Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ?

doi:10.1016/J.IJFORECAST.2010.11.001

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Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ?

Journal Article•DOI•

Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ?

Kissan Joseph¹, M. Babajide Wintoki¹, Zelin Zhang¹•Institutions (1)

University of Kansas¹

01 Oct 2011-International Journal of Forecasting (Elsevier)-Vol. 27, Iss: 4, pp 1116-1127

TL;DR: In this paper, the authors examined the ability of online search intensity to forecast abnormal stock returns and trading volumes and found that search intensity reliably predicts abnormal stock return and trading volume, and that the sensitivity of returns to search intensity is positively related to the difficulty of a stock being arbitraged.

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About: This article is published in International Journal of Forecasting.The article was published on 2011-10-01. It has received 330 citations till now. The article focuses on the topics: Online search & Stock (geology).

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Posted Content•

Economics of Information

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George J. Stigler

01 Jan 2011-Economic Policy

TL;DR: In this article, the authors introduce the concept of ''search'' where a buyer wanting to get a better price, is forced to question sellers, and deal with various aspects of finding the necessary information.

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Abstract: The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

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3,790 citations

Journal Article•

Investor Sentiment and the Cross-Section of Stock Returns

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Wang Ming-zhao¹•Institutions (1)

Tongji University¹

01 Jan 2009-Economic Management Journal

TL;DR: Wang et al. as mentioned in this paper investigated whether investor sentiment affects the cross-section of stock returns in China A-share market and found that the sensitivity of stock return to sentiment changes is different.

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Abstract: We investigate whether investor sentiment affects the cross-section of stock returns in China A-share market.The evidence shows that the sensitivity of stock returns to sentiment changes is different. When an index of investor sentiment takes high values,low tangible assets,high Debt-asset ratio,and non-dividend-paying stocks earn relatively higher returns,when sentiment is low,the aforementioned categories of stocks earn relatively lower returns.When sentiment is high,low-price,unprofitable and high book-market ratio stocks earn relatively higher returns and vice versa,but which is insignificant.The capitalization, volatility and institutional ownership appear to have no significant cross-section effect of investor sentiment on its characteristic Portfolio return.

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380 citations

Journal Article•DOI•

Investor attention, index performance, and return predictability.

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Nadia Vozlyublennaia¹•Institutions (1)

College of Business Administration¹

01 Apr 2014-Journal of Banking and Finance

TL;DR: In this paper, a link between the performance of several security indexes in broad investment categories and investor attention as measured by Google search probability was investigated, and it was shown that increased investor attention diminishes return predictability and improves market efficiency.

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Abstract: We investigate a link between the performance of several security indexes in broad investment categories and investor attention as measured by Google search probability. We find that there is a significant short-term change in index returns following an increase in attention. Conversely, a shock to returns leads to a long-term change in attention. Given this evidence, we hypothesize that a change in index return or the sign of its return in the past can indicate the nature of the information that investors are paying attention to. Therefore, past returns should determine the impact of attention on the future returns and volatility. Indeed, we find significant interaction effects between lagged returns and attention. This result suggests that attention can alter predictability of index returns. Specifically, we demonstrate that increased investor attention diminishes return predictability and, therefore, improves market efficiency.

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274 citations

Journal Article•DOI•

Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach

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Prosper F. Bangwayo-Skeete¹, Ryan W. Skeete•Institutions (1)

University of the West Indies¹

01 Feb 2015-Tourism Management

TL;DR: The twelve month forecasts reveal that Google Trends information offers significant benefits to forecasters, particularly in tourism, and policymakers and business practitioners especially in the Caribbean can take advantage of the forecasting capability of Google search data for their planning purposes.

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269 citations

Journal Article•DOI•

The technology and economic determinants of cryptocurrency exchange rates

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Xin Li¹, Chong Alex Wang¹•Institutions (1)

City University of Hong Kong¹

01 Mar 2017

TL;DR: The value of blockchain-based cryptos has changed little in the past year despite receiving extensive public attention, and theoretical understanding is limited regarding the value of Blockchain-based Cryptocurrencies.

