Noise traders and smart money: Evidence from online searches

Traditional finance theory considers that the impact of noise traders' attention on asset prices is offset by attention from smart investors. This paper uses online search data to study the influence of noise traders and smart investors on stock returns and volatility. Adopting an original approach, we construct a proxy for smart investor attention based on investors' online search behavior provided by Wikipedia Page Traffic. We combine this new measure with a standard measure of noise traders' attention as proxied by Google Search Volume Index. We show for a sample of 87 French firms over the period 2008–2018 that only noise traders' attention influences stock returns. Noise traders' attention increases volatility by creating an extra risk that is priced into the market. Conversely, smart investors' attention decreases volatility because their presence stabilizes stock prices by reducing uncertainty. Our empirical results support a behavioral explanation of stock prices.     

Investor Pessimism and the German Stock Market: Exploring Google Search Queries

We analyze the relationship of retail investor sentiment and the German stock market by introducing four distinct investor pessimism indices (IPIs) based on selected aggregate Google search queries. We assess the predictive power of weekly changes in sentiment captured by the IPIs for contemporaneous and future DAX returns, volatility and trading volume. The indices are found to have individually varying, but overall remarkably high explanatory power. An increase in retail investor pessimism is accompanied by decreasing contemporaneous market returns and an increase in volatility and trading volume. Future returns tend to increase while future volatility and trading volume decrease. The outcome is in line with the conjecture of correction effects. Overall, the results are well in line with modern investor sentiment theory.     

Open source information,investor attention,and asset pricing

In this paper, we advocate the search frequency of stock name in Baidu Index as a novel and direct proxy for investor attention. Firstly, empirical results show that the quantified investor attention is a desired explanatory variable for abnormal return even trading volume is considered. Secondly, the Main Board is more efficient than the ChiNext and the SME Board in the view of informational efficiency. Thirdly, investor attention exhibits strong contemporary relationship with abnormal return. Fourthly, open source information can enhance the speed of information dissemination and make the market efficient.     

Changes in Investors' Market Attention and Near-Term Stock Market Returns

We use Google Search volume to track changes investors' positive and negative market attention. Our results support the hypothesis that this information reflects investors' optimistic and pessimistic anticipation and can be used to predict near-term future returns. We find that changes in negative search term volume of “market crash” and “bear market” and changes in positive search term volume “market rally” explain near-term stock returns. Changes in investors' attention are partly related to past stock market returns, implying that investors are prone to pay attention to possible price reversals. These measures of market attention are potential gauges of investor sentiment.     

Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models

This paper applies the threshold quantile autoregressive model to study stock return autocorrelations and predictability in the Chinese stock market from 2005 to 2014. The results show that the Shanghai A-share stock index has significant negative autocorrelations in the lower regime and has significant positive autocorrelations in the higher regime. It attributes that Chinese investors overreact and underreact in two different states. These results are similar when we employ individual stocks. Besides, we investigate stock return autocorrelations by different stock characteristics, including liquidity, volatility, market to book ratio and investor sentiment. The results show autocorrelations are significantly large in the middle and higher regimes of market to book ratio and volatility. Psychological biases can result into return autocorrelations by using investor sentiment proxy since autocorrelations are significantly larger in the middle and higher regime of investor sentiment. The empirical results show that predictability exists in the Chinese stock market.     

Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil

This study explores the relationship between Google search activity and the conditional volatility of oil and gold spot market returns. By aggregating the volume of queries related to the two commodity markets in the spirit of Da et al. ( 2015 ), we construct a weekly Searching Volume Index (SVI) for each market as proxy of households and investors information demand. We employ a rolling EGARCH framework to reveal how the significance of information demand has evolved through time. We find that higher information demand increases conditional volatility in gold and oil spot market returns. Information flows from Google SVI's reduce the proportion of the significant volatility asymmetry produced by negative shocks in both commodity markets. The latter is more profound in the gold market.     

Limited attention,salience of information and stock market activity

It is now widely recognized in the literature that individuals have limited attention and that salient information plays a key role in individuals choices. We analyze the salience of two sources of information for investors: firm-specific and market. Salient information on firm and market levels is captured by 52-week highs and low indicators while investor attention is filtered by Google web searches. Results show that web searches is a predictor of volume, volatility and returns, and the effects are stronger when using market information. Our findings help to better understand the sources of information that lead individuals in making investment decisions.     

