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学者姓名：唐勇
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Abstract ：
数字中国建设峰会是否会对资本市场形成冲击,尚缺乏可靠的经验证据.文章采用2010-2021年我国A股上市公司参展数字峰会的数据,结合渐进双重差分模型,分析企业参展数字峰会能否改善资本市场的股票流动性.研究发现:数字峰会提高了股票流动性,在进行内生性及稳健性检验后结论仍然成立;机制检验表明,提高股票交易强度、提升分析师关注程度以及增强股吧活跃度是数字峰会产生溢出效应的主要途径;经进一步的异质性分析发现,数字峰会的溢出效应在信息环境较差的企业中更为显著,而且企业多次参展数字峰会更能促进股票流动性;在经济后果方面,发现数字峰会提高股票流动性具有缓解企业融资约束的作用.
Keyword ：
数字峰会 数字峰会 溢出效应 溢出效应 股票流动性 股票流动性
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| GB/T 7714 | 朱康 , 唐勇 . 数字峰会的溢出效应:基于股票流动性的研究 [J]. | 华东经济管理 , 2024 , 38 (1) : 79-88 . |
| MLA | 朱康 等. "数字峰会的溢出效应:基于股票流动性的研究" . | 华东经济管理 38 . 1 (2024) : 79-88 . |
| APA | 朱康 , 唐勇 . 数字峰会的溢出效应:基于股票流动性的研究 . | 华东经济管理 , 2024 , 38 (1) , 79-88 . |
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数字经济全球化时代，跨境数据流动成为促进经济高质量发展的新动能，催生各地区构建跨境数据流动规则以激活数据要素潜能。RCEP成为我国首个纳入放松跨境数据流动和限制数据本地化条款的区域贸易协议，标志着我国在跨境数据流动规则制定上放松了对数据跨境流转的限制。然而政策层面上支持数据跨境流动是否有利于促进我国制造业创新发展，当前学者鲜有涉猎。本文以RCEP的签订为准自然实验，结合广义双重差分模型，从企业合规成本和生产效率两个渠道量化跨境数据流动对我国制造业企业创新能力的影响。研究发现：跨境数据流动显著提高了我国制造业创新产出和创新效率；异质性分析发现跨境数据流动对企业实质性创新的影响大于策略性创新，而管理层拥有IT背景、企业数字化转型更有助于促进跨境数据流动的创新溢出；机制分析表明跨境数据流动通过提高企业生产效率来增强企业创新产出和效率，但是通过企业合规成本渠道只能提高创新产出而不能提高企业创新效率。本文首次利用广义双重差分方法测算跨境数据流动的创新溢出效应，为我国未来制定更高标准的数据流动规则以推动企业科技创新提供政策参考。
Keyword ：
RCEP RCEP 创新产出 创新产出 创新效率 创新效率 合规成本 合规成本 生产效率 生产效率 跨境数据流动 跨境数据流动
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| GB/T 7714 | 朱康 , 唐勇 , 刘恬恺 . 跨境数据流动对制造业创新能力的影响 [J]. | 工业技术经济 , 2024 , 43 (04) : 67-77 . |
| MLA | 朱康 等. "跨境数据流动对制造业创新能力的影响" . | 工业技术经济 43 . 04 (2024) : 67-77 . |
| APA | 朱康 , 唐勇 , 刘恬恺 . 跨境数据流动对制造业创新能力的影响 . | 工业技术经济 , 2024 , 43 (04) , 67-77 . |
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随着数据规模的不断扩大,数据质量问题不断涌现.数据质量究竟如何评估才能合理体现数据实际适用情况,适应数据要素市场化需要,为数据资产定价、估值提供合适的评估方法.本文从应用场景出发,构建数据质量指标体系,根据指标累计权重剔除低影响的指标.在简单比率法的基础上,构建异质偏好效用函数对不同维度的数据质量进行评估,并采用组合赋权法确定权重,计算综合评估得分.最后以我国碳试点市场公布的交易数据为例进行实证分析,结合CNN-LSTM模型估计数据效用,降低评估过程中的主观性.研究结果表明,在数据应用过程中,数据使用者对不同维度的数据质量问题具有不同的容忍度,即存在异质风险偏好.该方法能够有效反馈数据真实适用程度,为数据资产价值评估提供依据.本文立足于数据可用性,结合效用提出非线性的数据质量评估方法,以期为数据质量评估提供新思路.
