网络关注度对我国股票市场信息不对称程度的影响

以2013-2016年391支股票的360搜索指数中的投资者关注度和媒体关注度的指数作为网络关注度度量指标,同时基于股票市场交易数据采用多种信息不对称计算方法构建了信息不对称性度量指标,并进一步构造了信息不对称主成分综合指标。通过建立横截面回归模型,探究投资者和媒体关注度对我国股票市场的信息不对称程度的影响作用。实证分析及稳健性检验结果表明：投资者关注度的增加会减少知情交易及信息不透明程度,从而减少了股票市场的信息不对称程度,提高了股票市场的流动性;媒体关注度对不同的信息不对称性度量指标的影响存在着不一致性。本研究通过探索投资者关注度及媒体关注度在新兴市场中的应用,对于我国证券市场监管层制定政策以及对于普通投资者优化投资策略都具有重要的参考意义。     

媒体关注度对新股表现的影响——来自中国股票市场的证据

新闻媒体对证券投资的影响日趋重要,它会通过影响投资者的心理和行为而影响资产价格。首次公开发行的股票（IPOs）由于倍受媒体关注,从而会在上市以后的价格表现上产生一定的影响。本文选取2006年6月至2008年6月上市的246只首次公开发行的股票作为样本,以百度新闻搜索到包含股票名称的新闻数量作为媒体关注度的衡量指标,实证检验了媒体关注度对新股表现的影响。本文得出结论：媒体关注度通过影响投资者情绪,对新股短期累积超额收益产生正的影响,而对长期累积超额收益产生负的影响。同时发现,媒体关注度高的新股,其发行价格也相对较高。     

媒体为什么会报道上市公司丑闻?

本文以违规公司为样本,研究了媒体为什么会报道上市公司丑闻。研究发现,在我国资本市场,媒体发挥了一定的监督职能,一般表现为在处罚公告公布前,媒体已经深入报道了其中24.7%的样本的违规行为;但另一方面,媒体的监督却又显得比较有限,这表现在媒体自身发掘有价值新闻的数量非常有限,媒体更多的是对市场已有信息的收集、整理、加工与重新包装。进一步的研究发现,反映市场关注度的媒体覆盖率对媒体报道上市公司丑闻的行为影响显著,这说明媒体总是热衷于关注那些明星公司或热点新闻,媒体具有追求轰动效应的倾向。政府对媒体报道的影响只有在一定条件下才会成立,具体来说就是,只有终极控制人为国有时,会对媒体报道丑闻产生显著影响;在市场关注度较低的条件下,终极控制人国有的性质会对媒体报道影响显著,这是因为在市场关注度较低的条件下,政府影响媒体给其带来的声誉损失也相对较低。     

我国金融资产价格与通货膨胀的关联性检验

解答货币政策是否应干预金融资产价格的膨胀和波动这一问题的关键,就是要判断一国的金融资产价格与通货膨胀有何关联性。本文运用向量自回归模型实证考察了以股票价格为代表的金融资产价格对我国通货膨胀的影响。实证分析表明,我国股票价格的变动对产出缺口存在一定的正面影响,但是这种影响不太稳定,说明我国股票价格通过总需求渠道对未来通货膨胀产生的影响比较微弱。同时,我国股票价格的变动能引起未来CPI和WPI的同向变化,说明股票价格在一定程度上包含了我国未来通货膨胀的信息。因此,央行可以将金融资产价格作为参考指标,用以帮助判断未来的经济走势和通货膨胀变动趋势,从而充分发挥金融资产价格的指示器作用。     

我国货币流动性过剩的价格效应研究

本文通过实证方法研究了我国流动性过剩的实物资产价格效应和金融资产价格效应,发现我国的流动性过剩与一般物价、房地产价格股票价格具有很强的正相关关系,且对一般物价的影响具有滞后效应.同时,过剩的流动性对金融资产价格的影响要大于对实物资产价格的影响.     

