) is the general prevailing attitude ofinvestorsas to anticipatedprice developmentin a market.This attitude is the accumulation of a variety offundamentalandtechnicalfactors, including price history, economic reports, seasonal factors, and national and world events.

If investors expect upward price movement in the stock market, the sentiment is said to bebullish. On the contrary, if the market sentiment isbearish, most investors expect downward price movement. Market participants who maintain a static sentiment, regardless of market conditions, are described aspermabullsandpermabearsrespectively. Market sentiment is usually considered as acontrarianindicator: what most people expect is a good thing to bet against. Market sentiment is believed to be a good predictor of market moves and a good indicator to hedge risk2, especially when it is more extreme.3Very bearish sentiment is usually followed by the market going up more than normal, and vice versa.4

Market sentiment is monitored with a variety of technical and statistical methods such as the number of advancing versus declining stocks and new highs versus new lows comparisons. A large share of the overall movement of an individual stock has been attributed to market sentiment.5The stock markets demonstration of the situation is often described asall boats float or sink with the tide, in the popularWall Streetphrasethe trend is your friend. In the last decade, investors are also known to measure market sentiment through the use ofnews analytics, which includesentiment analysison textual stories about companies and sectors.

A particular thread of scientific literature connects results frombehavioural finance, changes of investor attention on financial markets, and fundamental principles ofasset pricing: Barberiset al.(1998),6Barberis & Thaler (2003),7and Baker & Wurgler (2007).8The authors argue that behavioral patterns of retail investors have a significant impact on market returns.At leastfivemain approaches to measuring investor attentionare known today in scientific literature: financial market-based measures, survey-based sentiment indexes, textual sentiment data from specialized on-line resources, Internet search behavior, and non-economic factors.

According to thefirst approach, investor attention can be approximated with particularfinancial market-based measures. According to Gervaiset al.(2001)9and Houet al.(2009),10trading volumeis a good proxy for investor sentiment. High (low) trading volume on a particular stock leads to appreciating (depreciating) of its price. Extreme one-dayreturnsare also reported to draw investors attention (Barber & Odean (2008)11). Noise traders tend to buy (sell) stocks with high (low) returns. Whaley (2001)12and Baker & Wurgler (2007)8suggestChicago Board Options Exchange(CBOE) Volatility Index (VIX) as an alternative market sentiment measure. Credit Suisse Fear Barometer (CSFB) is based on prices of zero-premiumcollarsthat expire in three months. This index is sometimes used as an alternative to VIX index.13TheAcertus Market Sentiment Indicator(AMSI) incorporates five variables (in descending order of weight in the indicator):Price/Earnings Ratio(a measure of stock market valuations); pricemomentum(a measure of market psychology);Realized Volatility(a measure of recent historical risk); High Yield Bond Returns (a measure of credit risk); and theTED spread(a measure of systemic financial risk). Each of these factors provides a measure of market sentiment through a unique lens, and together they may offer a more robust indicator of market sentiment.14Closed-end funddiscount (the case when net asset value of a mutual fund does not equal to its market price) reported to be possible measure of investor attention (Zweig (1973)15and Leeet al.(1991)16). The studies suggest an evidence that changes in discounts of closed-end funds are highly correlated with fluctuations in investor sentiment. Brownet al.(2003)17investigate dailymutual fundflow as possible measure of investor attention.18According to Daet al.(2014),1319and Vieira (2011)20).Retail investortrades data is also reported to be able to represent investor attention (Kumar & Lee (2006)21). The study shows that retail investor transactions …are systematically correlated that is, individuals buy (or sell) stocks in concert.Initial Public Offering(IPO) of a company generate a big amount of information that can potentially be used to proxy investor sentiment. Ljungqvistet al.(2006)22and Baker & Wurgler (2007)8report IPO first-day returns and IPO volume the most promising candidates for predicting investor attention to a particular stock. It is not surprising that high investments in advertisement of a particular company results in a higher investor attention to corresponding stock (Grullonet al.(2004)23). The authors in Chemmanur & Yan (2009)24provide an evidence that …a greater amount of advertising is associated with a larger stock return in the advertising year but a smaller stock return in the year subsequent to the advertising year. Equity issues over total new issues ratio,insider tradingdata, and other financial indicators are reported in Baker & Wurgler (2007)8to be useful in investor attention measurement procedure.