Keyword ：
CNN-LSTM CNN-LSTM 效用函数 效用函数 数据质量 数据质量 组合赋权 组合赋权 综合评估 综合评估
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| GB/T 7714 | 唐勇 , 李东鹏 , 林娟娟 . 基于效用的数据质量综合评估方法探讨 [J]. | 财会月刊 , 2024 , 45 (16) : 110-116 . |
| MLA | 唐勇 等. "基于效用的数据质量综合评估方法探讨" . | 财会月刊 45 . 16 (2024) : 110-116 . |
| APA | 唐勇 , 李东鹏 , 林娟娟 . 基于效用的数据质量综合评估方法探讨 . | 财会月刊 , 2024 , 45 (16) , 110-116 . |
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The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor's 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices.
Keyword ：
Attention networks Attention networks Deep learning Deep learning Sentiment analysis Sentiment analysis Stock market prediction Stock market prediction Time series forecast Time series forecast
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| GB/T 7714 | Liao, Honglin , Huang, Jiacheng , Tang, Yong . LEET: stock market forecast with long-term emotional change enhanced temporal model [J]. | PEERJ COMPUTER SCIENCE , 2024 , 10 . |
| MLA | Liao, Honglin 等. "LEET: stock market forecast with long-term emotional change enhanced temporal model" . | PEERJ COMPUTER SCIENCE 10 (2024) . |
| APA | Liao, Honglin , Huang, Jiacheng , Tang, Yong . LEET: stock market forecast with long-term emotional change enhanced temporal model . | PEERJ COMPUTER SCIENCE , 2024 , 10 . |
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以2013—2021年我国A股上市公司为研究样本，将数据交易所设立看作准自然实验，结合渐进双重差分模型，实证探究数据要素配置对地区企业审计收费的影响。研究发现：第一，以数据交易所设立为表征的数据要素配置显著降低了地区企业的审计收费，在进行内生性及稳健性检验后结论依然成立。第二，现阶段，数据要素配置可以通过降低审计资源投入和缓解审计风险感知两条渠道来抑制企业审计收费。第三，数据要素配置降低企业审计收费的作用存在异质性，表现为国有企业、高科技企业以及数字化转型程度较高企业会削弱数据要素配置对企业审计收费的降低作用。研究验证了数据要素市场化在缓解微观企业经营成本中的重要作用，为数据要素进一步深化改革以赋能企业高质量发展提供了经验参考。
Keyword ：
企业数字化 企业数字化 审计收费 审计收费 审计资源投入 审计资源投入 审计风险 审计风险 数字经济 数字经济 数据交易 数据交易 数据要素配置 数据要素配置
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| GB/T 7714 | 唐勇 , 朱康 , 刘恬恺 . 数据要素配置的微观经济效应：基于审计收费视角 [J]. | 南京审计大学学报 , 2024 , 21 (05) : 102-111 . |
| MLA | 唐勇 等. "数据要素配置的微观经济效应：基于审计收费视角" . | 南京审计大学学报 21 . 05 (2024) : 102-111 . |
| APA | 唐勇 , 朱康 , 刘恬恺 . 数据要素配置的微观经济效应：基于审计收费视角 . | 南京审计大学学报 , 2024 , 21 (05) , 102-111 . |
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The circular economy (CE) has acquired significant interest for its potential to contribute to sustainable development (SD). The present study utilizes empirical methodology, specifically panel data analysis, to examine the distinct effects and outcomes of the circular economy and its associated factors within a unified framework. The focus is on the G20 countries from 2008 to 2021. We evaluated the influence of various CE value sources (renewable energy consumption, composting rate, repair services availability, recycling rate) and a factor-analysis-derived measure of the CE on economic, environmental, and social aspects of SD. The objective was to assess the distinct effects and outcomes of CE and its components in a unified framework-the analysis utilized panel data from G20 countries from 2008 to 2021. Our findings show a substantial influence of CE in achieving SD, with positive implications for the economy, environment, and society. However, the impact of each CE value source on the SD dimensions shows variation. While renewable energy consumption (RENEC) and composting rate (CR) lessen environmental impact, recycling rate (RR) shows no significant effect, and repair services availability (RSA) increases the Ecological Footprint (EFP). Notably, RSA is the sole CE component, showing a positive economic impact at the national level. Additionally, RENEC, RSA, and RR contribute to reducing the inflation rate (INFR). Policymakers should undertake detailed impact assessments to develop effective, tailored strategies based on each country's unique goals. The findings of this study have important policy implications, particularly in terms of emphasizing targeted strategies for implementing CE practices to achieve sustainable development.
