DOCSLIB.ORG

Behavioral Finance in Asset Management a Primer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Behavioral Finance in Asset Management a Primer FEATURE Behavioral Finance in Asset Management A Primer By Marcus Schulmerich, PhD, CFA®, FRM Heuristic Simplification Biases inancial crises are taking place called behavioral biases. They played more frequently and their impact a central role in the development of Heuristics are experience-based tech- F is spreading across the globe— behavioral finance. niques that help us make decisions the latest examples are the 2007–2009 Slovic saw the relevance of behav- (Kahneman et al. 1982, 3–4). Three key Great Recession and stock market ioral concepts in finance and set out heuristics discussed in this article are crash. The need to better understand his thesis in two articles at the end of representativeness, anchoring/salience, and address the root causes of such the 1960s (Shefrin 2002, 7–8). Then, and loss aversion/prospect theory. crashes becomes ever more important. in 1974, Tversky and Kahneman intro- Loss aversion forms part of Kahneman Standard finance theory cannot duced the fundamental concept of heu- and Tversky’s prospect theory (1979, explain the phenomenon, but behav- ristics—the study of how people make 263–291, reviewed later in this article. ioral finance theory offers some com- decisions, rush to judgment, or solve In 2002, Kahneman and Smith were pelling explanations. Behavioral finance problems, often using their experiences awarded the Nobel Memorial Prize in supplements standard finance theory and biases. Since then, academics and Economic Sciences for their work in the by introducing behavioral and psycho- practitioners have incorporated heu- field of behavioral finance. logical criteria to help clarify investors’ ristics into behavioral finance and led Representativeness decision-making process. This article a vast research effort focusing on the outlines the background and key find- cause and effect of psychological biases Representativeness bias describes the ings of behavioral finance and examines in financial markets. tendency to evaluate a situation by the influence of behavioral biases in More recently, in 2001, David comparing it to generalities or stereo- stock market crashes. Hirshleifer5 published a concise over- types. It can be thought of as a mental view of the most important behavioral shortcut that helps decision makers An Introduction to Behavioral biases, as shown in table 1. The biases avoid the need to analyze similar pro- Finance are grouped into four categories: cesses again and again. In 1956, economist Vernon L. Smith1 • Self-deception (limits to learning) In investment decision-making, rep- was the first to introduce the concept • Heuristic Simplification resentativeness bias can arise either from of behavioral finance. At the time, the • Emotion/Affect base-rate neglect or sample-size neglect. investment community did not believe • Social Base-rate neglect: Investors attempt in the idea that human behavior influ- to determine the potential success of ences security prices. However, others Key biases are highlighted in table 1. an investment in a stock by contextual- such as the psychologists Paul Slovic,2 As examined below, these biases, in izing it in a familiar, easy-to-understand Amos Tversky,3 and Daniel Kahneman,4 large part, help explain the occurrence classification scheme. For example, an continued to analyze investors’ so- of stock market bubbles and crashes. investor might categorize a stock as a TABLE 1: OVERVIEW OF BEHAVIORAL BIASES BY HIRSHLEIFER Behavioral Biases Heuristic Simplification Self-Deception (limits to learning) Emotion/Affect Social Representativeness Overoptimism Ambiguity Aversion Herding Anchoring/Salience Overconfidence Self-Control (hyperbolic discounting) Contagion Loss Aversion/Prospect Theory Confirmation Mood Imitation Framing Self-Attribution Regret Theory Cascades Availability Hindsight Cue Competition Cognitive Dissonance Categorization Conservation Source: Hirschleifer (2001, 1,533–1,597) 42 Investments&Wealth MONITOR © 2013 Investment Management Consultants Association. Reprint with permission only. FEATURE TABLE 2: TYPES OF REPRESENTATIVENESS BIASES Type of Representativeness Bias Examples 1. Investors can make significant financial errors when they examine a money manager’s track record. They look at that past few quarters or years and conclude, based on inadequate statistical data, that a fund’s performance