All mentioned above market-based measures have a one important drawback. In particular, according to Daet al.(2014):13Although market-based measures have the advantage of being readily available at a relatively high frequency, they have the disadvantage of being the equilibrium outcome of many economic forces other than investor sentiment. In other words, one can never be sure that a particular market-based indicator was driven due to investor attention. Moreover, some indicators can work pro-cyclical. For example, a hightrading volumecan draw an investor attention. As a result, the trading volume grows even higher. This, in turn, leads to even bigger investor attention. Overall, market-based indicators are playing a very important role in measuring investor attention. However, an investor should always try to make sure that no other variables can drive the result.

Thesecond wayto proxy for investor attention can be to usesurvey-based sentiment indexes. Among most known indexes should be mentionedUniversity of Michigan Consumer Sentiment IndexThe Conference Board Consumer Confidence Index, and UBS/Gallup Index of Investor Optimism. The University of Michigan Consumer Sentiment Index is based on at least 500 telephone interviews. The survey contains fifty core questions.25The Consumer Confidence Index has ten times more respondents (5000 households). However, the survey consists of only five main questions concerning business, employment, and income conditions. The questions can be answered with only three options: positive, negative or neutral.26A sample of 1000 households with total investments equal or higher than $10,000 are interviewed to construct UBS/Gallup Index of Investor Optimism.27Mentioned above survey-based sentiment indexes were reported to be good predictors for financial market indicators (Brown & Cliff (2005)28). However, according to Daet al.(2014),13using such sentiment indexes can have significant restrictions. First, most of survey-based data sets are available at weekly or monthly frequency. At the same time, most of the alternative sentiment measures are available at daily frequency. Second, there is a little incentive for respondents to answer question in such surveys carefully and truthfully (Singer (2002)29). To sum up, survey-based sentiment indexes can be helpful in predicting financial indicators. However, the usage of such indexes has specific drawbacks and can be limited in some cases.

Under thethird direction, researchers propose to usetext miningandsentiment analysisalgorithms to extract information about investors mood from social networks, media platforms, blogs, newspaper articles, and otherrelevant sources of textual data(sometimes referred asnews analytics). A thread of publications (Barber & Odean (2008),11Dougalet al.(2012),30and Ahern & Sosyura (2015)31) report a significant influence of financial articles and sensational news on behavior of stock prices. It is also not surprising, that such popular sources of news asWall Street JournalNew York TimesorFinancial Timeshave a profound influence on the market. The strength of the impact can vary between different columnists even inside a particular journal (Dougalet al.(2012)30). Tetlock (2007)32suggests a successful measure of investors mood by counting the number of negative words in a popular Wall Street Journal column Abreast of the market. Zhanget al.(2011)33and Bollenet al.(2011)34reportTwitterto be an extremely important source of sentiment data, which helps to predict stock prices and volatility. The usual way to analyze the influence of the data from micro-blogging platforms on behavior of stock prices is to construct special mood tracking indexes. The easiest way would be to count the number of positive and negative words in each relevant tweet and construct a combined indicator based on this data. Nasseriet al.(2014)35and Xinget al.(2018)36report the predictive power ofStockTwits(Twitter-like platform specialized on exchanging trading-related opinions) data with respect to behavior of stock prices. An alternative, but more demanding, way is to engage human experts to annotate a large number of tweets with the expected stock moves, and then construct a machine learning model for prediction. The application of the event study methodology to Twitter mood shows significant correlation to cumulative abnormal returns (Sprengeret al.(2014),37Rancoet al. (2015),38Gabrovšeket al.(2017)39). Karabulut (2013)40reportsFacebookto be a good source of information about investors mood. Overall, most popular social networks, finance-related media platforms, magazines, and journals can be a valuable source of sentiment data, summarized in Peterson (2016).41However, important to notice that it is relatively more difficult to collect such type of data (in most cases a researcher needs a special software). In addition, analysis of such data can also require deepmachine learninganddata miningknowledge (Hothoet al.(2005)42). Specifically designedmachine learningarchitectures are better at processing such type of data (Xinget al.(2018)43).