投资者关注与IPO异象——来自网络搜索量的经验证据

利用谷歌趋势提供的搜索量数据,本文构建了一个投资者关注的直接衡量指标,在此基础上,运用行为金融学里投资者关注的理论,系统解释了IPO市场存在的三种异象。与文献中常见的其他解释变量相比,本文发现IPO前个股网络搜索量对于市场热销程度、首日超额收益和长期表现有更好的解释力和预测力,它可以解释首日超额收益的23%,长期累积收益率的10%以上,结果均十分显著。研究结果表明,投资者关注对资产价格有直接的影响,2009年的新股发行体制改革对首日超额收益的降低起到了明显作用。     

Daily happiness and stock returns: The case of Chinese company listed in the United States

Existing literature exclusively focuses on the association between local investor sentiment and local stock market performance. In this paper, we investigate the contemporaneous and the lead-lag relationship between local daily happiness sentiment extracted from Twitter and stock returns of cross-listed companies, i.e., the Chinese companies listed in the United States. The empirical results show that: 1) by respectively controlling for the firm capitalization, liquidity and volatility, there exists the largest skewness on the Most-happiness subgroup. (2) There exist bi-directional relationships between daily happiness sentiment and market variables, i.e., the stock return, range-based volatility and excess trading volume. (3) There are significantly positive stock returns, higher excess trading volume and higher range-based volatility around the daily happiness sentiment spike days. These findings not only suggest that there exists significant interdependence between online activities and stock market dynamics, but also provide evidence for the existence of “home bias”.     

A nonlinear Granger causality test between stock returns and investor sentiment for Chinese stock market: a wavelet-based approach

In this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns and investor sentiment into time series with different frequencies. Additionally, we test for nonlinear causal relationships based on Taylor series approximation. Our results indicate that there is a one-directional linear causality from stock returns to investor sentiment on the original series, while there is a strong bi-directional nonlinear causality between stock returns and investor sentiment at different timescales.     

Individual investor’s limited attention and IPO performance

The paper models and verifies the spread and decay of investor’s attention before listing on the stock market by using the Baidu Index as a proxy for investor attention of the frequency of searches. We find that individual investor’s attention has a significant influence on the first-day IPO return. We empirically study the allocation of investor attention among several stocks and discover that the more stocks going public on the same day, the milder and more similar their performance tends to be. This paper is the first to research IPO performance and individual attention.     

Dynamic sentiment asset pricing model

Conventional wisdom suggests that the equilibrium stock price is not affected by investor sentiment, and the equilibrium price at an early time is higher than the one at a later time. In contrast to this wisdom, we present a dynamic asset pricing model with investor sentiment and we find that investor sentiment has a significant impact on the equilibrium stock price. The equilibrium stock price, which is affected by pessimistic sentiment at time 0, may be lower than the one at time 1. Moreover, consistent with the reality stock market, our model shows that time varying sentiments can lead to various price changes. Finally, the model could offer a partial explanation for the financial anomaly of high volatility.     

Investor Attention Strategy

This article documents the motivation, the construction, and the profitability of an investment strategy based on investor attention in the options market. Using the option volume after a 1-week dormant period as a proxy for investor attention, the author shows that heightened investor attention after the dormant period has rich investment implications. A portfolio constructed on the basis of volume spike events immediately after the dormant period generates an abnormal return of 68 basis points on a monthly basis (8.16% on an annualized basis). This abnormal return is robust to risk adjustment using standard asset pricing models. The author's findings constitute strong evidence that it is profitable for outside investors to mimic attentive investors in the options market and reap economically and statistically significant profits.     

Asymmetric effect of advertising on the Chinese stock market

In this paper, we investigate whether investor attention to advertising has an asymmetric effect on Chinese stock returns by using a multivariate Markov switching model with time-varying regime transition probabilities. Using the Chinese stock market as a setting, we obtain lagged conditional volatility from generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the time-varying transition probabilities of the regime-switching process to capture changes in the market regime. Our evidence documents that the high advertising portfolio does earn higher abnormal return than the low advertising portfolio in low-volatility periods. In high-volatility periods, however, the abnormal return is insignificant when the firm increases advertising spending. Our results support the behavioural model argument that in high-volatility period, advertising information diffuses slowly due to cognitive dissonance. Thus, the effect of advertising on stock returns is asymmetric, and it shows statistical significance in low-volatility periods.     