is the result of skilled allocation and security selection. Base-rate neglect 2. Investors also make similar mistakes when investigating track records of stock analysts. For exam- ple, they look at the success of an analyst’s past few recommendations, erroneously assessing the analyst’s aptitude based on this limited data sample. Investors often fail to judge the likelihood of an investment outcome because they choose an inap- Sample-size neglect propriate sample size of data on which to base their judgments. They incorrectly assume that small sizes are representative of real data: Researchers call this phenomenon the “law of small numbers.” Source: Pompian (2006, 66–67) “value stock” and draw quick conclusions or irrelevant experience. Shiller (2005, tive assessment—their views can be sig- about the risks and rewards that follow 148) defines two types of anchors: quan- nificantly influenced by suggestions, e.g., from that categorization. This reason- titative and moral. how the numbers are presented. In this ing ignores variables that can impact Quantitative anchors are numbers example, the answers are strongly influ- the success of the investment. Investors or quantitative variables used to make enced by the first numbers that appear in often embark on this erroneous path estimations. One obvious example is the question. because they perceive it as an easy alter- past stock prices. When faced with investment decisions, native to the diligent and more compli- Moral anchors play an important investors seem to get anchored in past cated research that’s actually required role when people compare the intuitive price changes and the average prices of when evaluating an investment. force of stories and reasons to hold their assets (Shefrin 2005, 51). Figure 1 shows Sample-size neglect: Investors fail investments against their perceived that financial analysts also fall victim to to accurately consider the sample size of need to consume the wealth that the quantitative anchoring: Their forecasts the data on which they are basing their investments represent. of the S&P 500 index from June 1991 judgments and incorrectly assume that While heuristics can aid in decision to June 2004 were highly correlated to small sample sizes are representative making, they sometimes lead to severe the past prices of the index (Montier of populations. When people do not and systematic investment errors 2005, 11). initially comprehend a phenomenon because they rely on intuitive judgments In discussing moral anchors, Shiller reflected in a series of data, they quickly that are categorically different from the (2005, 151–152) recalls The Millionaire link assumptions based on only a few rational models on which investment Next Door, which was a best seller of the available data points. Individuals decisions should be based. during the stock market boom of the prone to sample-size neglect are quick Consider the following example 1990s (Stanley and Danko 1996). The to treat properties reflected in such from Montier (2007, 25): In performing book explained that most millionaires small samples as properties that accu- calculations, the way equations are in the United States are not exceptional rately describe universal pools of data. presented appears to have a significant income earners but merely frugal savers— However, the small sample examined influence on the result. people who are not interested in buy- may not be representative of the real Two versions of the same calculation ing a new car every year, for instance. data. This is also known as the “law of are shown below. The book’s enticing story about invest- small numbers.” ment millionaires, who save rather than (a) 1 × 2 × 3 × 4 × 5 × 6 × 7 × 8 As illustrated by the examples in spend their money, was just the kind (b) 8 × 7 × 6 × 5 × 4 × 3 × 2 × 1 table 2, both types of representativeness of moral anchor needed to help sustain bias can lead to significant investment In both cases the answer is 40,320. the unusual bull market during the mid mistakes. However, experiments have shown that to late 1990s. using different ways of presenting this Reasons to hold stocks or other Anchoring/Salience question of calculating the factorial of investments also can take on ethical as Anchoring is the tendency of decision eight produces different results: well as practical dimensions. Our culture makers to use “anchor” values or beliefs In (a) the median answer was 512. may supply reasons to hold stocks and as the basis for an