Pub Date : 2025-06-01Epub Date: 2025-05-06DOI: 10.1016/j.jempfin.2025.101618
Paul Borochin , Yanhui Zhao
We evaluate the importance of nonlinear and interactive effects in implied volatility innovation forecasting by comparing the performance of machine learning models that can search for interactive effects relative to classical ones that cannot, measuring the economic significance of these predictions in cross-sectional and time series pricing tests of delta-hedged option returns. Machine learning models offer superior out of sample performance. Since the predictive variables are the same across all models, these performance differences likely capture the value of nonlinear and interactive effects in implied volatility forecasts. Our results are robust to look-ahead bias and model overfitting.
{"title":"The economic value of equity implied volatility forecasting with machine learning","authors":"Paul Borochin , Yanhui Zhao","doi":"10.1016/j.jempfin.2025.101618","DOIUrl":"10.1016/j.jempfin.2025.101618","url":null,"abstract":"<div><div>We evaluate the importance of nonlinear and interactive effects in implied volatility innovation forecasting by comparing the performance of machine learning models that can search for interactive effects relative to classical ones that cannot, measuring the economic significance of these predictions in cross-sectional and time series pricing tests of delta-hedged option returns. Machine learning models offer superior out of sample performance. Since the predictive variables are the same across all models, these performance differences likely capture the value of nonlinear and interactive effects in implied volatility forecasts. Our results are robust to look-ahead bias and model overfitting.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101618"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923621","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 : 2025-06-01Epub Date: 2025-05-13DOI: 10.1016/j.jempfin.2025.101622
Deshui Yu , Yayi Yan
This paper proposes a system of time-varying models for predictive regressions, where a time-varying autoregressive (TV-AR) process is introduced to model the dynamics of the predictors and a linear control function approach is used to improve the estimation efficiency. We employ a profile likelihood estimation method to estimate both constant and time-varying coefficients and propose a hypothesis test to examine the parameter stability. We establish the asymptotic properties of the proposed estimators and test statistics accordingly. Monte Carlo simulations show that the proposed methods work well in finite samples. Empirically, the TV-AR process effectively approximates the time-series behavior of a broad set of potential predictors. Furthermore, we reject the stability assumption of predictive models for more than half of these predictors. Finally, the linear projection method not only improves estimator efficiency but also enhances out-of-sample forecasting performance, leading to significant utility gains in forecasting experiments.
{"title":"A system of time-varying models for predictive regressions","authors":"Deshui Yu , Yayi Yan","doi":"10.1016/j.jempfin.2025.101622","DOIUrl":"10.1016/j.jempfin.2025.101622","url":null,"abstract":"<div><div>This paper proposes a system of time-varying models for predictive regressions, where a time-varying autoregressive (TV-AR) process is introduced to model the dynamics of the predictors and a linear control function approach is used to improve the estimation efficiency. We employ a profile likelihood estimation method to estimate both constant and time-varying coefficients and propose a hypothesis test to examine the parameter stability. We establish the asymptotic properties of the proposed estimators and test statistics accordingly. Monte Carlo simulations show that the proposed methods work well in finite samples. Empirically, the TV-AR process effectively approximates the time-series behavior of a broad set of potential predictors. Furthermore, we reject the stability assumption of predictive models for more than half of these predictors. Finally, the linear projection method not only improves estimator efficiency but also enhances out-of-sample forecasting performance, leading to significant utility gains in forecasting experiments.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101622"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937810","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 : 2025-06-01Epub Date: 2025-05-14DOI: 10.1016/j.jempfin.2025.101624
Lu Jolly Zhou , Nan Deng , Chenchen Li
This study examines the granular impact of capital market liberalization on the real economy, utilizing the distinctive context of the Chinese market as a quasi-natural experimental setting. Our analysis demonstrates that capital market liberalization positively influences firm-level productivity. We further explore the mechanisms and provide empirical evidence that capital market liberalization improves asset pricing efficiency by enhancing informed trading effectiveness and rectifying stock mispricing. It also optimizes corporate governance from four distinct perspectives: mitigating agency costs, augmenting operational profitability, bolstering labor productivity, and enhancing transparency. These factors collectively contribute to improved productivity at the firm level, confirming the granular impact of financial liberalization in the product market.