Thefourth roadis an important source of information about investor attention is theInternet search behavior of households.This approach is supported by results from Simon (1955),44who concludes that people start their decision making process by gathering relevant information. Publicly available data on search volumes for most Internet search services starts from the year 2004. Since that time many authors showed the usefulness of such data in predicting investor attention and market returns (Daet al.(2014),13Preiset al.(2013),45and Curmeet al.(2014)46). Most studies are usingGoogle Trends(GT) service in order to extract search volume data and investigate investor attention. The usefulness of Internet search data was also proved based onYahoo! Corporationdata (Bordinoet al.(2012)47). The application of Internet search data gives promising results in solving different financial problems. The authors in Kristoufek (2013b)48discuss the application of GT data inportfolio diversificationproblem. Proposed in the paper diversification procedure is based on the assumption that the popularity of a particular stock in Internet queries is correlated with the riskiness of this stock. The author reports that such diversification procedure helps significantly improve portfolio returns. Daet al.(2014)13and Dimpfl & Jank (2015)49investigate a predictive power of GT data for two most popular volatility measures:realized volatility(RV) andCBOEdaily market volatility index (VIX). Both studies report positive and significant dependence between Internet search data and volatility measures. Bordinoet al.(2012)47and Preiset al.(2010)50reveal the ability of Internet search data to predict trading volumes in the US stock markets. According to Bordinoet al.(2012),47…query volumes anticipate in many cases peaks of trading by one day or more. Some researchers find the usefulness of GT data in predicting volatility onforeign currency market(Smith (2012)51). An increasingly important role of Internet search data is admitted incryptocurrency(e.g.BitCoin) prices forecasting (Kristoufek (2013a)52). Google Trends data is also reported to be a good predictor for dailymutual fundflows. Daet al.(2014)13concludes that such type of sentiment data …has significant incremental predictive power for future daily fund flow innovations of both equity and bond funds. One more promising source of Internet search data is the number of visits of finance-related Wikipedia pages (Wikipedia page statistics53) (Moatet al.(2013)54and Kristoufek (2013a)52). To sum up, the Internet search behavior of households is relatively new and promising proxy for investor attention. Such type of sentiment data does not require additional information from other sources and can be used in scientific studies independently.

Finally thefifth sourceof investor attention can also depend on somenon-economic factors. Every day many non-economic events (e.g. news, weather, health condition, etc.) influence our mood, which, in term, influence the level of ourrisk aversionand trading behavior. Edmanset al.(2007)55discuss the influence of sport events on investors trading behavior. The authors report a strong evidence of abnormally negative stock returns after losses in major soccer competitions. The loss effect is also valid after international cricket, rugby, and basketball games. Kaplanski & Levy (2010)56investigate the influence of bad news (aviation disasters) on stock prices. The authors conclude that a bad piece of news (e.g. about aviation disaster) can cause significant drop in stock returns (especially for small and risky stocks). The evidence that the number of sunlight minutes in a particular day influence the behavior of a trader is presented in Akhtari (2011)57and Hirshleifer & Shumway (2003).58The authors conclude that the sunshine effect is statistically significant and robust to different model specifications. The influence of temperature on stock returns is discussed in Cao & Wei (2005).59According to the results in the mentioned study, there is a negative dependence between temperature and stock returns on the whole range of temperature (i.e. the returns are higher when the weather is cold). Aseasonal affective disorder(SAD) is also known to be a predictor of investors mood (Kamstraet al.(2003)60). This is an expected result because SAD incorporates the information about weather conditions. Some researchers go even further and reveal the dependence betweenlunar phasesand stock market returns (Yuanet al.(2006)61). According to Dichev & Janes (2001):62…returns in the 15 days around new moon dates are about double the returns in the 15 days around full moon dates. Even geomagnetic activity is reported to have an influence (negatively correlated) on stock returns (C. Robotti (2003)63). To sum up, non-economic events have a significant influence on traders behavior. An investor would expect high market returns on a sunny, but cool day, fifteen days around a new moon, with no significant geomagnetic activity, preferably the day after a victory on a significant sport event. In most cases such data should be treated as supplemental in measuring investor attention, but not as totally independent one.

