DOCSLIB.ORG

NLP and Sentiment Driven Automated Trading

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
NLP and Sentiment Driven Automated Trading NLP and Sentiment Driven Automated Trading Atish Davda ([email protected]) Parshant Mittal ([email protected]) Faculty Advisor: Michael Kearns ([email protected]) Atish Davda NLP and Sentiment Driven Automated Trading Parshant Mittal Senior Design 2007‐08 Page 2 Abstract Movements in financial markets are directly influenced by information exchange – between a company and its owners, between the government and its citizens, between one individual and another. The channels of distributing news have expanded from the singular ticker tape in the middle of town to intra‐minute delivery to the computer via RSS feeds. With information quickly available markets are becoming increasingly efficient, as humans design intricate algorithms to continuously take advantage of any perceived mispricing in the markets (Kelly, 2007). This phenomenon, which is especially prevalent in the stock market, begs the question: is there still an active need for the human element? After all, machines are faster – given more information and better hardware, their computation power decidedly exceeds that of humans. The answer lies in the challenge of abstraction; deciding the impact of each piece of information is important and more isn’t always better (Greenwald, Jennings, & Stone, 2003) . In this project we explored the field of natural language processing and identified methods we can use to automate stock trading based on news articles. The project was implemented in three phases (see Appendix 1). The first phase included data collection from sources on the web. News articles and headlines were scraped from Yahoo! Finance; historical market data was collected from Google Finance. The data was collected for 600 small market cap stocks (SML), 400 medium market cap stocks (MID) and 500 stocks from S&P 500 index (SP500). The second phase included sentiment analysis on the first half of the dataset, in order to compute sentiments to be tested on the (out of sample) second half. In the final stage, we implemented an NLP approach to quantifying the headlines. This was done using a number of NLP packages available online, including the Stanford Lex Parser, WordNET, and General Inquirer.1 The last stage of the project comprised of developing a trading module with which we could incorporate the results of historical market, sentiment, and NLP analysis to give a Buy, Sell, or a Hold 1 Please refer to the Bibliography section for further information on these projects. Atish Davda NLP and Sentiment Driven Automated Trading Parshant Mittal Senior Design 2007‐08 Page 3 recommendation for securities under consideration. Using sentiment and NLP analysis we were able to achieve significantly improved returns. In fact we averaged a return of 4.0% over a two month period (27% annualized), while the market fell 8.7% during the same period (‐42.1% annualized). With the help of this and other metrics, we explored the value of NLP in automated trading. Related Work Given the widespread implications of introducing abstraction capability to machines, it isn’t surprising that NLP is a highly researched discipline. In fact, even in just the US there exist several groups sponsored by universities, corporations, and the government, which focus solely on improving the capabilities of current language‐processing techniques (Fallows, 2004). However, although the paradigm of examining news articles attracts a lot of academic studies, it is rather biased toward long‐term, macro news reports2; unexplored by comparison, is the realm of short‐term, firm‐specific news.3 One of the first studies specifically focused on quantifying the relationship between news releases and movements in the stock markets was conducted not too long ago (Gillam, Ahmad, & Ahmad, 2002). The challenge of predicting which news events will have what impact on the trading characteristics, such as price and volume traded of stocks still remains. While there have been recent advancements in the applications of NLP in predicting other markets (e.g. election markets), the specific role of language analysis in financial markets is unclear (Gilder & Lerman, 2007). The novelty of our project lies in applying NLP analysis to news headlines, rather than the entire article. In addition, we consider highly liquid and efficient markets. These markets present additional challenges as there is no end date and our analysis must then include a wider range of factors. One natural dimension we explored in detail was distinguishing the impact between the headline “IBM’s earnings drop” and “IBM’s earnings 2 Macro news reports include interest rate changes by central banks, announcements of inflation news, etc. 3 Firm‐specific news includes earnings reports, merger/acquisition rumors, etc. Atish Davda NLP and Sentiment Driven Automated Trading Parshant Mittal Senior Design 2007‐08 Page 4 plummet.”4 Our paper is, in part, an extension of the 2002 study “Economic News and Stock Market Correlations” which solely looked at the sign (positive or negative) of the connotation associated with words in the news articles. We have implemented a framework with the use of General Inquirer as well as our own sentiment analysis to distinguish between the emotional charges people innately give to certain