{"title":"Unlocking efficiency: How capital market liberalization shapes firm productivity","authors":"Lu Jolly Zhou , Nan Deng , Chenchen Li","doi":"10.1016/j.jempfin.2025.101624","DOIUrl":"10.1016/j.jempfin.2025.101624","url":null,"abstract":"<div><div>This study examines the granular impact of capital market liberalization on the real economy, utilizing the distinctive context of the Chinese market as a quasi-natural experimental setting. Our analysis demonstrates that capital market liberalization positively influences firm-level productivity. We further explore the mechanisms and provide empirical evidence that capital market liberalization improves asset pricing efficiency by enhancing informed trading effectiveness and rectifying stock mispricing. It also optimizes corporate governance from four distinct perspectives: mitigating agency costs, augmenting operational profitability, bolstering labor productivity, and enhancing transparency. These factors collectively contribute to improved productivity at the firm level, confirming the granular impact of financial liberalization in the product market.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101624"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098664","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 : 2025-06-01Epub Date: 2025-03-07DOI: 10.1016/j.jempfin.2025.101607
George Kladakis , Nicole Lux , Alexandros Skouralis
The commercial real estate (CRE) market significantly influences financial stability, given its size, use as collateral, and cyclicality. This study explores macro-financial vulnerabilities arising from the CRE market, revealing that adverse developments in CRE capital values amplify systemic risk across financial sub-sectors, namely, banks, insurance companies and investment trusts, consistent with the collateral channel hypothesis. The CRE and financial markets relationship, however, displays nonlinearities. We introduce a UK CRE Misalignment index which integrates various market indicators to assess deviations from fundamental values in the CRE sector. We find that during market misalignments, the link between systemic risk and CRE growth weakens, suggesting that further property price increases in an overheated market could lead to a bubble and heightened systemic risk, in line with the deviation hypothesis. Finally, we employ a quantile regression model that captures another aspect of this non-linear relationship. We find that positive (negative) developments in the CRE market decrease (increase) the right tail of the historical systemic risk distribution, but CRE variation has a weak impact on the left tail and cannot effectively reduce systemic risk in periods of growth.
{"title":"Exploring the non-linear dynamics between Commercial Real Estate and systemic risk","authors":"George Kladakis , Nicole Lux , Alexandros Skouralis","doi":"10.1016/j.jempfin.2025.101607","DOIUrl":"10.1016/j.jempfin.2025.101607","url":null,"abstract":"<div><div>The commercial real estate (CRE) market significantly influences financial stability, given its size, use as collateral, and cyclicality. This study explores macro-financial vulnerabilities arising from the CRE market, revealing that adverse developments in CRE capital values amplify systemic risk across financial sub-sectors, namely, banks, insurance companies and investment trusts, consistent with the <em>collateral channel hypothesis</em>. The CRE and financial markets relationship, however, displays nonlinearities. We introduce a UK CRE Misalignment index which integrates various market indicators to assess deviations from fundamental values in the CRE sector. We find that during market misalignments, the link between systemic risk and CRE growth weakens, suggesting that further property price increases in an overheated market could lead to a bubble and heightened systemic risk, in line with the <em>deviation hypothesis</em>. Finally, we employ a quantile regression model that captures another aspect of this non-linear relationship. We find that positive (negative) developments in the CRE market decrease (increase) the right tail of the historical systemic risk distribution, but CRE variation has a weak impact on the left tail and cannot effectively reduce systemic risk in periods of growth.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101607"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-15DOI: 10.1016/j.jempfin.2025.101608
Chen Chen , Andrew Cohen , Qiqi Liang , Licheng Sun
Subrahmanyam (1991) presents a model in which increased variance in liquidity trades reduces price efficiency when market makers are risk-averse. Motivated by this theoretical insight, we hypothesize that pent-up demand from lottery-seeking investors amplifies their overreactions to news, leading to larger short-term return reversals. Consistent with this hypothesis, we identify a significant pattern in weekly U.S. stock returns for lottery-like stocks, defined by high recent maximum daily returns (MAX). Specifically, high-MAX stocks that were past 1-week losers (or winners) exhibit notably positive (or negative) returns in the following week. Applying a short-term reversal strategy to high-MAX stocks generates an average weekly return of 1.66%, significantly outperforming the 0.65% return from the same strategy applied to low-MAX stocks. This result remains robust even after controlling for market microstructure biases and survives a series of robustness tests. Interestingly, the MAX-enhanced reversal strategy proves effective only when retail order imbalance is in the highest quintile. This result holds across both value-weighted and equal-weighted portfolios, underscoring the pivotal role of retail investors. Taken together, our findings highlight a new channel through which retail investors’ preference for lottery-like payoffs amplifies their overreactions, enhancing the profitability of short-term reversal strategies.