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Abstract: Cryptocurrencies, such as Bitcoin, have ignited intense discussions. Despite receiving extensive public attention, theoretical understanding is limited regarding the value of blockchain-based cryptocurrencies, as expressed in their exchange rates against traditional currencies. In this paper, we conduct a theory-driven empirical study of the Bitcoin exchange rate (against USD) determination, taking into consideration both technology and economic factors. To address co-integration in a mix of stationary and non-stationary time series, we use the autoregressive distributed lag (ARDL) model with a bounds test approach in the estimation. Meanwhile, to detect potential structural changes, we estimate our empirical model on two periods separated by the closure of Mt. Gox (one of the largest Bitcoin exchange markets). According to our analysis, in the short term, the Bitcoin exchange rate adjusts to changes in economic fundamentals and market conditions. The long-term Bitcoin exchange rate is more sensitive to economic fundamentals and less sensitive to technological factors after Mt. Gox closed. We also identify a significant impact of mining technology and a decreasing significance of mining difficulty in the Bitcoin exchange price determination. We theoretically discuss the technology and economic determinants of the Bitcoin exchange rateWe use the ARDL model with bounds test to address co-integration of a mix of stationary and non-stationary time seriesWe find Bitcoin exchange rate relates more with economic fundamentals and less with technology factors as Bitcoin evolvesWe find the impact of computational capacities on Bitcoin is decreasing as technology progresses

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263 citations

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References

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Open Access

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Journal Article•DOI•

Common risk factors in the returns on stocks and bonds

[...]

Eugene F. Fama¹, Kenneth R. French¹•Institutions (1)

University of Chicago¹

01 Feb 1993-Journal of Financial Economics

TL;DR: In this article, the authors identify five common risk factors in the returns on stocks and bonds, including three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity.

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24,874 citations

"Forecasting Abnormal Stock Returns ..." refers background or methods in this paper

...Then, for firms in each volatility decile, we run regressions of the daily abnormal returns on the three factors from Fama and French (1993), the momentum factor in Carhart (1997) and our newly constructed sentiment index (SENT ) that is based on search intensity....
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...These factors have been found to explain cross-sectional differences in stock returns (see for example, Fama and French (1993) and Kothari and Warner (2008))....
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...…to better understand the impact of search intensity on financial returns, we further examine the four factors that are typically employed in the Fama and French (1993) and Carhart (1997) models of stock returns, namely, Rm −R f , SMB, HML, and UMD, along with the factor that we create from our…...
[...]
...For each portfolio, we then run regressions of daily returns on the three factors from Fama and French (1993): the excess return on the market (Rm −R f ); the return difference between a portfolio of “small” and “big” stocks (SMB) and the return difference between a portfolio of “high” and “low”…...
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...We note that this abnormal return occurs after controlling for the risk-factors employed in the Fama and French (1993) and Carhart (1997) models of stock returns....
[...]

Journal Article•DOI•

On Persistence in Mutual Fund Performance

[...]

Mark M. Carhart

01 Mar 1997-Journal of Finance

TL;DR: Using a sample free of survivor bias, this paper showed that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual fund's mean and risk-adjusted returns.

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Abstract: Using a sample free of survivor bias, I demonstrate that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual funds' mean and risk-adjusted returns Hendricks, Patel and Zeckhauser's (1993) "hot hands" result is mostly driven by the one-year momentum effect of Jegadeesh and Titman (1993), but individual funds do not earn higher returns from following the momentum strategy in stocks The only significant persistence not explained is concentrated in strong underperformance by the worst-return mutual funds The results do not support the existence of skilled or informed mutual fund portfolio managers PERSISTENCE IN MUTUAL FUND performance does not reflect superior stock-picking skill Rather, common factors in stock returns and persistent differences in mutual fund expenses and transaction costs explain almost all of the predictability in mutual fund returns Only the strong, persistent underperformance by the worst-return mutual funds remains anomalous Mutual fund persistence is well documented in the finance literature, but not well explained Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Wermers (1996) find evidence of persistence in mutual fund performance over short-term horizons of one to three years, and attribute the persistence to "hot hands" or common investment strategies Grinblatt and Titman (1992), Elton, Gruber, Das, and Hlavka (1993), and Elton, Gruber, Das, and Blake (1996) document mutual fund return predictability over longer horizons of five to ten years, and attribute this to manager differential information or stock-picking talent Contrary evidence comes from Jensen (1969), who does not find that good subsequent performance follows good past performance Carhart (1992) shows that persistence in expense ratios drives much of the long-term persistence in mutual fund performance My analysis indicates that Jegadeesh and Titman's (1993) one-year momentum in stock returns accounts for Hendricks, Patel, and Zeckhauser's (1993) hot hands effect in mutual fund performance However, funds that earn higher