words, which lead to varying degrees of influence the news has on the characteristics of the stock. Upon additional research on generic topics such as conjunctive handling, we found a good fit for such fundamental pillars of NLP (Meena & Prabhakar, 2007). Furthermore, we expanded upon this kind of study by examining syntax in addition to semantics, empirically deriving an adjustment factor to each word’s sentiment charge, depending on its use in a sentence. This second order correction helped improve accuracy of predictions, once we moved away from the naïve bag‐of‐words analysis. Building this lexicon with each word having an associated sentiment is a field of research in itself, Sentiment Analysis. There are several models for generating such a corpus; one of the fundamental models is described in the paper “Determining the Sentiment of Opinions” by Kim and Hovy (2004). The study discusses a region (news headline) around the central anchor (company of interest), which when examined as a whole, yields a positive or negative rating for the company itself (Kim & Hovy, 2004). Another approach suggests a more empirical analysis by examining vast amounts of HTML documents in order to generate a polarity score for words, described as a function of the distance of a given word from a pre‐defined, manually selected corpus (Kaji & Kitsuregawa, 2007). While it would certainly help having a sentiment list as close to perfect as possible, we focused on the use of sentiment scores, rather than determining the optimal method to calculate them. As you will read in the Technical Approach section, we adopted a combination of these two methodologies along with General Inquirer – initially, we used a discretionary method akin to the latter model, and eventually, will develop a hybrid. 4 Gilliam article titled “Economic News and Stock Market Correlation” discusses the impact of “good” versus “bad” words, but does not incorporate degrees of positive/negative sentiment associated with the word. Atish Davda NLP and Sentiment Driven Automated Trading Parshant Mittal Senior Design 2007‐08 Page 5 The project’s goal is two‐fold: one is to test whether a relationship exists between news articles and the movements in the market data of a stock; the second goal is to model this relationship, if it exists, by implementing it into a trading strategy. In regard to the former, the scope of news content can be broadly divided into two sets: news reporting on past performance, and announcements of future activity (Gillam et al., 2002). While it would be an interesting dimension to explore, this study limits itself to quantifying relationships between characteristics of news articles and relevant stock returns, regardless of the category under which the news falls. The reason for doing so is because we focus on implementing this strategy as if it were to be used in a high frequency event driven trading platform where it is often acceptable to be accurate just little over 50%. The reason hinges on consistently being right over half the time, so that the profits generated will more than account for the losses sustained due to incorrect decisions. Detailed analysis on the subject of Statistical Arbitrage has been performed, by testing various experimental trading strategies used to test predictive effects of news releases on stock movement (Hariharan, 2004). While Hariharan’s ideas are in a way predecessors to the space of stock trading based on news release, this project delves more into the realm of NLP in the context of financial textual data, rather than the development of a trading strategy (which is a secondary focus of the project).5 Primarily, the project will explore and attempt to derive a predictive relationship between news reports and stock movements. Another study by Subramanian, aimed at optimization of automated trading algorithms would have come in handy in later phases, had we decided to focus on strategies. Rather, we employed a simple set of trading ideas, described later, to quantify and avoid confounding the results with advanced models (Subramanian, 2004). 5 If we happen to make significant progress towards our goals of achieving satisfactory NLP accuracy, we may begin to shift our focus on refining the trading strategy tailored to the results. Atish Davda NLP and Sentiment Driven Automated Trading Parshant Mittal Senior Design 2007‐08 Page 6 An interesting source of data encountered during the preliminary research stages was the TextMap Suite of web‐accessible services (Skiena, 2007). The suite of tools provides visual representations of vast amounts of potentially helpful time‐series data on various topics. While we can fathom several uses of this data, we did not make much use of it.
Load more

Recommended publications
  • A Sentiment Analysis Approach to the Prediction of Market Volatility
    A Sentiment Analysis Approach to the Prediction of Market Volatility JUSTINA DEVEIKYTE, Dept. of Computer Science & Information Systems Birkbeck, University of London HELYETTE GEMAN, Dept. of Economics, Mathematics & Statistics Birkbeck, University of London CARLO PICCARI, Dept. of Economics, Mathematics & Statistics Birkbeck, University of London ALESSANDRO PROVETTI, Dept. of Computer Science & Information Systems Birkbeck, University of London Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors’ confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news.
