1. Introduction
It has been commonly assumed that investors behave rationally. A long strand of literature provides evidence that this assumption is unrealistic and that investors behavior is, indeed, prone to psychological biases. Several studies show that taking into account psychological features in financial analysis can explain several anomalies observed in stock markets.
The present study belongs to a growing literature on the importance of behavioral aspects in explaining stock market movements. Particularly, this paper focuses on limited attention, one of the most prevalent psychological biases. Investors are limited in their ability to optimally allocate attention across various sources of information (
Kahneman 1973). Most importantly, previous studies contend that attention allocation is a key determinant of investor’s trading decisions (
Simon 1955). For instance,
Barber and Odean (
2008) find evidence that investors are net buyers of attention-grabbing stocks.
Peng and Xiong (
2006) suggest that attention could explain under-reaction and overreaction to news, which causes prices to swing away from their fundamental values. Besides,
Andrei and Hasler (
2014) argue that a high level of attention accelerates the transmission of news into stock prices, which leads to a higher level of volatility. Thus, examining investor attention can shed light on a variety of observations in stock markets. In this context, two challenging problems arise: (i) selecting an accurate measure of investor attention and (ii) determining the appropriate approach to study the link between investor attention and stock market volatility.
A rich literature highlights the importance of online search intensity in exploring the trading activity (see, e.g.,
Bank et al. 2011;
Ekinci and Bulut 2021;
Joseph et al. 2011;
Klemola et al. 2016;
Padungsaksawasdi et al. 2019, among others). Crowds of internet users daily search for specific terms through web search engines. One of the most popular search engines is Google.
1 Da et al. (
2011) argue that searching for a stock in Google provides a clear indication of investor’s interest in that stock. Interestingly, they use the Google Volume Index (GVI) to gauge the attention of retail investors. In an intriguing study,
Hamid and Heiden (
2015) note that the movements of the GVI for the term “dow” are well aligned with that of the DJIA volatility. Based on this observation, they extend the Empirical Similarity (ES) model, developed by
Lieberman (
2012), in order to examine the link between investor attention and the volatility of the DJIA index. By allowing the autoregressive coefficient to depend on the similarity between past GVI and volatility, their model is shown to improve the accuracy of volatility forecasts.
The aim of present paper is to investigate the role of investor attention in forecasting volatility for 14 international stock markets, by means of GVI. This paper extends the study of
Hamid and Heiden (
2015) in several ways. First, we employ a larger dataset and examine whether the findings of
Hamid and Heiden (
2015) hold for a broader set of international stock markets covering the geographical regions of America (Brazil, Mexico and U.S.), Europe (Belgium, France, Germany, Netherlands, Spain, Switzerland and U.K.), Asia (China, India, Japan) and Australia, over a time window of about 18 years of weekly observations spanning from 5 January 2004 to 26 November 2021. Second, we propose an augmented Empirical Similarity model, which we dub HAR-ES. Our model is based on the similarity measure between lagged GVI and three volatility components, defined over different time horizons, of the Heterogeneous Autoregressive (HAR) model (
Corsi 2009). In addition to the attractiveness of the HAR model, which has been proven to account for the main stylized facts of volatility and to achieve higher predictive performance than traditional volatility models,
Golosnoy et al. (
2014) show that combining the HAR components using the ES approach leads to systematic improvements over the HAR model. While
Golosnoy et al. (
2014) use the similarity between past values of volatility, we employ the similarity between two different variables, namely GVI and volatility. Therefore, the HAR-ES accounts not only for the dynamics of GVI and realized volatility but also the heterogeneous beliefs among investors via heterogeneous volatility components. More specifically, our model incorporates time-varying coefficients that account for the dynamics of both GVI and volatility. Thus, we expect our model to show rapid adjustment to changes in stock market volatility.
Our methodology is summarized as follows. First, we examine causal relationships between GVI and volatility by means of Granger causality test. Second, in order to investigate the sign and timing of the relationships between investor attention and volatility, we estimate a bivariate VAR model. Finally, the forecasting performance of our predictive regressions is compared by using the modified
Diebold and Mariano (
1995) test and the Model Confidence Set approach (
Hansen et al. 2011) based on noise-robust loss functions. Our main results point to the superiority of the HAR-ES model over benchmark volatility forecasting models for almost all markets. More importantly, we find that our model outperforms the ES model proposed by
Hamid and Heiden (
2015).
The rest of the paper is structured as follows.
Section 2 presents related literature.
Section 3 introduces the econometric models.
Section 4 describes the data and discusses the empirical results.
Section 5 concludes.