Examining the relationship between stock return volatility and trading volume: new evidence from an emerging economy

Using daily stock return data for individual stocks from an emerging economy, this article examines the relationship between return volatility and trading volume under the theoretical postulate of the mixture of distributions hypothesis. The results suggest that the contemporaneous trading volume as a proxy for latent information arrival to the market did not contribute to the removal of significant ARCH or Generalized Autoregressive Conditional Heteroscedasticity effects that are found in stocks at the first stage of the investigation. The same holds for the lagged volume except for one case. This, perhaps, suggests that the trading volume (contemporaneous or lagged) is not adequately conveying information to induce traders’ views of the desirability of trade and, therefore, points to the need for searching for other micro and macro variables to be used as potential proxy for information arrival to the stock market of the emerging economy.     

Investor attention and currency performance: international evidence

This article investigates the relationship between investor attention measured by Google search volume index and the performance of several currencies. We find that currency performance is remarkably responsive to changes in investor attention. These impacts, generated rapidly, are present over the relatively long term, especially for emerging currencies, and are intensified during periods of high uncertainty. We also demonstrate that there is a prominent asymmetric effect for the impact of attention, as past currency performance also influences attention. Typically, past currency performance can determine the magnitude of the impact on current currency performance. Moreover, we confirm that investor attention has a predictive power for forecasting emerging currency performance in the out-of-sample analysis. Further, these forecasts generate substantial economic value in the framework of asset allocation. By contrast, statistical predictability and economic value do not exist in the currencies from developed markets. These results indicate that investor attention can alter currency performance and its predictability. More broadly, our study emphasizes the potential of employing investor attention for emerging currency performance forecasting applications.     

我国股市波动的非对称性和杠杆效应研究

本文选取2006年1月4日到2008年12月31日期间上证综合价格指数日收益率和收益波动率的数据,建立二者变量指标的GARCH模型、AGARCH模型、EGARCH模型,对我国牛熊市轮替过程中股票市场波动的非对称性和杠杆效应进行实证分析。结果发现,股改后牛熊市期间我国股票市场的波动表现出显著的长记忆性、非对称性和杠杆效应,股票市场波动性对"利好"和"利空"消息呈现出不平衡性反应,我国股票市场出现了强市恒强、弱市恒弱现象。最后,从投资者心理预期、过度反应与反应不足、投资者构成和交易机制等方面对该结论进行了分析。     

Uncertainty and the Timing of Automobile Purchases

Earlier studies have shown that lumpy investment models well characterize individual expenditures on durables, in particular automobiles. In this class of models, a higher level of uncertainty generally implies that the household should tolerate a larger imbalance between the actual stock of the durable and the target stock before adjusting it by buying and/or selling. Then, if the level of uncertainty increases, aggregate expenditures would temporarily fall. This hypothesis is tested by estimating an aggregate lumpy investment model on automobile expenditure data, using stock market volatility to proxy uncertainty. The result is that expenditures fall significantly as stock market volatility increases.     

The influence of cultural distance on the volatility of the international stock market

Along with the development of cultural dimensions and cultural distance, the influence of cultural variables on the stock market is attracting more and more attention. In this study, we propose an improved gravity model to examine the relationship between culture and the volatility of the international stock market. Firstly, based on Hofstede's cultural dimensions theory, a model of the impact of cultural dimensions on the volatility of the national stock market is presented. Secondly, cultural distance is incorporated into the extended gravity model. Then, models of the impact of cultural distance on fluctuations in the international stock market and on foreign securities investment are proposed. Finally, the results of case studies using samples of national stock market indices indicate that different cultural dimensions have different influences on the volatility of national stock markets. The smaller the cultural distance between countries, the more similar the level of volatility in those countries' stock markets. Greater cultural similarity promotes increased securities investment between countries.     

Foreign investor trading and information asymmetry: evidence from a leading emerging market

This article examines the influence of foreign investor trading on information asymmetry in the Korean stock market, a representative emerging market characterized by a high level of information asymmetry between corporate insiders and outsiders, and among investors. We find a significantly positive relationship between foreign investor trading and the consequent bid–ask spread – the latter of which is considered as a proxy for the degree of information asymmetry – on both daily and weekly bases. Our results indicate that active foreign investor trading tends to exacerbate informational variation.