Keyword ：
Circular economy Circular economy Ecological footprint Ecological footprint G20 economies G20 economies Inflation rate Inflation rate Renewable energy consumption Renewable energy consumption
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| GB/T 7714 | Cai, Zhanpeng , Tang, Yong , Lin, Juanjuan . Exploring the impact of circular economy practices on ecological footprint, inflation rate, and renewable energy consumption: evidence from G20 economies [J]. | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2024 . |
| MLA | Cai, Zhanpeng 等. "Exploring the impact of circular economy practices on ecological footprint, inflation rate, and renewable energy consumption: evidence from G20 economies" . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2024) . |
| APA | Cai, Zhanpeng , Tang, Yong , Lin, Juanjuan . Exploring the impact of circular economy practices on ecological footprint, inflation rate, and renewable energy consumption: evidence from G20 economies . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2024 . |
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A novel network with Wavelet denoising-GARCHSK and Mixed CoVaR method is proposed to construct full-sample and dynamic networks for investigating the risk spillover effects across international crude oil and Chinese stock sectors before and after the COVID-19 outbreak. The empirical results denote that the total bidirectional oil-sector risk spillover effects increase rapidly after the COVID-19 outbreak. Interestingly, sectors shift from net risk receivers to net risk contributors in the oil-sector risk transfer effects during the pandemic period. Second, unlike the pre-COVID-19 period, Shanghai crude (SC) replaces Brent as the largest oil risk transmitter to stocks during the COVID-19 period. Third, there are notable sectoral features in the oil-sector risk spillovers, which differ across different periods. After the burst, Energy has an incredibly weak connection with crude oil, while the sectors, which oil products are input for, become close with crude oil. Far more surprising is that the petroleum-independent sectors have increasing closer risk transfer effects with crude, even becoming the largest risk contributors to oil, after that. Finally, the oil-sector relationships during the same period are time-varying but stable. This paper provides policymakers and investors with new method and insight into the oil-sector relationships.
Keyword ：
COVID-19 COVID-19 Crude oil Crude oil network with Wavelet denoising-GARCHSK and mixed CoVaR network with Wavelet denoising-GARCHSK and mixed CoVaR risk spillovers risk spillovers stock sectors stock sectors
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| GB/T 7714 | Zhu, Pengfei , Tang, Yong , Lu, Tuantuan . How Connected is Crude Oil to Stock Sectors Before and After the COVID-19 Outbreak? Evidence from a Novel Network Method [J]. | FLUCTUATION AND NOISE LETTERS , 2023 , 22 (03) . |
| MLA | Zhu, Pengfei 等. "How Connected is Crude Oil to Stock Sectors Before and After the COVID-19 Outbreak? Evidence from a Novel Network Method" . | FLUCTUATION AND NOISE LETTERS 22 . 03 (2023) . |
| APA | Zhu, Pengfei , Tang, Yong , Lu, Tuantuan . How Connected is Crude Oil to Stock Sectors Before and After the COVID-19 Outbreak? Evidence from a Novel Network Method . | FLUCTUATION AND NOISE LETTERS , 2023 , 22 (03) . |
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The price-volume nexus is essential for understanding and discriminating the structure of the carbon market. The current research investigates the price-volume dependences in the European carbon market and Chinese national carbon markets through fractal methods. The results demonstrate that prices have close connections with volumes at multi-time scales in both markets, and that the price-volume correlation coefficient series in the Chinese carbon market have more intense volatility, implying that it is more difficult for Chinese investors to reach consensus on prices at different time scales than European investors. In addition, there are bidirectional price-volume transmissions in both markets. Interestingly, in most cases, the influence from volume to price is much more apparent than that from price to volume in both markets, indicating that volume acts a more important role in the price-volume transmission. Finally, the European price-volume dependence has lower risk and higher efficiency than the Chinese price-volume dependence, which is due to the fact that the European market is more mature than the Chinese market. This study has both theoretical and practical applications for policymakers and investors to make optimum decisions relating to policy formation and risk management in the carbon market.