2. Literature Review
Prior research shows that understanding investors behavior is quintessential in financial analysis. Researchers have explored various psychological aspects and studied their impacts on the behavior of investors. One of the major psychological biases is limited attention. The psychology of attention is an active field of research in cognitive psychology. The general conclusion emerging from research in this area is that attention is a scare cognitive resource (
Kahneman 1973). A relevant strand in the literature stemming from the “
price pressure hypothesis” (
Barber and Odean 2008) states that investors do not face the same search problem when deciding whether to buy or sell stocks. Individual investors are net buyers of attention-attracting stocks (
Grullon et al. 2004).
Barber and Odean (
2008) point out that investors have to choose among a large set of stocks when buying, which involves a search activity that requires attention, whereas when winding up their positions, investors sell stocks that they already own, thereby assuming that they do not often sell short. More importantly, the authors provide evidence that attention-driven buying induces short-term positive price pressure, most markedly for retail investors.
A large number of studies suggest that examining investor attention can shed light on a variety of observations in stock markets (e.g.,
DellaVigna and Pollet 2009;
Hasler and Ornthanalai 2018;
Mondria and Quintana-Domeque 2013;
Seasholes and Wu 2007). In their study,
Peng and Xiong (
2006) showed that investors display category-learning behavior. They argue that, given the vast amount of information, investors mostly focus on both market-level and sector-level information rather than on firm-specific information. In addition,
Hou et al. (
2009) showed that a high level of attention leads to an overreaction, in which case investors are likely to buy recent winners and sell losers. Hence, they suggest that attention-driven overreaction may explain the price momentum effect. Moreover,
Hou et al. (
2009) point out that when attention decreases, investors may under-weight earnings announcement. Consequently, earnings news will not be fully integrated into prices, which leads to a stronger stock price underreaction. Thus, this lack of attention may justify post-earnings announcement drift. In the same vein,
Hirshleifer et al. (
2009) proposed the “
investor distraction hypothesis” according to which the arrival of many competing announcements distracts investor attention from earnings news, resulting in a weaker stock price reaction, lower trading volume and a stronger post-announcement drift.
Analyzing investor behavior using the Internet has gained momentum in recent years. The number of Internet users has increased massively all over the world. In this context,
Da et al. (
2011) suggest that online search intensity may reveal the attention of investors to stock markets. They examined the link between investor attention and stock returns using the GVI of ticker symbols for a sample of the Russell 3000 constituent stocks. The availability of the GVI has expanded the scope of research and has been applied in various fields of study (see, e.g.,
Carneiro and Mylonakis 2009;
Choi and Varian 2012;
Ginsberg et al. 2009;
Guzman 2011;
Vosen and Schmidt 2011;
Yang et al. 2015). In their study,
Da et al. (
2011) tested and confirmed the price pressure hypothesis. They found that a large abnormal search volume induces higher prices (i.e., positive price pressure) in the subsequent two weeks and a price reversal within the year.
Da et al. (
2011) emphasized that this proxy reflects the attention of individual investors rather than that of institutional investors, who use more advanced tools to collect information, such as Reuters and Bloomberg terminals. Within the GARCH framework,
Vlastakis and Markellos (
2012) showed that the volume of Google search queries positively affects stock market volatility.
Andrei and Hasler (
2014) found that investor attention drives future volatility, though the reverse causality is not supported. Within a VAR framework,
Vozlyublennaia (
2014) found strong evidence that investor attention affects future returns in the short-term. Nonetheless, the link between volatility and investor attention is less pronounced. In addition, past returns have a significant long-lasting impact on investor attention. The most noteworthy result to emerge from
Vozlyublennaia (
2014) is that past performance of certain market indices is a key determinant of the impact of the previous level of attention on future returns and volatility.
Klemola et al. (
2016) used the GVI to explain changes in S&P 500 index returns. They measured investor attention during up-market periods using the search frequency for the terms “bull market” and “market rally”. Additionally, they used the GVI for “bear market” and “market crash” to gauge investor attention during market downturns. They found evidence that pessimistic search terms predict lower stock returns and optimistic search terms, predict higher stock returns.
Chen (
2017) documented a negative effect of investor attention on stock returns for a sample of 67 countries. The effect was found to be more pronounced in developed countries and tends to be weaker (stronger) during low (high) sentiment periods.
Wen et al. (
2019) used the search frequency from the Baidu index as a proxy of investor attention, and documented a negative relationship between investor attention and expected stock price crash risk in China.
Though several studies have shown that investor attention significantly affects stock market movements, a very limited number of research papers have investigated its role in predicting future volatility. Using panel data regression models,
Kim et al. (
2019) showed that the predictive power of Google search is stronger than its contemporary explanatory power for both volatility and trading volume, although there exists no relation between GVI and stock returns in the Norwegian market.