Additional indicators exist to measure the sentiment specifically onForexmarkets. Though the Forex market is decentralized (not traded on a central exchange),64various retail Forex brokerage firms publish positioning ratios (similar to the Put/Call ratio) and other data regarding their own clients trading behavior.656667Since most retail currency traders are unsuccessful,68measures of Forex market sentiment are typically used ascontrarianindicators.69Some researchers report Internet search data (e.g.Google Trends) to be useful in predicting volatility on foreign currency markets.51Internet search data and (relevant) Wikipedia page views data are reported to be useful incryptocurrency(e.g.BitCoin) prices forecasting.52

Malandri, Lorenzo; Xing, Frank Z.; Orsenigo, Carlotta; Vercellis, Carlo; Cambria, Erik (2018-11-26). Public Mood-Driven Asset Allocation: the Importance of Financial Sentiment in Portfolio Management.

Sentiment: A Meaningful Shift For Stock Bulls? Seeking AlphaSeeking Alpha

AAII: The American Association of Individual InvestorsAmerican Association of Individual Investors.

66% influence on the overall movement of an individual stock

Barberis, Nicholas; Shleifer, Andrei; Vishny, Robert W. (1998). A Model of Investor Sentiment.

(3): 307343.doi10.1016/S0304-405X(98)00027-0.

Barberis, Nicholas; Thaler, Richard (2003-01-01). Finance, BT – Handbook of the Economics of (ed.).

. Financial Markets and Asset Pricing. 1, Part B. Elsevier. pp.10531128.doi10.1016/S1574-0102(03)01027-6ISBN

Baker, Malcolm; Wurgler, Jeffrey (2007). Investor Sentiment in the Stock Market.

Gervais, Simon; Kaniel, Ron; Mingelgrin, Dan H. (2001-06-01). The High-Volume Return Premium.

.doi10.1111/0022-1082.00349ISSN1540-6261.

Hou, Kewei; Xiong, Wei; Peng, Lin (2009-01-16). A Tale of Two Anomalies: The Implications of Investor Attention for Price and Earnings Momentum. Rochester, NY: Social Science Research Network.SSRN

Barber, Brad M.; Odean, Terrance (2008-04-01).All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors.

(2): 785818.doi10.1093/rfs/hhm079ISSN0893-9454.

Whaley, Robert E (2000-03-01).The Investor Fear Gauge.

(3): 1217.doi10.3905/jpm.2000.319728ISSN0095-4918.

Da, Zhi; Engelberg, Joseph; Gao, Pengjie (2014-10-17).The Sum of All FEARS Investor Sentiment and Asset Prices.

(1): 132.doi10.1093/rfs/hhu072ISSN0893-9454.

A New Market Sentiment Indicator. Journal of Indexes.

Zweig, Martin E. (1973). An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums on JSTOR.

(1): 6778.doi10.1111/j.1540-6261.1973.tb01346.xJSTOR2978169.

Lee, Charles; Shleifer, Andrei; Thaler, Richard vestor Sentiment and the Closed-End Fund Puzzle.

(1): 75109.doi10.1111/j.1540-6261.1991.tb03746.x.

Brown, Stephen J.; Goetzmann, William N.; Hiraki, Takato; Shirishi, Noriyoshi; Watanabe, Masahiro (February 2003). Investor Sentiment in Japanese and U.S. Daily Mutual Fund Flows.

Baker, Malcolm; Wurgler, Jeffrey (2004). Appearing And Disappearing Dividends: The Link To Catering Incentives.

(2): 271288.doi10.1016/j.jfineco.2003.08.001.

Elisabete Simões Vieira (2011-10-18). Investor sentiment and the market reaction to dividend news: European evidence.

(12): 12131245.doi10.1108/00hdl10773/6575ISSN0307-4358.

Kumar, Alok; Lee, Charles M.c. (2006-10-01). Retail Investor Sentiment and Return Comovements.

(5): 24512486.doi10.1111/j.1540-6261.2006.01063.xISSN1540-6261.