    [Show full text]
  • 1 Investor Sentiment and the Market Reaction to Dividend News
    Investor sentiment and the market reaction to dividend news: European evidence Abstract Purpose: This paper examines the effect of investor sentiment on the market reaction to dividend change announcements. Design/methodology/approach: We use the European Economic Sentiment Indicator data, from Directorate General for Economic and Financial Affairs (DG ECFIN), as a proxy for investor sentiment and focus on the market reaction to dividend change announcements, using panel data methodology. Findings: Using data from three European markets, our results indicate that the investor sentiment has some influence on the market reaction to dividend change announcements, for two of the three analysed markets. Globally, we find no evidence of investor sentiment influencing the market reaction to dividend change announcements for the Portuguese market. However, we find evidence that the positive share price reaction to dividend increases enlarges with sentiment, in the case of the UK markets, whereas the negative share price reaction to dividend decreases reduces with sentiment, in the French market. Research limitations/implications: We have no access to dividend forecasts, so, our findings are based on naïve dividend changes and not unexpected change dividends. Originality/value: This paper offers some insights on the effect of investor sentiment on the market reaction to firms’ news, a strand of finance that is scarcely developed and contributes to the analysis of European markets that are in need of research. As the best of our knowledge, this is the first study to analyse the effect of investor sentiment on the market reaction to dividend news, in the context of European markets. Key Words: Investor Sentiment, Dividend News, Market Reaction, Behavioural Finance Classification: Research Paper 1 1.
    [Show full text]
  • Do Sentiment Indicators Improve Dynamic Asset Allocation?
    EDHEC RISK AND ASSET MANAGEMENT RESEARCH CENTRE 393-400 promenade des Anglais 06202 Nice Cedex 3 Tel.: +33 (0)4 93 18 32 53 E-mail: [email protected] Web: www.edhec-risk.com When to Pick the Losers: Do Sentiment Indicators Improve Dynamic Asset Allocation? October 2006 Devraj Basu EDHEC Business School Chi-Hsiou Hun Durham Business School, University of Durham Roel Oomen Warwick Business School, University of Warwick Alexander Stremme Warwick Business School, University of Warwick Abstract Recent finance research that draws on behavioral psychology suggests that investors systematically make errors in forming expectations about asset returns. These errors are likely to cause significant mis-pricing in the short run, and the subsequent reversion of prices to their fundamental level implies that measures of investor sentiment are likely to be correlated with stock returns. A number of empirical studies using both market and survey data as proxies for investor sentiment have found support for this hypothesis. In this paper we investigate whether investor sentiment (as measured by certain components of the University of Michigan survey) can help improve dynamic asset allocation over and above the improvement achieved based on commonly used business cycle indicators. We find that the addition of sentiment variables to business cycle indicators considerably improves the performance of dynamically managed portfolio strategies, both for a standard market-timer as well as for a momentum investor. For example, sentiment-based dynamic trading strategies, even out-of-sample, would not have incurred any significant losses during the October 1987 crash or the collapse of the dot.com bubble in late 2000.
    [Show full text]
  • Sentiment and Speculation in a Market with Heterogeneous Beliefs
    Online seminar via Zoom Thursday, September 17, 2020 11:00 am Sentiment and speculation in a market with heterogeneous beliefs Ian Martin Dimitris Papadimitriou∗ April, 2020 Abstract We present a model featuring risk-averse investors with heterogeneous beliefs. Individuals who are correct in hindsight, whether through luck or judgment, be- come relatively wealthy. As a result, market sentiment is bullish following good news and bearish following bad news. Sentiment drives up volatility, and hence also risk premia. In a continuous-time Brownian limit, moderate investors trade against market sentiment in the hope of capturing a variance risk premium created by the presence of extremists. In a Poisson limit that features sudden arrivals of information, CDS rates spike following bad news and decline during quiet times. ∗Martin: London School of Economics, http://personal.lse.ac.uk/martiniw/. Papadimitriou: University of Bristol, https://sites.google.com/site/papadimitrioudea/. We thank Brandon Han, Nobu Kiyotaki, John Moore, Jeremy Stein, Andrei Shleifer, John Campbell, Eben Lazarus, Pe- ter DeMarzo and seminar participants at LSE, Arizona State University, Harvard University, HKUST, Shanghai Jiao Tong University (SAIF) and the NBER Summer Institute for comments. Ian Martin is grateful for support from the Paul Woolley Centre and from the ERC under Starting Grant 639744. 1 In the short run, the market is a voting machine, but in the long run it is a weighing machine. |Benjamin Graham. In this paper, we study the effect of heterogeneity in beliefs on asset prices. We work with a frictionless dynamically complete market populated by a continuum of risk-averse agents who differ in their beliefs about the probability of good news.