assessment. When In (b) the median answer was 2,250. other savings vehicles that are related asked to form an assessment, they estab- This test shows that when people are to our sense of identity as responsible, lish a judgment based on inappropriate asked to make a decision—say a quantita- good, or level-headed people. November/December 2013 43 © 2013 Investment Management Consultants Association. Reprint with permission only. FEATURE Loss Aversion/Prospect Theory FIGURE 1: S&P 500 INDEX LEVEL COMPARED TO S&P 500 INDEX LEVEL Loss aversion is the third type of heu- FORECASTS ristic bias with significant implications 1600 for the financial markets. Kahneman and Tversky (1979) introduced prospect
Load more

Recommended publications
  • Good Day Sunshine: Stock Returns and the Weather
    THE JOURNAL OF FINANCE VOL. LVIII, NO. 3 JUNE 2003 Good Day Sunshine: Stock Returns and theWeather DAVID HIRSHLEIFER and TYLER SHUMWAY n ABSTRACT Psychological evidence and casual intuition predict that sunny weather is as- sociated with upbeat mood. This paper examines the relationship between morning sunshine in the city of a country’s leading stock exchange and daily market index returns across 26 countries from 1982 to 1997. Sunshine is strongly signi¢cantly correlated with stock returns. After controlling for sun- shine, rain and snow are unrelated to returns. Substantial use of weather- based strategies was optimal for a trader with very low transactions costs. However, because these strategies involve frequent trades, fairly modest costs eliminate the gains.These ¢ndings are di⁄cult to reconcile with fully rational price setting. SUNSHINE AFFECTS MOOD, as evidenced by song and verse, daily experience, and formal psychological studies. But does sunlight a¡ect the stock market? The traditional e⁄cient markets view says no, with minor quali¢cations. If sun- light a¡ects the weather, it can a¡ect agricultural and perhaps other weather- related ¢rms. But in modern economies in which agriculture plays a modest role, it seems unlikely that whether it is cloudy outside the stock exchange today should a¡ect the rational price of the nation’s stock market index. (Even in coun- tries where agriculture plays a large role, it is not clear that one day of sunshine versus cloud cover at the stock exchange should be very informative about har- vest yield.) An alternative view is that sunlight a¡ects mood, and that people tend to eval- uate future prospects more optimistically when they are in a good mood than when they are in a bad mood.
    [Show full text]
  • Visibility Bias in the Transmission of Consumption Beliefs and Undersaving
    NBER WORKING PAPER SERIES VISIBILITY BIAS IN THE TRANSMISSION OF CONSUMPTION BELIEFS AND UNDERSAVING Bing Han David Hirshleifer Johan Walden Working Paper 25566 http://www.nber.org/papers/w25566 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 February 2019 We thank Pierluigi Balduzzi, Martin Cherkes, Marco Cipriani, Douglas Bernheim, Andreas Fuster, Giacomo de Giorgi, Mariassunta Giannetti, Luigi Guiso, Robert Korajczyk, Theresa Kuchler, Chris Malloy, Timothy McQuade, Peter Reiss, Nick Roussanov, Erling Skancke, Andy Skrzypacz, Tano Santos, Siew Hong Teoh, Bernie Yeung, Youqing Zhou, David Benjamin Zuckerman, and participants at the Amerian Economics Association meetings in Boston, the MIT Conference on Evolution and Financial Markets, the National Bureau of Economic Research Behavioral Finance Working Group meeting at UCSD, the OSU Alumni Conference at Ohio State University, the Miami Behavioral Finance Conference at the University of Miami, the SFS Finance Cavalcade at the University of Toronto; the WISE18 Workshop on Information and Social Economics at Caltech; and seminar participants at Arizona State University, Boston College, Calgary University, Caltech, Cheung Kong Graduate School of Business, Columbia University, Hong Kong Polytechnic University, London Business School, National University of Singapore, New York Federal Reserve Board, Peking University, Renmin University, Shanghai University of Finance and Economics, Southwest University of Finance and Economics, Stanford University, University of Hong Kong, University of Houston, University of International Business and Economics, University of Texas Austin, and York University for very helpful comments; and Lin Sun and Qiguang Wang for very helpful comments and research assistance. David Hirshleifer is a paid advisor to EvoShare, a firm that helps employees save more for retirement.
