Journal of Empirical Finance最新文献

英文中文

Improving information leadership share for measuring price discovery 提高衡量价格发现的信息领导份额

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-08-26 DOI: 10.1016/j.jempfin.2025.101638

Shulin Shen , Yixuan Zhang , Eric Zivot

We propose an improvement to the information leadership (IL) measure of price discovery of Yan and Zivot (2010), and the information leadership share (ILS) measure of Putniņš (2013). Our improved PIL and PILS measures integrate the price discovery share (PDS) of Shen et al. (2024) with the component share (CS) measure. Our improved PIL measure accurately reflects the ratio of initial responses of competing markets to a permanent shock in the presence of correlated reduced-form vector error correction model residuals, thereby substantially generalizing the IL measure for practical applications. Simulation evidence strongly supports the superiority of our improved PIL measure over a wide spectrum of existing price discovery metrics (Lien and Shrestha, 2009; Putniņš, 2013; Sultan and Zivot, 2015; Patel et al., 2020). We demonstrate the effectiveness of our improved measure by examining price discovery for various Chinese stocks cross-listed in Shanghai and Hong Kong (SH-HK) both before and after the initiation of the Shanghai-Hong Kong Stock Connect.

我们提出了对Yan和Zivot（2010）的价格发现的信息领导（IL）度量和Putniņš（2013）的信息领导份额（ILS）度量的改进。我们改进的PIL和PIL指标将Shen等人（2024）的价格发现份额（PDS）与组件份额（CS）指标相结合。我们改进的PIL测量准确地反映了在相关的简化形式矢量误差校正模型残差存在的情况下，竞争市场的初始反应与永久冲击的比率，从而在实际应用中大大推广了IL测量。模拟证据有力地支持了我们改进的PIL措施优于现有价格发现指标的广泛范围（Lien和Shrestha, 2009; Putniņš， 2013； Sultan和Zivot, 2015； Patel等人，2020）。我们通过研究沪港通启动前后在上海和香港交叉上市的各种中国股票（SH-HK）的价格发现来证明我们改进措施的有效性。

引用次数: 0

Tick size and firm financing decisions: Evidence from a natural experiment 蜱虫大小和公司融资决策：来自自然实验的证据

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-08-25 DOI: 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。

引用次数: 0

Option-implied idiosyncratic skewness and expected returns: Mind the long run 期权隐含的特殊偏度和预期回报：注意长期

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-08-13 DOI: 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 (

S k e w

) for the aggregate stock market. We find that

S k e w

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,

S k e w

outperforms the previously used financial and macroeconomic variables. Furthermore, combining information in the transitional predictors with

S k e w

can further improve the forecasting performance than using

S k e w

alone. We provide two explanations for the documented predictability. First,

S k e w

exhibits strong procyclical behavior and consistently declines ahead of economic downturns. Second,

S k e w

acts as a forward-looking signal of investor sentiment and disagreement—positive shocks to

S k e w

significantly increase both future investor sentiment and disagreement, with effects that persist over several horizons.

本文考察了月期权隐含的特殊偏度（Skew）对总股票市场的时间序列预测能力。我们发现，在1996年1月至2021年12月长达36个月的预测期内，使用样本内和样本外测试，Skew是美国股票溢价的有力预测指标。相比之下，Skew优于之前使用的金融和宏观经济变量。此外，将过渡预测器中的信息与Skew相结合可以比单独使用Skew进一步提高预测性能。我们为记录的可预测性提供了两种解释。首先，偏度表现出强烈的顺周期行为，并在经济衰退之前持续下降。其次，Skew是投资者情绪和分歧的前瞻性信号——对Skew的正面冲击会显著增加未来投资者情绪和分歧，其影响会持续几个时期。

{"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":"2025-08-13","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}

引用次数: 0

Default-probability-implied credit ratings for Chinese firms 违约概率隐含的中国企业信用评级

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-08-07 DOI: 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.