    [Show full text]
  • Does Sentiment Matter for Stock Market Returns? Evidence from a Small European Market
    Does sentiment matter for stock market returns? Evidence from a small European market Carla Fernandes †1 University of Aveiro Paulo Gama ‡ University of Coimbra/ISR-Coimbra * Elisabete Vieira University of Aveiro/GOVCOPP March, 2010 † Address: ISCA-UA, R. Associação Humanitária dos Bombeiros de Aveiro, Apartado 58, 3811-902 Aveiro, Portugal. Phone: +351 234 380 110 . Fax: +351 234 380 111 . E-mail: [email protected] . ‡ Address: Faculdade de Economia, Av. Dias da Silva, 165, 3004-512 Coimbra, Portugal. Phone: +351 239 790 523. Fax: +351 239 403 511. E-mail: [email protected] . * Address: ISCA-UA, R. Associação Humanitária dos Bombeiros de Aveiro, Apartado 58, 3811-902 Aveiro, Portugal. Phone: +351 234 380 110 . Fax: +351 234 380 111 . E-mail: [email protected] . Does sentiment matter for stock market returns? Evidence from a small European market Abstract An important issue in finance is whether noise traders, those who act on information that has no value, influence prices. Recent research indicates that investor sentiment affects the return distribution of a few categories of assets in some stock markets. Other studies also document that US investor sentiment is contagious. This paper investigates whether Consumer Confidence (CC) and the Economic Sentiment Indicator (ESI) – as proxies for investor sentiment – affects Portuguese stock market returns, at aggregate and industry levels, for the period between 1997 to 2009. Moreover the impact of US investor sentiment on Portuguese stock market returns is also addressed. We find several interesting results. First, our results provide evidence that consumer confidence index and ESI are driven by both, rational and irrational factors.
    [Show full text]
  • An Investigation Into Sentiment-Induced Institutional Trading Behavior and Asset Pricing in the REIT Market
    An investigation into sentiment-induced institutional trading behavior and asset pricing in the REIT market Article Accepted Version Das, P. K., Freybote, J. and Marcato, G. (2015) An investigation into sentiment-induced institutional trading behavior and asset pricing in the REIT market. Journal of Real Estate Finance and Economics, 51 (2). pp. 160-189. ISSN 1573-045X doi: https://doi.org/10.1007/s11146-014-9490-z Available at http://centaur.reading.ac.uk/60285/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1007/s11146-014-9490-z Publisher: Springer All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online An Investigation into Sentiment-Induced Institutional Trading Behavior and Asset Pricing in the REIT Market Prashant K. Das1 Julia Freybote2 Gianluca Marcato3 ABSTRACT Institutional investors such as pension funds or insurance companies commonly invest in the unsecuritized and securitized real estate market. We investigate how institutional investor sentiment in the commercial real estate market affects institutional trading behavior in the REIT market and subsequently asset pricing. In particular, we test two alternative theories - flight to liquidity and style investing theory - to explain the sentiment-induced trading behavior of institutional investors in the REIT market for the pre-crisis (2002-2006), crisis (2007-2009) and post-crisis (2010-2012) period.
    [Show full text]
  • How Market Sentiment Drives Forecasts of Stock Returns Roman
    How Market Sentiment Drives Forecasts of Stock Returns * Roman Frydman, Nicholas Mangee** and Josh Stillwagon*** Working Paper No. 115 April 21st, 2019 ABSTRACT We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns. https://doi.org/10.36687/inetwp115 * Department of Economics, New York University, email: [email protected] ** Department of Finance, Parker College of Business, Georgia Southern University, email: [email protected]. *** Economics Division, Babson College, email: [email protected] JEL Codes: G12; G14; G41; C58 Keywords: stock-return forecasts, fundamentals, market sentiment, structural change, model ambiguity. Acknowledgements: The authors are participants in the Program on Knightian Uncertainty Economics at the Institute for New Economic Thinking (INET).