    [Show full text]
  • The Impact of Institutional Trading on Stock Prices*
    Journal of Financial Economics 31(1992) 13-43. North-Holland The impact of institutional trading on stock prices* Josef Lakonishok Cnirersity of Illinois at (;rbana-Champaign. Champuign. IL 61820. USA Andrei Shleifer Hurcard tiniwrsir~. Cambridge. MA 02138. L’SA Robert W. Vishny L’nicersiry 01’ Chicago, Chicago. IL 60637, USA Received September 1991, tinal version received March 1992 This paper uses new data on the holdings of 769 tax-exempt (predominantly pension) funds. to evaluate the potential effect of their trading on stock prices. We address two aspects of trading by these money managers: herding, which refers to buying (selling) simultaneously the same stocks as other managers buy (sell), and positive-feedback trading, which refers to buying past winners and selling past losers. These two aspects of trading are commonly a part of the argument that institutions destabilize stock prices. The evidence suggests that pension managers do not strongly pursue these potentially destabilizing practices. 1. Introduction Instead of merely buying and holding the market portfolio, most investors follow strategies of actively picking and trading stocks. When investors trade actively, their buying and selling decisions may move stock prices. Understand- ing the behavior of stock prices thus requires an understanding of the invest- ment strategies of active investors. Correspondence ro: Robert W. Vishny, Graduate School of Business, University of Chicago, 1101 East 58th Street. Chicago, IL 60637, USA. *We are grateful to Louis than, Kenneth Froot (the referee). Lawrence Harris. hIark Mitchell. Jay Ritter. Jeremy Stein. and Richard Thaier for helpful comments; and especially to Gil Beebower and Vasant Kamath for their helpful advice.
    [Show full text]
  • Tyler Shumway John C
    Tyler Shumway John C. and Sally S. Morley Professor of Finance University of Michigan Office: Ross School of Business University of Michigan 701 Tappan Street Ann Arbor, MI 48109-1234 (734) 763-4129 [email protected] http://www-personal.umich.edu/~shumway Education Ph.D., University of Chicago Graduate School of Business, 1996 B.A., Brigham Young University (Economics), 1991 Academic Appointments Ross School of Business, University of Michigan John C. and Sally S. Morley Professor of Finance, 2016-present Professor of Finance, 2010-2015 Associate Professor of Finance, 2002-2010 Assistant Professor of Finance, 1996-2002 Lecturer of Finance, 1995-1996 Stanford Graduate School of Business, Visiting Associate Professor of Finance, 2004-2005 Marriott School of Business, Brigham Young University, Visiting Professor, 2017-2018 Journal Publications The Delisting Bias in CRSP Data, Journal of Finance, March 1997, 327-340. The Delisting Bias in CRSP's Nasdaq Data and its Implications for the Size Effect (with Vincent Warther), Journal of Finance, December 1999, 2361-2379. Forecasting Bankruptcy More Accurately: A Simple Hazard Model, Journal of Business, January 2001, 101-124. Expected Option Returns (with Joshua Coval), Journal of Finance, June 2001, 983-1009. Is Sound Just Noise? (with Joshua Coval), Journal of Finance, October 2001, 1887-1910. Good Day Sunshine: Stock Returns and the Weather (With David Hirshleifer), Journal of Finance, June 2003, 1009-1032. Do Behavioral Biases Affect Prices? (with Joshua Coval), Journal of Finance, February 2005, 1-34. Forecasting Default with the Merton Distance to Default Model (with Sreedhar Bharath), Review of Financial Studies, May 2008, 1339-1369.
    [Show full text]
  • Earnings Management and the Long-Run Market Performance of Initial Public Offerings
    THE JOURNAL OF FINANCE • VOL. LIII, NO. 6 • DECEMBER 1998 Earnings Management and the Long-Run Market Performance of Initial Public Offerings SIEW HONG TEOH, IVO WELCH, and T. J. WONG* ABSTRACT Issuers of initial public offerings ~IPOs! can report earnings in excess of cash flows by taking positive accruals. This paper provides evidence that issuers with unusu- ally high accruals in the IPO year experience poor stock return performance in the three years thereafter. IPO issuers in the most “aggressive” quartile of earnings managers have a three-year aftermarket stock return of approximately 20 percent less than IPO issuers in the most “conservative” quartile. They also issue about 20 percent fewer seasoned equity offerings. These differences are statistically and economically significant in a variety of specifications. SEVERAL STUDIES FIND THAT INITIAL public offerings ~IPOs! underperform after the issue.1 Over a three-year holding period after the offering, Ritter ~1991! reports substantially lower stock returns ~mean of 227 percent and median of 255 percent! for a sample of 1,526 IPOs going public between 1975 and *Teoh is at the University of Michigan, Welch is at the University of California, Los Angeles, and Wong is at the University of Science and Technology, Hong Kong. We thank Andrew Alford, Joe Aharony, John Barber, Vic Bernard, Shlomo Bernartzi, Robert Bowen, Laura Field, Steve Hansen, David Hirshleifer, Michael Kirschenheiter, Charles Lee, Tim Loughran, Susan Moyer, Gita Rao, Jay Ritter, Jake Thomas, Dan Tinkelman, Sheridan Titman, and workshop partici- pants at the University of British Columbia, Columbia University, New York University, and the University of Washington for helpful comments.
