD model is found to compare favorably with various competitors from the literature in terms of out-of-sample portfolio performance across real-world data sets.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"83 ","pages":"Article 101623"},"PeriodicalIF":2.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号