Dimpfl and Jank (
2016) investigated the link between GVI and the volatility of the DJIA index. They found that augmenting the Autoregressive (AR) and the Heterogeneous Autoregressive (HAR) models with the Google component significantly improves the forecasting accuracy of volatility. However,
Hamid and Heiden (
2015) showed that simply adding the GVI variable into the HAR model worsens the fit and does not improve the forecasting power. Interestingly, they showed that the ES approach is more suitable than standard models for studying the link between volatility and investor attention. The authors conjecture that past volatility determines the impact of the previous level of investor attention on future volatility. Their model demonstrates important gains in terms of volatility forecast accuracy by using a similarity measure between RV and GVI. More recently,
Wang et al. (
2021) investigated the impact of investors attention to the COVID-19 pandemic on stock market volatility. Their findings indicate that the expected component of investor attention is more informative about the stock market dynamics than its unexpected counterpart.
To date, the methodology of
Hamid and Heiden (
2015) has only been applied to the DJIA index. The present paper aims to test the results of
Hamid and Heiden (
2015) on a broader set of stock markets. Most subtly, we provide new insights into the relationship between investor attention and stock market volatility by proposing an augmented empirical similarity model. The empirical similarity approach has been successfully applied in volatility modeling and forecasting (e.g.,
Golosnoy et al. 2014;
Hamid 2015;
Hamid and Heiden 2015).
Hamid and Heiden (
2015) showed that an AR(1) model with a time-varying coefficient that varies with the similarity between volatility and GVI is highly advantageous to examine the link between investor attention and market volatility. In the present paper, we merely combine the HAR model with the ES approach using the similarity between the GVI and volatility.
5. Conclusions
In light of recent and still growing literature on the importance of behavioral aspects in financial analysis, we scrutinize the role of investor attention in predicting stock market volatility for international equity markets, by means of GVI. First, we employ Granger causality test to examine causal relationships between GVI and Realized Volatility. Second, we estimate a VAR model in order to investigate the sign and timing of the GVI-RV relationship. Finally, we compare the predictive ability of volatility forecasting models with and without Google data. We propose the HAR-ES as a weighting average of volatility components over different time horizons, wherein weights are determined via the similarity between lagged GVI and Realized Volatility.
Our findings confirm that Google data convey useful information to the stock market. On the one hand, the volume of Google search queries Granger causes future volatility for almost all markets. On the other hand, GVI positively affects future RV in the short-term. However, this positive effect is likely to reverse in the long-run, consistently with price pressure hypothesis and generalizing earlier findings in the U.S. equity market (
Dimpfl and Jank 2016;
Hamid and Heiden 2015;
Vlastakis and Markellos 2012) to a broader universe of developed and emerging markets. The in-sample analysis reveals that the ES approach is highly suitable for volatility modeling. Interestingly, we find that combining the HAR model with the ES approach leads to promising results. Indeed, we show that the time-varying coefficients of the HAR-ES account for the dynamics of both RV and GVI, being highly dynamic during turbulent periods and relatively stable during low volatility phases. This provides additional evidence regarding the usefulness of the ES approach in terms of volatility modeling. For the group of countries including China, Spain and Mexico, Realized Volatility appears to be less affected by the past level of GVI. The foremost reason is that the proportion of individual investors is lower in this country group. Another reason could be the lower information asymmetry among investors.
Out-of-sample analysis reveals important gains in terms of volatility forecast accuracy when combining the HAR model with the ES approach using the similarity measure between GVI and RV. The HAR-ES model exhibits the best forecasting performance for most indices. Statistical tests confirm that the link between the volume of Google search queries and Realized Volatility can not be accurately depicted by linear models. More importantly, the ES model in
Hamid and Heiden (
2015) is outperformed by the HAR-ES for almost all markets.
Even though the present study has been limited to the information conveyed by retail investors attention about the aggregate stock market volatility at the weekly frequency, it has important practical implications and several avenues open for further investigation. First, tracking the behavior of abnormal attention can enrich the information set available to regulators and policy-makers. Our findings suggest that timely communication and information disclosure, by listed firms, could help dampen excess volatility triggered by noise trading. Second, given the promising predictive ability of the HAR-ES, financial institutions may utilize investors attention as an additional input to improve internal risk models in compliance with regulatory requirements. Future research could assess the economic relevance of the HAR-ES in terms of allocating tailored regulatory capital provision, thereby reducing the cost of risk management. Third, the empirical evidence presented here may provide new insights into investment decision making for investors following a volatility targeting portfolio strategy (
Bollerslev et al. 2018;
Dimpfl and Jank 2016). We conjecture that if investors learn from the impact of fluctuating attention on asset prices, there is a potential to devise profitable strategies while controlling for the targeted level of risk. Finally, it would be of interest to disentangle the effect of institutional investors attention to better assess the market environment, using alternative online search engines such as Bloomberg and Thomson Reuters which are known to be popular among sophisticated traders.