{"title":"Maxing out short-term reversals in weekly stock returns","authors":"Chen Chen , Andrew Cohen , Qiqi Liang , Licheng Sun","doi":"10.1016/j.jempfin.2025.101608","DOIUrl":"10.1016/j.jempfin.2025.101608","url":null,"abstract":"<div><div>Subrahmanyam (1991) presents a model in which increased variance in liquidity trades reduces price efficiency when market makers are risk-averse. Motivated by this theoretical insight, we hypothesize that pent-up demand from lottery-seeking investors amplifies their overreactions to news, leading to larger short-term return reversals. Consistent with this hypothesis, we identify a significant pattern in weekly U.S. stock returns for lottery-like stocks, defined by high recent maximum daily returns (MAX). Specifically, high-MAX stocks that were past 1-week losers (or winners) exhibit notably positive (or negative) returns in the following week. Applying a short-term reversal strategy to high-MAX stocks generates an average weekly return of 1.66%, significantly outperforming the 0.65% return from the same strategy applied to low-MAX stocks. This result remains robust even after controlling for market microstructure biases and survives a series of robustness tests. Interestingly, the MAX-enhanced reversal strategy proves effective only when retail order imbalance is in the highest quintile. This result holds across both value-weighted and equal-weighted portfolios, underscoring the pivotal role of retail investors. Taken together, our findings highlight a new channel through which retail investors’ preference for lottery-like payoffs amplifies their overreactions, enhancing the profitability of short-term reversal strategies.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101608"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715063","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 : 2025-06-01Epub Date: 2025-05-05DOI: 10.1016/j.jempfin.2025.101617
Zhi De Khoo , Kok Haur Ng , You Beng Koh , Kooi Huat Ng
This paper proposes a long memory stochastic range (LMSR) model to investigate the persistence of range-based volatility series. The latent variable in the LMSR model is derived from the established autoregressive fractionally integrated moving average process. To estimate the model parameters, there is no closed-form solution for the latent process. Hence, the parameters of the stochastic model are estimated by applying the quasi-maximum likelihood method via the Whittle approximation. A comprehensive simulation study assesses the method’s performance, with results showing that estimated parameters are close to true values and precision improves with longer simulated time series lengths. To demonstrate the applicability of the model, we conducted empirical studies based on four financial assets, and their volatilities are estimated directly using the range-based Parkinson (PK) volatility measure. The results show evidence of long memory in these volatility series using the rescaled range and Geweke-Porter-Hudak methods. We fit the resulting PK volatility estimates to the LMSR model and other competing volatility models, and their modelling performances are compared. Results indicate that all LMSR models outperform competitors according to the log-likelihood and Akaike information criterion as well as out-of-sample loss functions. Additionally, the estimated parameters of these LMSR models confirm the presence of long memory, while competing short memory models struggle to capture the persistent nature of volatility in financial markets.
