Information asymmetry,sentiment interactions,and asset price

We develop an asset pricing model with sentiment interactions between institutional and individual investors under the condition of information asymmetry. Our model considers private information and investor sentiment, two imperfections in securities markets, and integrates them into a theoretical model to investigate the role of the interaction between information asymmetry and investor sentiment in asset pricing. We show that the joint effect of private information and investor sentiment deviate the price of risky assets and efficiently explains anomalies in the stock market. Investor sentiment changes the effect of information on the equilibrium price relative to a world where all investors are completely rational. Private information changes the effect of investor sentiment on the equilibrium price in comparison with a scenario with symmetric market information. In addition, the individual investors’ learning and the disclosure of information both allow private information to be better integrated into the price and simultaneously changes the effect of investor sentiment on the equilibrium price.     

The COVID-19 pandemic uncertainty,investor sentiment,and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.     

行业资产的Black-Litterman模型配置研究——基于社交网络情绪文本挖掘算法

研究目标:构建反映行业股价走势的基于社交网络文本挖掘算法的行业投资者情绪指标，并改善嵌入行业投资者情绪指标的Black-Litterman模型对资产的配置结果。研究方法:基于社交网络文本挖掘算法度量投资者情绪，运用主成分分析法构建行业投资者情绪指标，并嵌入Black-Litterman模型中构建投资者观点矩阵，确定行业资产配置比。研究发现:基于行业投资者情绪的BL模型有效提高了资产配置的日均收益率和夏普比率。实证结果在样本外验证(除受新冠疫情影响阶段)、暴涨暴跌阶段以及经过允许卖空和交易成本调整后仍稳健，进而证实了投资者情绪对资产组合有显著影响。研究创新:基于社交网络文本挖掘算法构建投资者情绪指数，解决了仅依赖于预期收益或历史数据的预测模型无法直观揭示投资者心理认知和行为的局限性问题，从一个崭新的视角科学地解决Black-Litterman模型中投资者观点的生成问题。研究价值:扩展了Black-Litterman模型理论体系研究，并推动了行为金融理论在资产配置中的应用。     

关键核心技术竞争态势感知模型——以人工智能算力芯片为例

科技博弈背景下,关键核心技术竞争态势进行感知成为研判技术格局的重要方法。建立态势觉察、态势理解及态势投射三维模型,结合社会网络分析法,基于专利数据库对人工智能芯片竞争态势及网络特征进行深度挖掘,构建态势感知集成分析框架。研究发现,专利数量与关键专利节点美国优势较大,在资源整合、协同研发与控制垄断上中国具有相对优势,但存在技术风险与堵点。建议交叉融合构建开源平台,发挥需求侧牵引作用,加快构建更加包容审慎的国内监管体系。     

机会主义盈余管理、投资者情绪与股价崩盘风险

股价崩盘风险受到哪些因素影响一直是学者和资本市场关注的热点问题之一,本文考察了机会主义盈余管理行为如何影响股价崩盘风险。本文选取2007-2016年中国上市公司数据进行研究,结果发现:上市公司的盈余管理程度与股价崩盘风险呈显著正相关关系;相对于非机会主义,机会主义的盈余管理对股价崩盘风险的影响更大;无论是盈余管理程度、还是机会主义盈余管理行为,对股价崩盘风险的影响都随着投资者情绪高涨而加大。本文拓展了盈余管理与股价崩盘风险之间关系的研究,有助于投资者全面理解机会主义盈余管理对股价崩盘风险的影响。     

Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index

In this paper, we aim to improve the predictability of aggregate stock market volatility with industry volatilities. The empirical results show that individual industry volatilities can provide useful predictive information, while the predictive contribution is limited. We further consider the spillover index between industry volatilities and find it displays strong predictive power for stock market volatility. Based on the portfolio exercise, we find that a mean-variance investor can achieve sizeable economic gains by using volatility forecasts of the spillover index. In addition, we conduct three extended analyses and further demonstrate the superior performance of the spillover index. Also, our results show robustness to a series of alternative settings. Finally, we investigate why the spillover index performs better and answer what information it contains. The results show that the spillover index can reflect and explain investor sentiments that are related to stock market volatility.     

Does investor sentiment affect fund crashes? Evidence from Chinese open-end funds

This paper aims to detect the impact of investor sentiment on the open-end fund crashes, drawing on the open-end stock funds and partial stock funds of China for the 2009–2019 period. The results show that the rise of investor sentiment will significantly increase the risk of the open-end fund crashes, which remains valid after robustness tests. Further researches indicate that the market timing and stock selection abilities of fund managers weaken the positive impact of investor sentiment on the open-end fund crashes, and the market illiquidity promotes the positive impact of investor sentiment on the open-end fund crashes.     

Pandemic-driven financial contagion and investor behavior: Evidence from the COVID-19

This paper studies the pandemic-driven financial contagion during the COVID-19 period and the impact of investor behavior on it by constructing three types of direct behavior measurements based on Google search volumes. More specifically, using a sample of 26 major stock markets around the world during the COVID-19 pandemic, we construct a non-linear financial contagion network via a dynamic mixture copula-EVT (extreme value theory) model to quantitatively detect and measure the complex nature of pandemic-driven financial contagion. Furthermore, through constructing direct investor behavior measurements including investor attention, sentiment, and fear, we find investor behavior plays an important role in explaining pandemic-driven financial contagion. We also find that the impacts of investor behavior on the pandemic-driven financial contagion are heterogeneous under several different settings, including market conditions, market development levels, regional subsets, and contagion directions.     

A social media analysis of travel preferences and attitudes,before and during Covid-19

Covid-19 created tremendous uncertainty in the tourism industry; in this study, we use social media data to explore differences in the preferences and attitudes of tourism consumers, both before and during the pandemic. We use natural language processing (NLP) techniques to analyze over one million Reddit posts on travel-related subreddits. We investigate the preference for city and nature-oriented tourism in selected destinations; the analysis demonstrates that nature tourism gained interest during Covid-19 in destinations with rich nature resources, whereas city tourism lost interest in destinations known for city tourism. We also classify Reddit authors into two categories: conservation and openness, according to a psychological theory of personal values, and show that this is predictive, with openness associated with positive travel sentiment and low risk awareness. This points to the potential for value-based segmentation of travel consumers based on theoretically-grounded NLP analysis of social media data.     

When does attention matter? The effect of investor attention on stock market volatility around news releases

We empirically investigate how retail and institutional investor attention is related to the way stock markets process information. With a focus on 360 US stocks in the S&P 500 universe, our results show that higher retail investors’ attention around news releases increases the post-announcement stock return volatility, whereas institutional investor attention has a small but negative impact on volatility on days following news releases on average over the cross-section of companies. These findings are in line with the hypotheses that attention of retail investors slows price-adjustments to new information and attention of institutional investors results in the opposite reaction. We show that these effects are heterogeneous in the type of news and the topic of the information being released. A portfolio allocation application highlights that these results are not only statistically significant but also sizeable in economic terms and can lead to an overperformance as large as dozens of basis points.