Keyword ：
correlation coefficient correlation coefficient European and Chinese carbon markets European and Chinese carbon markets fractal methods fractal methods price-volume dependence price-volume dependence risk and efficiency risk and efficiency transmission direction transmission direction
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| GB/T 7714 | Zhu, Pengfei , Wei, Yu , Lu, Tuantuan et al. The Price-Volume Dependences in the European and Chinese Carbon Markets: New Evidence from the Fractal Analysis [J]. | FLUCTUATION AND NOISE LETTERS , 2023 , 22 (05) . |
| MLA | Zhu, Pengfei et al. "The Price-Volume Dependences in the European and Chinese Carbon Markets: New Evidence from the Fractal Analysis" . | FLUCTUATION AND NOISE LETTERS 22 . 05 (2023) . |
| APA | Zhu, Pengfei , Wei, Yu , Lu, Tuantuan , Tang, Yong , Zhang, Chenyu . The Price-Volume Dependences in the European and Chinese Carbon Markets: New Evidence from the Fractal Analysis . | FLUCTUATION AND NOISE LETTERS , 2023 , 22 (05) . |
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理论分析发现，资本市场定价偏离其内在价值会影响企业劳动投资效率，但在影响方向上存在促进还是抑制的不确定性。基此，利用2007—2019年中国A股非金融上市公司数据研究发现：实证结果支持了效率促进假说，资本市场错误估值每增加一个标准差，企业劳动投资效率将提高4.51%；机制检验表明，股价信息性和股权融资成本是两条促进作用渠道，前者表明管理者可以从股价中学习新的信息进而提高劳动投资决策效率，后者表明股价通过影响企业融资成本而提高劳动投资效率；资本市场错误估值既会抑制劳动过度投资，又会抑制劳动投资不足，对企业劳动投资效率的异质性影响表现为在股价高估、融资约束高和低壳溢价促进效应更显著。
Keyword ：
劳动投资效率 劳动投资效率 股价信息性 股价信息性 股价融资效应 股价融资效应 资本市场定价 资本市场定价 错误估值 错误估值
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| GB/T 7714 | 于然海 , 黄志刚 , 季国民 et al. 资本市场定价对企业劳动投资效率的影响研究 [J]. | 亚太经济 , 2023 , 14 (05) : 33-46 . |
| MLA | 于然海 et al. "资本市场定价对企业劳动投资效率的影响研究" . | 亚太经济 14 . 05 (2023) : 33-46 . |
| APA | 于然海 , 黄志刚 , 季国民 , 唐勇 . 资本市场定价对企业劳动投资效率的影响研究 . | 亚太经济 , 2023 , 14 (05) , 33-46 . |
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文章基于2005—2021年我国A股上市公司数据，采用多期双重差分模型，实证考察ESG评级对股价同步性的影响及作用机制。研究发现：ESG评级公布能够显著降低股价同步性，控制内生性问题等稳健性检验后结论仍成立。且上述关系在非国有企业和非重污染企业样本中更加显著。进一步的影响机制分析表明ESG评级通过降低盈余管理、吸引机构持股以及缓解环境不确定性来抑制股价同步性。结论丰富了ESG评级建设的经济后果研究，也为全面深化我国资本市场改革提供了有益启示。
Keyword ：
ESG评级 ESG评级 机构持股 机构持股 环境不确定性 环境不确定性 盈余管理 盈余管理 股价同步性 股价同步性
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| GB/T 7714 | 朱康 , 唐勇 . ESG评级如何影响股价同步性? [J]. | 财经理论研究 , 2023 , 13 (06) : 97-109 . |
| MLA | 朱康 et al. "ESG评级如何影响股价同步性?" . | 财经理论研究 13 . 06 (2023) : 97-109 . |
| APA | 朱康 , 唐勇 . ESG评级如何影响股价同步性? . | 财经理论研究 , 2023 , 13 (06) , 97-109 . |
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