投资者情绪对市场风险价格影响

本文利用我国证券市场统计数据研究了个体和机构投资者情绪对风险市场价格（MPR）的影响,实证结果证明了市场对波动的反应是异质性的,并且受投资者情绪变化的影响。具体来看,将投资者情绪分解成理性和非理性成分后,非理性乐观情绪的增加将导致MPR明显的下降,但理性情绪的变动不会对MPR有明显的影响。这意味着当市场投资者情绪是由基础价值变化来决定的时候,市场风险价格不会发生变化。进一步的VAR脉冲响应函数分析结果显示,非理性的乐观或悲观情绪并不受理性情绪波动的影响,这意味着非理性情绪不是由基础风险因素决定的。     

Industry herding in crypto assets

The aim of this paper is to investigate if herd behaviour is present in crypto assets at industry level. Using price information extracted from coinmarketcap.com between 29 April 2013 and 9 May 2022, we find evidence of herding and reverse herding in the crypto assets market. Concentrated periods of herding and reverse herding are particularly evident in the January 2020–April 2022 Covid period. At industry level, herding is more profound in large sectors with higher volatility. In smaller sectors where ventures are backed by ‘real assets’, very short periods of herding with marginal significance are detected. Reverse herding is present in all industries except Real Estate between June 2021 and May 2022, implying that strategies such as excessive ‘flight to quality’ or/and token picking are at play during the recent crypto crash. We also detect varying asymmetric herding at industry level. This paper further examines the factors that drive such industry herding and reverse herding in the crypto assets market, and our results show that industry concentration and investor sentiments contribute to the probability of herding/reverse herding. Our study provides further insights to the forces that drive the dispersion in crypto assets prices and contribute to the behavioural studies of the crypto market.     

创业板投资者情绪及其收益率相关性研究

本文采用向量自回归模型VAR方法分别研究了我国主板市场与创业板市场投资者情绪与收益率之间影响是系。研究发现我国主板市场中投资者情绪对收益率有显著影响,而收益率对投资者情绪无显著影响;而创业板市场表现不同,投资者情绪对收益率无显著相关影响。研究表明:出现上述现象的原因是由于两市场的投资者结构存在差异,创业板市场投资者更成熟、创业板政策及监管更为全面等原因。     

行业分析师盈利预测偏差的新解释

对于卖方行业分析师(简称分析师)盈利预测偏差的现象,本文提出一个新假说,即作为理性投资的代表者——分析师的盈利预测偏差会受到投资者情绪或噪音交易的影响,并通过我国的数据证实了该假说。首先,通过实证研究,发现国内行业分析师盈利预测存在系统性的偏差。其次,构造月度投资者情绪指数,通过非参数和参数的统计方法,针对盈利预测偏差的时间序列和面板数据两组数据,证明了情绪是分析师盈利预测偏差的一个重要因素。国际文献中对分析师偏差的绝大多数理论解释通常是基于经典金融学的框架,即假设分析师是完全理性的,其偏差主要来自利益驱动(他们供职的机构或个人利益最大化)。而本文认为情绪和利益驱动因素相结合才能更好地解释分析师预测的偏差。     

Adverse selection and reputation in a world of cheap talk

Internet message boards are inherently a world of cheap talk due to the anonymity of message authors. This paper investigates whether a pecuniary reputation system influences the adverse selection problem endemic to message boards. First, we find evidence that authors with high reputation scores are less likely to voluntarily offer a buy–hold–sell sentiment in a particular message. Second, we find that authors with no reputation at stake tend to be more bearish with their sentiment but, after controlling for selection, authors with more reputation at stake tend to be bullish in their sentiment. Third, we find that high-reputation authors tend to offer more accurate sentiments on the day their message was posted, which suggests day-trading behavior by authors, but that higher-reputation authors are no more accurate than others after the day of posting. Our results suggest that reputation, coupled with a small pecuniary reward system, can materially influence the adverse selection problem in a world of cheap talk.     

Extracting brand information from social networks: Integrating image,text, and social tagging data

Images are an essential feature of many social networking services, such as Facebook, Instagram, and Twitter. Through brand-related images, consumers communicate about brands with each other and link the brand with rich contextual and consumption experiences. However, previous articles in marketing research have concentrated on deriving brand information from textual user-generated content and have largely not considered brand-related images. The analysis of brand-related images yields at least two challenges. First, the content displayed in images is heterogeneous, and second, images rarely show what users think and feel in or about the situations displayed. To meet these challenges, this article presents a two-step approach that involves collecting, labeling, clustering, aggregating, mapping, and analyzing brand-related user-generated content. The collected data are brand-related images, caption texts, and social tags posted on Instagram. Clustering images labeled via Google Cloud Vision API enabled to identify heterogeneous contents (e.g. products) and contexts (e.g. situations) that consumers create content about. Aggregating and mapping the textual information for the resulting image clusters in the form of associative networks empowers marketers to derive meaningful insights by inferring what consumers think and feel about their brand regarding different contents and contexts.     

CEO statements in sustainability reports: Substantive information or background noise?

This paper examines the question of whether corporate sustainability reports can serve as accurate and fair representations of corporate sustainability performance. It presents the results of a sentiment analysis of CEO statements in corporate sustainability reports and corporate financial reports between 2001 and 2010. Making an analogy with corporate financial reporting it is expected that if corporate sustainability reports accurately reflect sustainability performance, then this should be reflected in the rhetoric used. The analysis shows that the rhetoric in the CEO statements of sustainability reports is indicative of impression management rather than accountability, despite increasing standardization of sustainability reporting.     

The power of sentiment: Irrational beliefs of households and consumer loan dynamics

We examine whether household sentiment can explain fluctuations in newly issued consumer loans. We construct a novel measure of household sentiment using detailed data from the harmonized consumer surveys conducted in European countries. We differentiate between rational sentiment, which mimics dynamics in macroeconomic fundamentals, and irrational sentiment, which proxies households’ optimism/pessimism on top of their rationally sourced beliefs. We show that shocks to the sentiment of households do have a measurable impact on growth of consumer loans. Specifically, we assert a significantly positive role of irrational sentiment on top of the economic fundamentals identified in the literature. Moreover, a closer examination reveals that the studied relationship is not symmetric over the business cycle — the effect of irrational sentiment is present only in periods in which a country’s output is well above its potential.     

Cyclicality of bank credit growth: Conventional vs Islamic banks in the GCC

Using a panel of 104 banks from the six Gulf Council Countries, we investigate the cyclicality of credit growth with regard to the discrepancies between Islamic banks and conventional banks. We found that Islamic banks are pro-cyclical and have higher credit growth compared to conventional banks. Indeed, the Profit and Loss Sharing (PLS) mechanism helps Islamic banks not to curb their credit growth during adverse economic conditions. We tested the role of the growth rate of market sentiment and found that positive market sentiment leads to higher bank credit growth. Furthermore, we investigate the impact of several bank-specific variables on bank credit growth and discuss to what extent diversification and the investment portfolio reshape the credit growth process.