    [Show full text]
  • Social Transmission Bias in Economics and Finance
    THE JOURNAL OF FINANCE • VOL. , NO. 0 • APRIL 2020 Presidential Address: Social Transmission Bias in Economics and Finance DAVID HIRSHLEIFER∗ ABSTRACT I discuss a new intellectual paradigm, social economics and finance—the study of the social processes that shape economic thinking and behavior. This emerging field recog- nizes that people observe and talk to each other. A key, underexploited building block of social economics and finance is social transmission bias: systematic directional shift in signals or ideas induced by social transactions. I use five “fables” (models) to illustrate the novelty and scope of the transmission bias approach, and offer sev- eral emergent themes. For example, social transmission bias compounds recursively, which can help explain booms, bubbles, return anomalies, and swings in economic sentiment. THIS ADDRESS DISCUSSES A NEW intellectual paradigm, which I call social eco- nomics and finance—the study of how social interaction affects economic out- comes. In standard analyses of economic behavior, people interact only imper- sonally via trading orders and observation of market price. A missing chapter in our understanding of finance consists of the social processes that shape economic thinking and behavior. Social economics and finance recognizes that people observe each other and talk to each other, where talking includes written text and social media. A key but underexploited intellectual building block of social economics and finance is social transmission bias, the systematic directional modification of ideas or signals as they pass from person to person. ∗David Hirshleifer is at Merage School of Business, University of California. This paper was the Presidential Address of the American Finance Association at the 2020 Annual Meeting in San Diego, CA.
    [Show full text]
  • Social Transmission Bias and Investor Behavior
    NBER WORKING PAPER SERIES SOCIAL TRANSMISSION BIAS AND INVESTOR BEHAVIOR Bing Han David Hirshleifer Johan Walden Working Paper 24281 http://www.nber.org/papers/w24281 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge MA 02138 February 2018, Revised January 2020 A previous version of this paper was entitled, “Self-Enhancing Transmission Bias and Active Investing.” We thank seminar participants at Cambridge University, Central University of Finance and Economics, Chinese University of Hong Kong, Columbia University, Emory University, the Federal Reserve Board of New York, Nanyang Business School, National University of Singapore, New York University, Oxford University, Princeton University, Shanghai Advanced Institute of Finance, Singapore Management University, Southwestern University of Finance and Economics, UCLA, UCSD, University of Hong Kong, University of North Carolina, University of Toronto, University of Washington at Seattle, Washington University in St. Louis, Xiamen University, Yale University, and the Institute for Mathematical Behavioral Sciences at UC Irvine; participants at the National Bureau of Economic Research behavioral finance working group meeting in Chicago, the American Finance Association annual meetings, the Applied Behavioral Finance Conference at UCLA, the Linde Conference at Caltech, the BYU Red Rock Finance Conference, the SITE conference at Stanford University, and the 2nd China International Forum on Finance and Policy; Jennifer Conrad (Editor), an anonymous referee, the NBER discussant, Nick Barberis; the AFA discussant, Blake LeBaron; the ABF discussant, Andrea Eisfeldt; the Five Star discussant JuanJuan Meng; Markus Brunnermeier, Terry Burnham, Jean-Paul Carvalho, David Dicks, Jakub Jurek, Edward Rice, Nikolai Roussanov, Martin Schmalz, Siew Hong Teoh, Paul Tetlock, Rossen Valkanov, Michela Verardo, Ivo Welch, Jeff Wurgler, Hongjun Yan, Liyan Yang, and Wei Xiong for very helpful comments; and Jason Chan, SuJung Choi, and Major Coleman for helpful research assistance.