Ljungqvist, Alexander; Singh, Rajdeep; Nanda, Vikram K. (2003-11-06). Hot Markets, Investor Sentiment, and IPO Pricing. Rochester, NY: Social Science Research Network.SSRN

Grullon, Gustavo; Kanatas, George; Weston, James P. (2004-04-01).Advertising, Breadth of Ownership, and Liquidity.

(2): 439461.doi10.1093/rfs/hhg039ISSN0893-9454.

Chemmanur, Thomas J.; Yan, An (2010-01-14).Advertising, Investor Recognition, and Stock Returnsdoi10.2139/ssrn.1536753ISSN1556-5068.

Consumer Confidence Index® The Conference Board.

Brown, Gregory W.; Cliff, Michael T. (2005-01-01). Investor Sentiment and Asset Valuation.

Singer, Eleanor (2002-01-01).The Use of Incentives to Reduce Nonresponse in Household Surveys.

Dougal, Casey; Engelberg, Joseph; Garca, Diego; Parsons, Christopher A. (2012-03-01).Journalists and the Stock Market.

(3): 639679.doi10.1093/rfs/hhr133ISSN0893-9454.

Ahern, Kenneth R.; Sosyura, Denis (2015-01-24).Rumor Has It: Sensationalism in Financial Media.

Tetlock, Paul C. (2007-06-01). Giving Content to Investor Sentiment: The Role of Media in the Stock Market.

(3): 11391168.doi10.1111/j.1540-6261.2007.01232.xISSN1540-6261.

Zhang, Xue; Fuehres, Hauke; Gloor, Peter A. (2011-01-01). Predicting Stock Market Indicators Through Twitter I hope it is not as bad as I fear

. The 2nd Collaborative Innovation Networks Conference – COINs2010.

Bollen, Johan; Mao, Huina; Zeng, Xiao-Jun (2011). Twitter mood predicts the stock market.

.doi10.1016/j.jocs.2010.12.007ISSN1877-7503.

Nasseri, Alya Al; Tucker, Allan; Cesare, Sergio de (2014-10-08). Džeroski, Sašo; Panov, Panče; Kocev, Dragi; Todorovski, Ljupčo (eds.).

Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market

. Lecture Notes in Computer Science. Springer International Publishing. pp.1324.doi10.1007/978-3-319-11812-3_2ISBN

Xing, Frank Z.; Cambria, Erik; Malandri, Lorenzo; Vercellis, Carlo (2018-06-29). Discovering Bayesian Market Views for Intelligent Asset Allocation.arXiv:

Sprenger, Timm O.; Tumasjan, Andranik; Sandner, Philipp G.; Welpe, Isabell M. (2014-11-01). Tweets and Trades: the Information Content of Stock Microblogs.

(5): 926957.doi10.1111/j.1468-036x.2013.12007.xISSN1468-036X.

Ranco, Gabriele; Aleksovski, Darko; Caldarelli, Guido; Grčar, Miha; Mozetič, Igor (2015-09-21).The Effects of Twitter Sentiment on Stock Price Returns.

.Bibcode2015PLoSO..1038441Rdoi10.1371/journal.pone.0138441ISSN1932-6203PMC

Gabrovšek, Peter; Aleksovski, Darko; Mozetič, Igor; Grčar, Miha itter sentiment around the Earnings Announcement events.

.Bibcode2017PLoSO..1273151Gdoi10.1371/journal.pone.0173151ISSN1932-6203PMC

Karabulut, Yigitcan (2013-08-13). Can Facebook Predict Stock Market Activity?. Rochester, NY: Social Science Research Network.SSRN

Trading on Sentiment: The Power of Minds Over Markets

Hotho, Andreas; Nrnberger, Andreas; Paaß, Gerhard (2005-01-01). A brief survey of text mining.

LDV Forum – GLDV Journal for Computational Linguistics and Language Technology

Xing, Frank Z.; Cambria, Erik; Welsch, Roy (2018-10-15). Intelligent Asset Allocation via Market Sentiment Views.

Simon, Herbert A. (1955-01-01).A Behavioral Model of Rational Choice.

(1): 99118.doi10.2307/1884852JSTOR1884852.

Preis, Tobias; Moat, Helen Susannah; Stanley, H. Eugene (2013-04-25).Quantifying Trading Behavior in Financial Markets Using Google Trends.