    [Show full text]
  • Sentiment-Induced Institutional Trading Behavior and Asset Pricing in Securitized Real Estate Markets
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PDXScholar Portland State University PDXScholar Business Faculty Publications and Presentations The chooS l of Business 2014 Sentiment-Induced Institutional Trading Behavior and Asset Pricing in Securitized Real Estate Markets Prashant K. Das Ecole Hoteliere de Lausanne Julia Freybote Portland State University, [email protected] Gianluca Marcato University of Reading Let us know how access to this document benefits ouy . Follow this and additional works at: https://pdxscholar.library.pdx.edu/busadmin_fac Part of the Real Estate Commons Citation Details Das, Prashant K.; Freybote, Julia; and Marcato, Gianluca, "Sentiment-Induced Institutional Trading Behavior and Asset Pricing in Securitized Real Estate Markets" (2014). Business Faculty Publications and Presentations. 40. https://pdxscholar.library.pdx.edu/busadmin_fac/40 This Pre-Print is brought to you for free and open access. It has been accepted for inclusion in Business Faculty Publications and Presentations by an authorized administrator of PDXScholar. For more information, please contact [email protected]. Sentiment-Induced Institutional Trading Behavior and Asset Pricing in Securitized Real Estate Markets By Prashant K. Das1 Julia Freybote2 Gianluca Marcato3 ABSTRACT Institutional investors such as pension funds or insurance companies commonly invest in the unsecuritized and securitized real estate market. We investigate how institutional investor sentiment in the inefficient commercial real estate market affects institutional trading behavior in the REIT market and subsequently asset pricing. In particular, we test two alternative theories - flight to liquidity and style investing theory - to explain the sentiment- induced trading behavior of institutional investors in the REIT market for the pre-crisis (2002-2006), crisis (2007-2009) and post-crisis (2010-2012) period.
    [Show full text]
  • Coronavirus-Related Sentiment and Stock Prices Measuring Sentiment Effects on Swedish Stock Indices
    DEGREE PROJECT IN FINANCE PROGRAM: REAL ESTATE AND FINANCE FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2020 Coronavirus-Related Sentiment and Stock Prices Measuring Sentiment Effects on Swedish Stock Indices Olga Piksina Patricia Vernholmen KTH INSTITUTIONEN FÖR FASTIGHETER OCH BYGGANDE 1 Bachelor of Science Thesis Title: Coronavirus-Related Sentiment and Stock Prices: Measuring Sentiment Effects on Swedish Stock Indices Authors: Olga Piksina, Patricia Vernholmen Institution: Institution of Real Estate and Construction Management Bachelor Thesis number: TRITA-ABE-MBT-20482 Archive number: Supervisor: Andreas Fili Keywords: market sentiment, behavioural finance, market efficiency, coronavirus, Swedish stock market, text analytics, sentiment analysis, news mining Abstract This thesis examines the effect of coronavirus-related sentiment on Swedish stock market returns during the coronavirus pandemic. We study returns on the large cap and small cap price indices OMXSLCPI and OMXSSCPI during the period January 2, 2020 – April 30, 2020. Coronavirus sentiment proxies are constructed from news articles clustered into topics using latent Dirichlet allocation and scored through sentiment analysis. The impact of the sentiment proxies on the stock indices is then measured using a dynamic multiple regression model. The results show that the proxies representing fundamental changes in our model — Swedish Politics and Economic Policy — have a strongly significant impact on the returns of both indices, which is consistent with financial theory. We also find that sentiment proxies Sport and Coronavirus Spread are statistically significant and impact Swedish stock prices. This implies that coronavirus-related news influenced market sentiment in Sweden during the research period and could be exploited to uncover arbitrage. Finally, the amount of sentiment-inducing news published daily is shown to have an impact on stock price volatility.