    [Show full text]
  • Extraneous Events and Underreaction to Earnings News
    October 25, 2006 Driven to Distraction: Extraneous Events and Underreaction to Earnings News David Hirshleifer¤ Sonya Seongyeon Lim¤¤ Siew Hong Teoh¤ ¤ The Paul Merage School of Business, University of California, Irvine, CA 92697-3125; Hirshleifer: [email protected], (949) 824-9955; Teoh: [email protected], (949) 824-9952 ¤¤ Department of Finance, DePaul University, 1 E. Jackson Blvd, Chicago, IL 60604; [email protected], (312) 362-8825 We thank Nick Barberis, Laura Field, Christo Karuna, Erik Lie, Mort Pincus, Charles Shi, and participants at the Merage School of Business Finance Colloqium at UC Irvine and the 10th Biennial Behavioral Decision Research in Management Conference at Santa Monica, California for helpful comments. Driven to Distraction: Extraneous Events and Underreaction to Earnings News Abstract Psychological evidence indicates that it is hard to process multiple stimuli and per- form multiple tasks at the same time. This paper tests the investor distraction hypoth- esis, which holds that the arrival of extraneous news causes trading and market prices to react sluggishly to relevant news about a ¯rm. Our test focuses upon the competi- tion for investor attention between a ¯rm's earnings announcements and the earnings announcements of other ¯rms. We ¯nd that the immediate stock price and volume re- action to a ¯rm's earnings surprise is weaker, and post-earnings announcement drift is stronger, when a greater number of earnings announcements by other ¯rms are made on the same day. A trading strategy that exploits post-earnings announcement drift is most pro¯table for earnings announcements made on days with a lot of competing news, but it is not pro¯table for announcements made on days with little competing news.
    [Show full text]
  • Curriculum Vitae
    7/1/2021 David A. Hirshleifer Affiliation: Marshall School of Business University of Southern California 3670 Trousdale Pkwy Los Angeles, CA 90089 and National Bureau of Economic Research (Corporate Finance and Asset Pricing Programs) Previous Positions: Merage Chair in Business Growth and Distinguished Professor of Finance and Economics The Paul Merage School of Business University of California, Irvine, 2006-21 Ralph Kurtz Chair in Finance Fisher College of Business The Ohio State University, 1999-2006 Merwin H. Waterman Professor of Finance Professor of Finance University of Michigan Business School, 1994-99 Associate Professor of Finance Assistant Professor of Finance Acting Assistant Professor of Finance Anderson Graduate School of Management, UCLA, 1984-94 Contact Information: (949) 724-2896; Assistant: Ginger Smith, [email protected] (949) 824-5122; FAX: (949) 824-8469 [email protected] Webpage and links to papers: Click here for an RSS feed that will notify you of my new papers and revisions. http://sites.uci.edu/dhirshle/ papers.ssrn.com/author=2024 ideas.repec.org/e/phi20.html http://scholar.google.com/citations?user=pblejMoAAAAJ&hl=en http://www.researcherid.com/rid/E-4342-2012 http://www.amazon.com/David-Hirshleifer/e/B002I5VMKC http://www.pricetheoryapplications.com/ https://twitter.com/4misceldah Degrees: BA 1980‚ Mathematics, UCLA MA 1983‚ Economics, University of Chicago PhD 1985‚ Economics, University of Chicago Articles: Moonshots, Investment Booms, and Selection Bias in the Transmission of Cultural Traits, David Hirshleifer and Joshua Plotkin, Proceedings of the National Academy of Sciences, June 29, 2021, 118 (26) e2015571118. Social Finance as Cultural Evolution, Transmission Bias and Market Dynamics, Erol Akçay and David Hirshleifer, Proceedings of the National Academy of Sciences June 29, 2021, 118 (26) e2015568118.