媒体关注度与上市公司高管薪酬

本文选用了2014-2016年度上市公司的有关数据,对媒体关注度和上市公司高管薪酬的关系进行了实证研究.研究结果表明,媒体关注度对上市公司高管薪酬产生了积极影响.     

公允价值选择权运用的盈余波动效应实证研究

文章以2008-2014年中国上市商业银行数据为样本,研究公允价值选择权的运用与盈余波动的关系,发现运用公允价值选择权整体上使盈余波动显著增加,违背了会计准则的制定初衷;仅运用于金融资产与同时运用于金融资产和负债的盈余波动增加没有显著差异.不同信息环境下公允价值选择权运用的盈余波动效应的进一步研究表明,信息披露质量较高的上市商业银行盈余波动显著增加,而信息披露质量较低的上市商业银行盈余波动增加并不显著;信息披露质量低,且同时对金融资产资产和负债运用公允价值选择权的银行表现为最低的盈余波动性.文章结论支持修订的CAS37,认为对CAS22与IFRS9持续趋同应相当谨慎.     

美国量化宽松货币政策退出对我国金融资产价格的影响

随着美国经济的复苏,美联储逐渐退出了其自2008年来采取的量化宽松货币政策,美国经济进入加息周期.在美联储加息预期下,全球金融资产价格波动频繁.美国量化宽松货币政策退出对我国金融资产价格影响如何?本文以2013年11月至2016年6月美联储的持债规模和上证指数、美元兑人民币汇率中间价、一年期Shibor数据为样本,采用VAR模型实证分析了美国量化宽松货币政策退出对我国金融资产价格的影响.结果显示,美国量化宽松货币政策退出对美元兑人民币汇率中间价有显著的正向冲击,人民币具有贬值的压力;对上证综合指数有负向冲击,我国上证综合指数在美国量化宽松货币政策退出期间会降低;对一年期的Shibor有正向冲击,一年期的Shibor在美国量化宽松货币政策退出期间会上浮.     

再生资源产业投资与价格波动、行业信息冲击关系研究——以废纸为例

再生资源产业的发展已经成为政府及企业重点关注的问题,正确解读行业信息、防止再生资源价格剧烈波动、引导理性产业投资是目前亟待解决的问题。以废纸再生资源为研究对象,通过VAR模型验证了再生资源产业投资和再生资源价格、行业信息之间存在长期稳定的协整关系。产业投资受到价格波动和行业信息发布的影响显著,并与价格存在为期5个月的时滞期。然后运用PPM模型识别再生资源产业投资与价格时间序列的突变点,通过分析废纸再生资源产业投资的4个阶段,发现政策信息、行业动态和媒体信息对价格波动造成影响并间接影响产业投资,价格的理性波动对再生资源产业投资有促进作用。     

资产价格波动与金融稳定关系研究综述

目前关于资产价格波动与金融稳定关系的理论研究主要集中在:研究资产价格波动和信用扩张相互作用,进而影响金融稳定;研究资产价格波动与银行流动性相互作用,进而影响金融稳定;强调信息不对称问题在资产价格波动影响金融经济中的作用;研究资产价格波动传递未来不平衡信息,进而影响金融稳定。大部分实证研究都支持资产价格大幅度渡动、信用扩张与金融稳定之间存在显著的相关性。从引发金融不稳定的条件来看,面对资产价格的波动,良好的金融经济环境、制度环境和政策环境对于维护金融体系稳定性具有重要作用。     

Does it really pay off for investors to consider information from social media?

This paper raises the question whether investors can learn something from social media sentiment that they do not already know from (existing) financial information disclosed by companies and financial analysts. Therefore, the relationship between financial information and Refinitiv’s MarketPsych social media sentiment index is explored. The paper introduces adjusted social media sentiment, which corrects social media sentiment for the impact of financial information such as earnings surprises, analyst forecast revisions, new dividends, and 8-K filings. It turns out that adjusted social media sentiment is related to subsequent short-term stock returns. This is particularly true for stocks with negative (adjusted) sentiment. Moreover, looking at long-term holding returns the paper does not find compelling evidence for reversals suggesting that (adjusted) social media sentiment reflects information about the prospects of the firm.     