{"title":"Forecasting financial volatility: An approach based on Parkinson volatility measure with long memory stochastic range model","authors":"Zhi De Khoo , Kok Haur Ng , You Beng Koh , Kooi Huat Ng","doi":"10.1016/j.jempfin.2025.101617","DOIUrl":"10.1016/j.jempfin.2025.101617","url":null,"abstract":"<div><div>This paper proposes a long memory stochastic range (LMSR) model to investigate the persistence of range-based volatility series. The latent variable in the LMSR model is derived from the established autoregressive fractionally integrated moving average process. To estimate the model parameters, there is no closed-form solution for the latent process. Hence, the parameters of the stochastic model are estimated by applying the quasi-maximum likelihood method via the Whittle approximation. A comprehensive simulation study assesses the method’s performance, with results showing that estimated parameters are close to true values and precision improves with longer simulated time series lengths. To demonstrate the applicability of the model, we conducted empirical studies based on four financial assets, and their volatilities are estimated directly using the range-based Parkinson (PK) volatility measure. The results show evidence of long memory in these volatility series using the rescaled range and Geweke-Porter-Hudak methods. We fit the resulting PK volatility estimates to the LMSR model and other competing volatility models, and their modelling performances are compared. Results indicate that all LMSR models outperform competitors according to the log-likelihood and Akaike information criterion as well as out-of-sample loss functions. Additionally, the estimated parameters of these LMSR models confirm the presence of long memory, while competing short memory models struggle to capture the persistent nature of volatility in financial markets.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101617"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917903","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 : 2025-06-01Epub Date: 2025-05-07DOI: 10.1016/j.jempfin.2025.101620
John M. Maheu , Azam Shamsi Zamenjani
Measuring, modeling, and forecasting volatility are of great importance in financial applications such as asset pricing, portfolio management, and risk management. In this paper, we investigate predictability of stock market volatility by macro-finance variables in a dynamic regression framework using latent thresholding. The latent threshold models allow data-driven shrinkage of regression coefficients by collapsing them to zero for irrelevant predictor variables and allowing for time-varying nonzero coefficients when supported by the data. This is a parsimonious framework which selects what potential predictor variables should be included in the regressions and when. We extend this model to allow for stochastic volatility for realized volatility innovations and discuss Bayesian estimation methods. We apply the models to monthly S&P 500 and NASDAQ 100 volatility and find that using macro-finance variables in volatility forecasts enhances model performance statistically and economically, particularly when we allow for dynamic inclusion/exclusion of these variables.
{"title":"The role of macro-finance factors in predicting stock market volatility: A latent threshold dynamic model","authors":"John M. Maheu , Azam Shamsi Zamenjani","doi":"10.1016/j.jempfin.2025.101620","DOIUrl":"10.1016/j.jempfin.2025.101620","url":null,"abstract":"<div><div>Measuring, modeling, and forecasting volatility are of great importance in financial applications such as asset pricing, portfolio management, and risk management. In this paper, we investigate predictability of stock market volatility by macro-finance variables in a dynamic regression framework using latent thresholding. The latent threshold models allow data-driven shrinkage of regression coefficients by collapsing them to zero for irrelevant predictor variables and allowing for time-varying nonzero coefficients when supported by the data. This is a parsimonious framework which selects what potential predictor variables should be included in the regressions and when. We extend this model to allow for stochastic volatility for realized volatility innovations and discuss Bayesian estimation methods. We apply the models to monthly S&P 500 and NASDAQ 100 volatility and find that using macro-finance variables in volatility forecasts enhances model performance statistically and economically, particularly when we allow for dynamic inclusion/exclusion of these variables.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101620"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072372","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 : 2025-06-01Epub Date: 2025-04-02DOI: 10.1016/j.jempfin.2025.101613
Amrita Nain , Jie Ying , Joseph Arthur
We show that an increase in the supply of venture capital (VC) leads to a decline in the quality of firms going public. We argue that due to VC selectivity, private capital flows disproportionately to the most promising firms causing them to hold back from public issuance. Post-IPO abnormal returns indicate that the stock market does not fully incorporate this decline in quality at the time of the IPO. Our research adds to recent evidence on the negative impact of fast-growing private markets on Main Street investors.