: 1684.Bibcode2013NatSR…3E1684Pdoi10.1038/srep01684ISSN2045-2322PMC

Curme, Chester; Preis, Tobias; Stanley, H. Eugene; Moat, Helen Susannah (2014-08-12).Quantifying the semantics of search behavior before stock market moves.

Proceedings of the National Academy of Sciences

(32): 1160011605.Bibcode2014PNAS..11111600Cdoi10.1073/pnas.1324054111ISSN0027-8424PMC

Bordino, Ilaria; Battiston, Stefano; Caldarelli, Guido; Cristelli, Matthieu; Ukkonen, Antti; Weber, Ingmar (2012-07-19).Web Search Queries Can Predict Stock Market Volumes.

.Bibcode2012PLoSO…740014Bdoi10.1371/journal.pone.0040014ISSN1932-6203PMC

Kristoufek, Ladislav (2013-09-19).Can Google Trends search queries contribute to risk diversification?.

.Bibcode2013NatSR…3E2713Kdoi10.1038/srep02713ISSN2045-2322PMC

Dimpfl, Thomas; Jank, Stephan (2012-06-06). Can Internet Search Queries Help to Predict Stock Market Volatility?. Rochester, NY: Social Science Research Network.SSRN

Preis, Tobias; Reith, Daniel; Stanley, H. Eugene (2010-12-28). Complex dynamics of our economic life on different scales: insights from search engine query data.

Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences

(1933): 57075719.Bibcode2010RSPTA.368.5707Pdoi10.1098/rsta.2010.0284ISSN1364-503XPMID21078644.

Smith, Geoffrey Peter (2012-06-01). Google Internet search activity and volatility prediction in the market for foreign currency.

Kristoufek, Ladislav (2013-01-01).BitCoin meets Google Trends and Wikipedia: quantifying the relationship between phenomena of the Internet era.

: 3415.Bibcode2013NatSR…3E3415Kdoi10.1038/srep03415ISSN2045-2322PMC

Moat, Helen Susannah; Curme, Chester; Avakian, Adam; Kenett, Dror Y.; Stanley, H. Eugene; Preis, Tobias (2013-05-08).Quantifying Wikipedia Usage Patterns Before Stock Market Moves.

: 1801.Bibcode2013NatSR…3E1801Mdoi10.1038/srep01801ISSN2045-2322PMC

Edmans, Alex; Garca, Diego; Norli, Øyvind (2007-08-01). Sports Sentiment and Stock Returns.

.doi10.1111/j.1540-6261.2007.01262.xISSN1540-6261.

Kaplanski, Guy; Levy, Haim (2010-02-01). Sentiment and stock prices: The case of aviation disasters.

(2): 174201.doi10.1016/j.jfineco.2009.10.002.

Reassessment of the Weather Effect: Stock Prices and Wall Street Weather.

Hirshleifer, David; Shumway, Tyler (2003-01-01). Good Day Sunshine: Stock Returns and the Weather.

(3): 10091032.doi10.1111/1540-6261.00556JSTOR3094570.

Cao, Melanie; Wei, Jason (2005-06-01). Stock market returns: A note on temperature anomaly.

(6): 15591573.doi10.1016/j.jbankfin.2004.06.028.

Kamstra, Mark J.; Kramer, Lisa A.; Levi, Maurice D. (2003-10-01). Winter Blues: A SAD Stock Market Cycle. Rochester, NY: Social Science Research Network.SSRN

Zheng, Lu; Yuan, Kathy; Zhu, Qiaoqiao (2001-09-05). Are Investors Moonstruck? – Lunar Phases and Stock Returns. Rochester, NY: Social Science Research Network.SSRN

Dichev, Ilia D.; Janes, Troy D. (2001-08-01). Lunar Cycle Effects in Stock Returns. Rochester, NY: Social Science Research Network.SSRN

Robotti, Cesare; Krivelyova, Anya (2003-10-01). Playing the Field: Geomagnetic Storms and the Stock Market. Rochester, NY: Social Science Research Network.SSRN

Finberg, Ron (2014-05-18).Final Q1 2014 US Retail Forex Profitability Report.

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