本文估计了中国企业的实时违约概率（pd），并以标准普尔评级类别的历史违约率为基准，分配了隐含的pd评级。与国内信用评级机构（DCRAs）发布的评级相比，pd隐含的评级往往更低，更精细。它们在预测违约方面优于DCRA评级，并在信贷价格发现方面提供补充信息。在信息内容方面，pd隐含评级比DCRA评级包含更丰富、更持久的现金流信息。诸如隐性政府担保和发行人支付商业模式中固有的道德风险等促成因素在提高DCRA评级方面发挥了重要作用，导致与pd隐含评级的差异更大，从而导致违约预测性能的差异。

引用次数: 0

Does a sudden breakdown in public information search impair analyst forecast accuracy? Evidence from China 公共信息搜索的突然中断是否会损害分析师预测的准确性？来自中国的证据

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-08-06 DOI: 10.1016/j.jempfin.2025.101643

Zihui Li , Lijun Ma , Min Zhang

We examine the effect of the sudden breakdown of public information search capability caused by Google’s withdrawal from mainland China on Chinese analysts’ earnings forecasts. We observe a decrease in analysts’ forecast accuracy regarding firms with foreign trade relative to those without foreign trade post-withdrawal. This decrease suggests that Google’s withdrawal has hindered analysts’ acquisition of information about firms with foreign trade, thus decreasing the quality of their earnings forecasts. We also find that the effect of this withdrawal on forecast accuracy is stronger for firms with higher business complexity and more opaque financial reporting and for analysts with weaker information processing capacity and more attention constraints. Additionally, we identify corporate site visits as an alternative information source that can compensate for the information loss caused by Google’s withdrawal and find that decreasing forecast accuracy has partially contributed to the deterioration of capital market conditions in the post-withdrawal era.

我们检验了百度退出中国大陆导致的公共信息搜索能力突然崩溃对中国分析师收益预测的影响。我们观察到，在退出后，分析师对有对外贸易的公司的预测准确性相对于没有对外贸易的公司有所下降。这一下降表明b谷歌的退出阻碍了分析师获取有关有对外贸易的公司的信息，从而降低了他们盈利预测的质量。我们还发现，对于业务复杂性较高、财务报告不透明的公司，以及信息处理能力较弱、注意力约束较多的分析师，这种退出对预测准确性的影响更强。此外，我们将企业网站访问确定为可替代的信息来源，可以弥补b谷歌退出造成的信息损失，并发现预测准确性的降低部分促成了后退出时代资本市场状况的恶化。

引用次数: 0

On the profitability of influential carry-trade strategies: Data-snooping bias and post-publication performance 有影响力的套息交易策略的盈利能力：数据窥探偏差与发表后绩效

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-07-31 DOI: 10.1016/j.jempfin.2025.101640

Po-Hsuan Hsu , Mark P. Taylor , Zigan Wang , Yan Li

This study examines whether 13 influential carry-trade strategies retain profitability after being published in the academic literature. We first implement several bootstrap methods to correct for the presence of data snooping and find that the pre-publication profitability of these strategies is not due to selection bias, demonstrating their original capacity to exploit market inefficiencies. On the other hand, their profitability has declined since their publication years. Our empirical evidence suggests that, although academic researchers may sometimes uncover market anomalies, their publication reduces inefficiencies in currency markets.

本研究考察了13种有影响力的套利交易策略在发表学术文献后是否仍能保持盈利能力。我们首先实施了几种bootstrap方法来纠正数据窥探的存在，并发现这些策略的出版前盈利能力不是由于选择偏差，证明了它们利用市场低效率的原始能力。另一方面，自出版以来，它们的盈利能力有所下降。我们的经验证据表明，尽管学术研究人员有时可能会发现市场异常，但他们的发表减少了货币市场的低效率。

引用次数: 0

Cross-market volatility forecasting with attention-based spatial–temporal graph convolutional networks 基于注意力的时空图卷积网络跨市场波动预测

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-07-29 DOI: 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.

