Journal of Empirical Finance最新文献

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Do investors reach for yield? Evidence from corporate bond mutual fund flows 投资者追求收益吗？来自公司债券共同基金流动的证据

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-06-11 DOI: 10.1016/j.jempfin.2025.101625

Jing-Zhi Huang , Peipei Li , Ying Wang , Yuan Wang , Xiangkun Yao , Licheng Zhang

This paper investigates the reaching-for-yield behavior of corporate bond mutual fund investors by analyzing how fund flows respond to changes in interest rates. We find that investment-grade (IG) bond funds experience increased inflows following lower interest rates, while high-yield (HY) bond funds show no significant response. Bond fund investors tend to seek higher yields during periods of lower interest rates by assuming greater interest rate risk through the purchase of longer-maturity IG funds, rather than by taking on additional credit risk. Our findings are robust to potential endogeneity concerns and alternative explanations—including investors’ flight-to-safety behavior, liquidity considerations, and fund managers’ skill—indicating that fund flows are primarily driven by investors’ reaching-for-yield behavior in response to expansionary monetary policy. Overall, this study advances the understanding of monetary policy transmission and its implications for financial stability in the corporate bond market.

本文通过分析资金流对利率变化的响应，对公司债券共同基金投资者的收益率趋近行为进行了研究。我们发现，投资级（IG）债券基金在利率降低后资金流入增加，而高收益（HY）债券基金则没有明显的反应。债券基金投资者倾向于在利率较低的时期寻求更高的收益率，他们通过购买期限较长的债券基金来承担更大的利率风险，而不是承担额外的信用风险。我们的研究结果对潜在的内生性担忧和其他解释（包括投资者的避险行为、流动性考虑和基金经理的技能）是强有力的，表明资金流动主要是由投资者对扩张性货币政策的收益率行为所驱动的。总体而言，本研究促进了对货币政策传导及其对公司债券市场金融稳定影响的理解。

引用次数: 0

(In)Attention: distracted shareholders and corporate innovation 注意力：分散的股东和企业创新

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-27 DOI: 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。

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引用次数: 0

High frequency online inflation and term structure of interest rates: Evidence from China 高频在线通货膨胀与利率期限结构：来自中国的证据

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-23 DOI: 10.1016/j.jempfin.2025.101626

Tao Zhang , Ke Tang , Taoxiong Liu , Tingfeng Jiang

In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation significantly predicts the yield curve, especially the slope factor, whereas the monthly official inflation cannot predict the yield curve and is instead predicted by the yield curve factors. The mechanism analysis reveals that, due to low price stickiness, online inflation is more sensitive to short-term economic fluctuations and better reflects money market liquidity, thereby having significant predictive power for short-term interest rates and the slope factor. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.

在数字时代，网络价格具有价格粘性弱、对经济冲击高度敏感的特点，其信息价值值得更多关注。本文将高频在线通货膨胀率整合到动态Nelson-Siegel （DNS）模型中，探讨其与利率期限结构的关系。实证结果表明，每周在线通货膨胀率显著预测收益率曲线，尤其是斜率因子，而月度官方通货膨胀率不能预测收益率曲线，而是由收益率曲线因子预测。机制分析表明，由于价格粘性较低，在线通货膨胀对短期经济波动更敏感，更能反映货币市场流动性，因此对短期利率和斜率因子具有显著的预测能力。具体而言，非耐用品和工作日的在线通货膨胀对斜率因子显示出更强的预测能力。这些类别之间价格粘性的异质性解释了对收益率曲线的不同影响。

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引用次数: 0

Credit distortions in Japanese momentum 日本势头中的信贷扭曲

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-17 DOI: 10.1016/j.jempfin.2025.101615

Sharon Y. Ross

Persistent credit distortions have warped equity returns in Japan, where decades of subsidized bank credit to “zombie firms” suppressed momentum premiums. Controlling for zombies revives Japan’s momentum effect: momentum earns significant alpha after adjusting for zombies, and momentum’s expected return and Sharpe ratio triple. The zombie-adjusted factor commands a positive price of risk, becomes unspanned by other factors, and aligns more closely with international patterns. Why? Zombies depend on forbearance from their banks, and zombie losers’ outsized betas to bank returns depress momentum. Analysis of syndicated loan data confirms that firms with forbearance-prone lenders drive Japan’s persistently low momentum returns.

持续的信贷扭曲扭曲了日本的股票回报，在日本，数十年来银行对“僵尸企业”的补贴信贷抑制了动量溢价。控制僵尸恢复了日本的动量效应：在调整僵尸后，动量获得了显著的alpha，动量的预期回报和夏普比率是原来的三倍。僵尸调整后的因素具有正的风险价格，不受其他因素的影响，并且与国际模式更接近。为什么?“僵尸”依赖于银行的宽容，而“僵尸输家”与银行回报的巨大贝塔系数抑制了势头。对银团贷款数据的分析证实，有忍让倾向的贷款机构的公司推动了日本持续低迷的动量回报。

引用次数: 0

Unlocking efficiency: How capital market liberalization shapes firm productivity 解锁效率：资本市场自由化如何塑造企业生产率

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-14 DOI: 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.