{"title":"The rise of venture capital and IPO quality","authors":"Amrita Nain , Jie Ying , Joseph Arthur","doi":"10.1016/j.jempfin.2025.101613","DOIUrl":"10.1016/j.jempfin.2025.101613","url":null,"abstract":"<div><div>We show that an increase in the supply of venture capital (VC) leads to a decline in the quality of firms going public. We argue that due to VC selectivity, private capital flows disproportionately to the most promising firms causing them to hold back from public issuance. Post-IPO abnormal returns indicate that the stock market does not fully incorporate this decline in quality at the time of the IPO. Our research adds to recent evidence on the negative impact of fast-growing private markets on Main Street investors.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101613"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881783","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 : 2025-06-01Epub Date: 2025-04-26DOI: 10.1016/j.jempfin.2025.101621
Yunhe Li , Yu Liu , Mihail Miletkov , Tina Yang
This study examines the interplay between two major global trends—the growing role of foreign institutional ownership (FIO) due to financial liberalization and the rise of corporate social responsibility (CSR) as an investment ethos. We choose the setting of China, the world’s second-largest economy that has recently experienced substantial growth in foreign portfolio investment and increased its commitment to CSR. We document that CSR performance significantly influences the portfolio allocation decisions of certain types of FIO. Crucially, our analysis reveals that firms with a higher level of ownership by foreign institutional investors are associated with a more positive relation between CSR performance and firm value. This finding is robust to endogeneity examinations, including quasi-natural experiments and instrumental variable estimations. The finding is stronger for non-state-owned enterprises, firms with higher customer awareness, firms with more foreign directors, and firms with more frequent corporate site visits from FIO. Monitoring and advising are two likely channels through which FIO enhance the CSR-value relation. Finally, we demonstrate that FIO enhance firms’ ability to harness the power of CSR as a driver of innovation.
{"title":"Creating value through corporate social responsibility: The role of foreign institutional investors in Chinese listed firms","authors":"Yunhe Li , Yu Liu , Mihail Miletkov , Tina Yang","doi":"10.1016/j.jempfin.2025.101621","DOIUrl":"10.1016/j.jempfin.2025.101621","url":null,"abstract":"<div><div>This study examines the interplay between two major global trends—the growing role of foreign institutional ownership (FIO) due to financial liberalization and the rise of corporate social responsibility (CSR) as an investment ethos. We choose the setting of China, the world’s second-largest economy that has recently experienced substantial growth in foreign portfolio investment and increased its commitment to CSR. We document that CSR performance significantly influences the portfolio allocation decisions of certain types of FIO. Crucially, our analysis reveals that firms with a higher level of ownership by foreign institutional investors are associated with a more positive relation between CSR performance and firm value. This finding is robust to endogeneity examinations, including quasi-natural experiments and instrumental variable estimations. The finding is stronger for non-state-owned enterprises, firms with higher customer awareness, firms with more foreign directors, and firms with more frequent corporate site visits from FIO. Monitoring and advising are two likely channels through which FIO enhance the CSR-value relation. Finally, we demonstrate that FIO enhance firms’ ability to harness the power of CSR as a driver of innovation.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101621"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927581","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 : 2025-06-01Epub Date: 2025-04-17DOI: 10.1016/j.jempfin.2025.101614
Hongkang Xu
Using a distinctive measure derived from the Federal Register, this study examines the relation between regulatory fragmentation and corporate innovation. While regulatory fragmentation is commonly perceived as a barrier due to increased compliance costs and operational complexities, I find a significant positive association between regulatory fragmentation and innovation outputs, a result that remains consistent across various robustness tests. This effect is particularly pronounced in older firms, those with considerable regulatory influence, large market shares, and firms operating in similar regulatory environments. The results challenge the predominantly negative perceptions surrounding regulatory fragmentation in policy discussions, highlighting its potential to significantly enhance a firm’s innovative capabilities.
{"title":"Regulatory fragmentation and corporate innovation","authors":"Hongkang Xu","doi":"10.1016/j.jempfin.2025.101614","DOIUrl":"10.1016/j.jempfin.2025.101614","url":null,"abstract":"<div><div>Using a distinctive measure derived from the Federal Register, this study examines the relation between regulatory fragmentation and corporate innovation. While regulatory fragmentation is commonly perceived as a barrier due to increased compliance costs and operational complexities, I find a significant positive association between regulatory fragmentation and innovation outputs, a result that remains consistent across various robustness tests. This effect is particularly pronounced in older firms, those with considerable regulatory influence, large market shares, and firms operating in similar regulatory environments. The results challenge the predominantly negative perceptions surrounding regulatory fragmentation in policy discussions, highlighting its potential to significantly enhance a firm’s innovative capabilities.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"82 ","pages":"Article 101614"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860531","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}
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