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13,218 citations

"Forecasting Abnormal Stock Returns ..." refers background or methods in this paper

...Then, for firms in each volatility decile, we run regressions of the daily abnormal returns on the three factors from Fama and French (1993), the momentum factor in Carhart (1997) and our newly constructed sentiment index (SENT ) that is based on search intensity....
[...]
...…impact of search intensity on financial returns, we further examine the four factors that are typically employed in the Fama and French (1993) and Carhart (1997) models of stock returns, namely, Rm −R f , SMB, HML, and UMD, along with the factor that we create from our measure of investor…...
[...]
...α is obtained by regressing daily returns on three factors from Fama and French (1993): the excess return on the market (Rm −R f ); the return difference between a portfolio of “small” and “big” stocks (SMB) and the return difference between a portfolio of “high” and “low” book-to-market stocks (HML), augmented with a momentum factor from Carhart (1997), which is the return difference between a portfolio of stocks with high returns in the past year and a portfolio of stocks with low returns in the past year (UMD)....
[...]
...The abnormal returns are obtained from the regression of the daily time series of returns on three factors from Fama and French (1993): the excess return on the market (Rm −R f ); the return difference between a portfolio of “small” and “big” stocks (SMB) and the return difference between a portfolio of “high” and “low” book-to-market stocks (HML), augmented with a momentum factor from Carhart (1997), which is the return difference between a portfolio of stocks with high returns in the past year and a portfolio of stocks with low returns in the past year (UMD)....
[...]
...The Fama-French factors are: the excess return on the market (Rm −R f ); the return difference between a portfolio of “small” and “big” stocks (SMB) and the return difference between a portfolio of “high” and “low” book-to-market stocks (HML), augmented with a momentum factor from Carhart (1997), which is the return difference between a portfolio of stocks with high returns in the past year and a portfolio of stocks with low returns in the past year (UMD)....
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Journal Article•DOI•

Detecting influenza epidemics using search engine query data

[...]

Jeremy Ginsberg¹, Matthew H. Mohebbi¹, Rajan Patel¹, Lynnette Brammer², Mark S. Smolinski¹, Lawrence B. Brilliant¹ - Show less +2 more•Institutions (2)

Google¹, Centers for Disease Control and Prevention²

19 Feb 2009-Nature

TL;DR: A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.

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Abstract: This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3,4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.

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3,984 citations

"Forecasting Abnormal Stock Returns ..." refers background in this paper

...Ginsberg et al. (2009) find that a basket of forty-five terms related to influenza successfully predicts the proportion of patients visiting health professionals with related symptoms....
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Posted Content•

Economics of Information

[...]

George J. Stigler

01 Jan 2011-Economic Policy

...read moreread less

3,790 citations

"Forecasting Abnormal Stock Returns ..." refers background in this paper

...Indeed, such a cost-benefit perspective is the dominant paradigm that explains consumer search behavior (Stigler, 1961; Klein and Ford, 2003)....
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...Moreover, consumer search behavior is explained by an implicit cost-benefit analysis (Stigler, 1961)....
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Book Chapter•DOI•

The Economics of Information

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George J. Stigler

01 Jun 1961-Journal of Political Economy

TL;DR: In this paper, the identification of sellers and the discovery of their prices is described as an example of the role of the search for information in economic life, and the identification and discovery of prices of goods and services is discussed.

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Abstract: The identification of sellers and the discovery of their prices is given as an example of the role of the search for information in economic life.

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3,575 citations