    [Show full text]
  • Integrating Market Sentiment with Trading Rules - Empirical Study on China Stock Market
    Advances in Social Science, Education and Humanities Research, volume 315 International Conference on Pedagogy, Communication and Sociology (ICPCS 2019) Integrating Market Sentiment with Trading Rules - Empirical Study on China Stock Market Long Qin Ruoen Ren School of Economics and Management School of Economics and Management Beihang University Beihang University Beijing, China Beijing, China Abstract—The effectiveness of technical analysis in forecasting accuracy. If the trader can get the data in a financial market has been discussed by researchers in many timely manner and aggregate the data into useful years. With the explosion of text data on internet, it is crucial information, he can respond in advance and increase the to capture the market sentiment and integrate with market probability of getting extra profit. data to optimize the trading strategy. This paper reviewed the literature of technical analysis, text mining application in In this paper, we analyzed the investor sentiment by financial market forecasting and proposed new method to retrieving the information on internet. And we proposed a optimize the trading rule. It analyzed the investor comments new method by considering investor sentiment into trading on top financial websites in China and further generated rules. We also conducted an empirical study on China market sentiment. The empirical study showed that the stock market. The structure of this paper is as follows: return of trading rules with market sentiments outperformed section two, literature review of technical analysis and text the traditional methods. mining in financial markets; section three, new trading strategy with integrating of investors’ sentiment; section Keywords—technical analysis, text mining, market four, empirical research on China stock market; section sentiment five, conclusion and discussion for future research.
    [Show full text]
  • Investor Sentiment in Stocks and Bonds
    NBER WORKING PAPER SERIES INVESTOR SENTIMENT IN THE STOCK MARKET Malcolm Baker Jeffrey Wurgler Working Paper 13189 http://www.nber.org/papers/w13189 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 June 2007 We thank Xavier Gabaix, Robin Greenwood, Jim Hines, Stefan Nagel, Ronnie Sadka, Andrei Shleifer, Timothy Taylor, and Michael Waldmann for very helpful comments. We thank the Investment Company Institute for data on mutual fund flows. Baker gratefully acknowledges financial support from the Division of Research of the Harvard Business School. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. © 2007 by Malcolm Baker and Jeffrey Wurgler. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Investor Sentiment in the Stock Market Malcolm Baker and Jeffrey Wurgler NBER Working Paper No. 13189 June 2007 JEL No. E32,G11,G12,G14 ABSTRACT Real investors and markets are too complicated to be neatly summarized by a few selected biases and trading frictions. The "top down" approach to behavioral finance focuses on the measurement of reduced form, aggregate sentiment and traces its effects to stock returns. It builds on the two broader and more irrefutable assumptions of behavioral finance -- sentiment and the limits to arbitrage -- to explain which stocks are likely to be most affected by sentiment. In particular, stocks of low capitalization, younger, unprofitable, high volatility, non-dividend paying, growth companies, or stocks of firms in financial distress, are likely to be disproportionately sensitive to broad waves of investor sentiment.
    [Show full text]
  • Individual Investor Sentiment and IPO Stock Returns: Evidence from the Korean Stock Market
    Individual Investor Sentiment and IPO Stock Returns: Evidence from the Korean Stock Market Chune Young Chung School of Business Administration, Chung-Ang University Joohwan Kim College of Business Administration, Hankuk University of Foreign Studies ∗ Jinwoo Park College of Business Administration, Hankuk University of Foreign Studies ∗ Corresponding author: Jinwoo Park, College of Business Administration, Hankuk University of Foreign Studies, 270 Imun-dong Dongdaemun-gu, Seoul 130-791, Korea. Tel: +82-2-2173-3175, Fax: +82-2-959-4645, E-mail: [email protected] 1 Individual Investor Sentiment and IPO Stock Returns: Evidence from the Korean Stock Market Abstract This paper examines the impact of individual investors’ pre-market and aftermarket sentiment on initial public offering (IPO) stock returns, based on 382 IPO firms in the Korean stock market from 2007 to 2014. The results indicate that individual investors’ pre-market sentiment is significantly and positively related to the initial returns initial returns, as measured by the difference between the offer price and the first-day opening price. The findings also indicate that individual investors’ pre-market sentiment is positively associated with their aftermarket sentiment, implying a spillover effect from pre-market to aftermarket sentiment. However, after high initial returns in IPO firms with individual investors’ high pre- market sentiment, we find subsequent underperformance; this is more pronounced in firms with high pre-market and aftermarket sentiment. Overall, our results imply that individual investors’ pre-market sentiment can largely explain IPO stocks’ high initial returns. Additionally, pre-market sentiment followed by aftermarket sentiment can account for IPO stocks’ underperformance. Nevertheless, the impact of individual investor sentiment on IPO stock returns seems to be a short-term phenomenon, as high initial returns and subsequent underperformance are more pronounced within the first month from the IPO.
    [Show full text]