State transformation of information spillover in asset markets and effective dynamic hedging strategies

This paper aims to develop a strategy to effectively and dynamically hedge risk by considering regime transitions of spillover effects between assets. We take six assets (stocks, bonds, real estate, currency exchange, crude oil, and gold) that are commonly used to construct investment portfolios as examples and analyze asset price data between September 2002 and January 2022. In doing so, we aim to examine the information spillover of different asset prices in both bear and bull market environments to determine whether state transformation affects dynamic hedging effectiveness. Using Markov-Switching Factor-Augmented Vector Autoregression (MS-FAVAR), we construct a regimen-switching model using variables from finance and economic conditions as endogenous variables to define the state transformations of the information spillover. Empirical results reveal that the MS-FAVAR model highlights changes in information spillover during a financial crisis/economic recession. Using dynamic-weighted hedging portfolios constructed with different indicators, we find that hedging effectiveness and volatility vary depending on the state of information spillover between different asset markets and that bear markets significantly impacted hedging effectiveness. Results also show that the panic sentiment (the fear index) explains the probability of a bear market. It is suggested that the state transformation of information spillovers should be monitored periodically, and hedging portfolios should be dynamically adjusted (bear or bull market) with shifting fear sentiment.     

Big data tools for Islamic financial analysis

Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.     

资产价格波动与金融不稳定性:传导机制与政策选择

美国次贷危机之后,资产价格对经济金融稳定的影响受到广泛关注。本文首先指出,金融的不稳定性很大程度上根源于资产价格周期性的波动;然后从银行信贷、市场流动性、信息不对称以及非理性行为四个方面具体阐述了资产价格波动对金融稳定的影响机制;最后提出应对资产价格波动的政策建议,包括货币政策应该关注而非盯住资产价格、关注资产价格背后的信用扩张而非资产价格本身、监管部门应加快构建宏观审慎监管框架等。     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

Asset prices and banking distress: A macroeconomic approach

This paper links banking with asset prices in a dynamic macroeconomic model, to provide a simple characterization of financial instability. In contrast with historical bank runs, recent banking crises were driven by deteriorating bank assets. Hence, in contrast with bank run models, this paper focuses on the interaction of falling asset prices, bank losses, credit contraction and bankruptcies. This interaction can explain credit crunches, financial instability, and banking crises, either as fundamental or as self-fulfilling outcomes. The model distinguishes between macroeconomic and financial stability. Its simplicity helps understand balance sheet effects and delivers closed-form solutions without resorting to linearization. For instance, the critical threshold beyond which an asset price decline triggers financial instability can be related explicitly to the structural parameters of the economy.     

人民币汇率与我国股价、房价的联动关系研究

近年来,我国人民币汇率形成机制、股票市场和房地产市场发生了巨大变化,人民币汇率和股价、房价之间的信息传导和波动关联备受瞩目。本文采用VAR-MGARCH-BEKK模型,分析了我国人民币汇率、股价和房价之间的联动关系。研究结果表明,从波动的溢出效应来看,人民币汇率的波动率、股票价格的增长率和房地产价格的增长率之间存在非常明显的波动溢出效应;从资产价格的水平影响来看,人民币汇率与股票价格、房地产价格等国内资产价格的水平相关性较弱,而股票价格对房地产价格的影响较明显,并就该结论提出了相关的理论解释和政策建议。     

Modeling the contemporaneous duration dependence for high-frequency stock prices

This paper proposes a novel copula approach to model the contemporaneous duration dependence for high-frequency (HF) stock prices via the bivariate hazard function. This method is useful in understanding the mechanism through which the prices of financial assets jointly adjust to reflect new information. In the empirical analysis, we use the HF data on the APPLE and IBM stocks to illustrate the feasibility of our approach. In brief, the main findings are as follows: (1) there is a strong evidence of contemporaneous duration dependence between the prices of these stocks and (2) as a result the estimators of the bivariate hazard function are sensitive to the choice of copulas under our study.