本文运用基于注意力的时空图卷积网络模型（ASTGCN）对18个金融市场股票指数的未来波动率进行预测，提出了一个跨市场波动率预测框架。在我们的工作中，我们构建了跨市场波动网络来整合金融市场之间的相互关系以及每个市场的相应特征。ASTGCN将时空注意机制与时空卷积相结合，同时捕捉全球波动率数据的动态时空特征。与竞争模型相比，ASTGCN在多预测视野下的多元预测精度方面具有优势。我们提出的框架通过几个鲁棒性检查证明了出色的稳定性。我们还通过提取空间注意力矩阵来检验ASTGCN的训练过程，发现全球金融市场之间的相互关系在平静和动荡时期表现不同。我们的研究将金融网络的实证研究结果与深度学习领域的一种新的预测方法结合起来应用于实际。

{"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":"2025-07-29","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}

引用次数: 0

Behavioral biases, information frictions and interest rate expectations 行为偏差、信息摩擦和利率预期

IF 2.4 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-07-22 DOI: 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.

我们使用从利率期限结构推断的短期利率预期来测试几个著名的行为偏差和信息摩擦模型。我们根据获取成本对有关未来短期利率的信号进行分类，并找到对高成本信号反应过度和对低成本信号反应不足的证据，为过度自信偏见提供支持。我们表明，我们的结果不太可能受到时变风险溢价的驱动。偏差如此之大，以至于市场在所有视界上的预测误差都大于假设短期利率遵循随机游走的预测。

引用次数: 0

Public data openness and trade credit: Evidence from China 公共数据开放与贸易信用：来自中国的证据

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-06-24 DOI: 10.1016/j.jempfin.2025.101636

Xiao Li, Yuan Li, Xiaoxu Yu, Chun Yuan

Exploiting the setting of public data openness in China, we demonstrate a significant trade credit provision increase following the data platforms’ introduction. Our mechanism tests confirm that the rise is driven by enhanced suppliers’ willingness and capability. We document that suppliers with more substantial incentives to offer trade credit before establishing the data platforms experience a more pronounced increase in trade credit usage. Additionally, we examine the economic consequences of public data openness, demonstrating that it not only strengthens supply chain financing but also generates spillover benefits. The impact of public data openness on trade credit provision extends to firm sales, productivity, and supply chain efficiency, resulting in significant increases in revenues and total factor productivity, and leading to significant decreases in interest expense ratio and receivable turnover days. Our results reveal that public data openness substantially improves financial conditions and fosters growth throughout the supply chain.

利用中国公共数据开放的设置，我们证明了数据平台引入后贸易信贷提供的显着增加。我们的机制检验证实，价格上涨是由供应商意愿和能力增强所驱动的。我们的研究表明，在建立数据平台之前，有更大动机提供贸易信贷的供应商在贸易信贷使用方面的增长更为明显。此外，我们研究了公共数据开放的经济后果，证明它不仅加强了供应链融资，而且产生了溢出效益。公共数据开放对贸易信贷提供的影响延伸到企业销售额、生产率和供应链效率，导致收入和全要素生产率显著增加，并导致利息费用率和应收账款周转日显著降低。我们的研究结果表明，公共数据开放大大改善了财务状况，促进了整个供应链的增长。

引用次数: 0

Strategic implications of corporate disclosure via Twitter 通过Twitter进行企业信息披露的战略意义

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance

Pub Date : 2025-06-23 DOI: 10.1016/j.jempfin.2025.101635

Devendra Kale , Vikram Nanda , Anin Rupp

We investigate the information and strategic aspects of corporate tweets. Despite limits on message length, tweets stimulate information acquisition by investors, as indicated by post-tweet downloads from the SEC-EDGAR website. Corporations appear to be effective at leveraging tweets to enhance their information environment. Specifically, tweets are associated with reduction in firms’ earnings surprise and stock return volatility. There is a decrease in negative skewness of stock returns, suggesting a more uniform release of favorable and unfavorable news, especially in high litigation industries. These effects are more evident when the CEO has greater equity incentives and when firms are smaller and less visible.

我们调查了企业推文的信息和战略方面。尽管消息长度有限制，但推文刺激了投资者的信息获取，这一点从SEC-EDGAR网站的推文后下载量可以看出。企业似乎能够有效地利用微博来改善他们的信息环境。具体而言，推文与公司盈利意外和股票回报波动性的降低有关。股票收益负偏度下降，利好和负面消息的发布更加统一，特别是在高诉讼行业。当首席执行官拥有更大的股权激励，以及公司规模较小、知名度较低时，这些影响更为明显。

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Journal of Empirical Finance

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