本研究考察了资本市场自由化对实体经济的微观影响，利用中国市场的独特背景作为准自然实验环境。我们的分析表明，资本市场自由化正影响企业层面的生产率。我们进一步探讨了机制，并提供了实证证据，证明资本市场自由化通过提高知情交易有效性和纠正股票错误定价来提高资产定价效率。它还从四个不同的角度优化公司治理：降低代理成本、提高运营盈利能力、提高劳动生产率和提高透明度。这些因素共同有助于提高企业一级的生产率，证实了金融自由化对产品市场的细微影响。

引用次数: 0

A system of time-varying models for predictive regressions 用于预测回归的时变模型系统

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-13 DOI: 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.

本文提出了一种时变预测回归模型系统，其中引入时变自回归（TV-AR）过程来对预测器的动态建模，并采用线性控制函数方法来提高估计效率。我们采用轮廓似然估计方法来估计常数和时变系数，并提出假设检验来检验参数的稳定性。我们建立了所提估计量的渐近性质，并相应地检验了统计量。蒙特卡罗仿真结果表明，该方法在有限样本情况下效果良好。根据经验，TV-AR过程有效地近似于一组广泛的潜在预测因子的时间序列行为。此外，我们拒绝超过一半的这些预测因子的预测模型的稳定性假设。最后，线性投影方法不仅提高了估计器的效率，而且提高了样本外预测性能，在预测实验中获得了显著的效用增益。

引用次数: 0

A robust latent factor model for high-dimensional portfolio selection 高维投资组合选择的稳健潜在因素模型

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-13 DOI: 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.

投资组合选择面对的是由资产收益强相关的大量波动数据集，容易产生不稳定的投资组合权重和严重的估计误差。为了减轻这个问题，我们的工作提出了一个新的潜在因素模型，该模型配备了一个合适的鲁棒估计器来处理单元数据污染，以及一个对角主导（DD）协方差结构来解释剩余收益之间的横截面依赖性。在真实世界数据集的样本外投资组合表现方面，发现所提出的鲁棒DD模型与文献中的各种竞争对手相比具有优势。

引用次数: 0

Portfolio optimization with estimation errors—A robust linear regression approach 具有估计误差的投资组合优化——一种鲁棒线性回归方法

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-08 DOI: 10.1016/j.jempfin.2025.101619

Yilin Du , Wenfeng He , Xiaoling Mei

Covariance and precision matrices of asset returns are unknown in practice and must be estimated in minimum variance portfolio optimizations. Although a variety of estimators have been proposed that give better out-of-sample performance than the sample covariance matrix, they nevertheless contain estimation error of the type that is most likely to disrupt the optimizer. In this study, we propose a robust optimization framework to tackle the estimation error issue. Rather than the sample covariance matrix, as is the case with the existing approaches, our framework focuses on the row sums of estimates of the precision matrix, which can greatly minimize the number of unknown parameters. A robust linear regression framework is developed to tackle the estimate error by first rewriting the portfolio optimization as a least-squares regression model. Furthermore, our results on both simulated and empirical data reveal that the suggested robust portfolios are more stable and perform better out-of-sample than existing estimators in general.

资产收益的协方差和精度矩阵在实践中是未知的，在最小方差组合优化中必须对其进行估计。尽管已经提出了各种各样的估计器，它们提供比样本协方差矩阵更好的样本外性能，但它们仍然包含最有可能破坏优化器的类型的估计误差。在这项研究中，我们提出了一个鲁棒优化框架来解决估计误差问题。与现有方法的样本协方差矩阵不同，我们的框架侧重于精度矩阵估计的行和，这可以极大地减少未知参数的数量。通过将投资组合优化重写为最小二乘回归模型，开发了一个鲁棒线性回归框架来解决估计误差。此外，我们在模拟和实证数据上的结果表明，所建议的稳健投资组合比现有的估计器更稳定，并且在样本外表现更好。

引用次数: 0

The role of macro-finance factors in predicting stock market volatility: A latent threshold dynamic model 宏观金融因素在股票市场波动预测中的作用：一个潜在阈值动态模型

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-07 DOI: 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.

测量、建模和预测波动性在诸如资产定价、投资组合管理和风险管理等金融应用中非常重要。本文利用潜在阈值法在动态回归框架下研究宏观金融变量对股票市场波动的可预测性。潜在阈值模型允许数据驱动的回归系数收缩，通过将不相关的预测变量压缩为零，并在数据支持下允许时变的非零系数。这是一个简洁的框架，选择哪些潜在的预测变量应该包括在回归和何时。我们扩展了这个模型，以允许实现波动率创新的随机波动，并讨论了贝叶斯估计方法。我们将模型应用于标准普尔500指数和纳斯达克100指数的月度波动，发现在波动率预测中使用宏观金融变量可以提高模型在统计和经济上的表现，特别是当我们允许动态包含/排除这些变量时。

引用次数: 0

The economic value of equity implied volatility forecasting with machine learning 用机器学习预测股票隐含波动率的经济价值

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-05-06 DOI: 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.

我们通过比较机器学习模型的性能来评估非线性和交互效应在隐含波动率创新预测中的重要性，机器学习模型可以搜索相对于经典模型的交互效应，并在delta对冲期权收益的横截面和时间序列定价测试中衡量这些预测的经济意义。机器学习模型提供了卓越的样本外性能。由于预测变量在所有模型中都是相同的，这些性能差异可能捕捉到隐含波动率预测中非线性和交互效应的价值。我们的结果对前视偏差和模型过拟合具有鲁棒性。

引用次数: 0

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