    [Show full text]
  • Seventh Miami Behavioral Finance Conference December 7-9, 2016
    Seventh Miami Behavioral Finance Conference December 7-9, 2016 PROOF Department of Finance School of Business Administration University of Miami Coral Gables, Florida 33124, USA Seventh Miami Behavioral Finance Conference December 7-9, 2016 Keynote Speaker: Richard Roll, Linde Institute Professor of Finance, California Institute of Technology Program Committee Chair: Alok Kumar, University of Miami Organizer: George Korniotis, University of Miami Wednesday, December 7, 2016 6:00 pm – 8:00 pm Welcome Reception: Pool Lawn and Laguna, Biltmore Hotel, Coral Gables Thursday, December 8, 2016 8:30 am – 9:00 am Continental Breakfast: Shalala Student Center, University of Miami 9:00 am – 9:10 am Conference Program Overview: George Korniotis, University of Miami Morning Session Chair: Stefanos Delikouras, University of Miami 9:10 am – 9:50 am The Dividend Disconnect, Samuel M. Hartzmark, University of Chicago, David H. Solomon, University of Southern California Presenter: Samuel M. Hartzmark Discussant: Markku Kaustia, Aalto University 9:50 am – 10:30 am Discrimination, Social Risk and Portfolio Choice, Yosef Bonaparte, University of Colorado, William Bazley, George Korniotis and Alok Kumar, University of Miami Presenter: William Bazley Discussant: Frank P. Stafford, University of Michigan 10:30 am – 11:00 am Coffee Break 11:00 am – 11:40 am Geographic Momentum, Christopher A. Parsons, University of California at San Diego, Riccardo Sabbatucci, University of California at San Diego, Sheridan Titman, University of Texas at Austin Presenter: Sheridan
    [Show full text]
  • January 2013 Siew Hong Teoh Dean's Professor of Accounting
    January 2013 Siew Hong Teoh Dean’s Professor of Accounting Phone: (949) 824-9952 Paul Merage School of Business, Fax: (949) 725-2847 University of California, Irvine Email: [email protected] Irvine, CA 92697-3195 Previous Positions Associate Professor of Accounting, Fisher College of Business, The Ohio State University, 1999-2006, promoted to full professor in 2006. Assistant Professor of Accounting & Finance, University of Michigan Business School, 1994-99; Anderson Graduate School of Management, University of California, Los Angeles, 1987-1994 Education Ph.D., Graduate School of Business, The University of Chicago M.B.A., Graduate School of Business, The University of Chicago M.Sc. (Econ.), London School of Economics and Political Science B.Sc. (Econ., First Class Honors), London School of Economics and Political Science Awards & Honors Don Beall Center for Innovation and Entrepreneurship Grants, July 2012, July 2010. 3rd Prize, 2007 Chicago Quantitative Alliance Annual Academic Competition Distinguished Member, National Society of Collegiate Scholars, Oct 2005. Dice Center Fellow, Fisher College of Business, Ohio State University, 2001- 2006 Nominated for Brattle Prize for outstanding paper published in Journal of Finance, 1998 Moskowitz Prize for best paper in the area of socially responsible investing, Social Investment Forum, October 1999 University of Michigan, Faculty Career Development Award, 1997 University of California Los Angeles, Faculty Career Development Award, 1992 KPMG Peat Marwick Faculty Fellowship, 1991-1994 KPMG Peat Marwick Research Fellowship, 1989-1991 Publications 1. “Non-Disclosure and Adverse Disclosure as Signals of Firm Value,” Siew Hong Teoh and Chuan Yang Hwang, Review of Financial Studies 4-2, April 1991; 283-314.
    [Show full text]
  • 1 Dongmei Li November 2020 Email
    Dongmei Li November 2020 Email: [email protected] Personal data: US citizen Google citations: https://scholar.google.com/citations?user=RITkZfsAAAAJ Education Ph.D. Finance University of Pennsylvania 2007 Master in Finance Princeton University 2003 M.S. Agricultural Economics University of Maryland at College Park 2001 B.S. Economics People’s (Renmin) University, China 1996 Employment Professor of Finance, Darla Moore School of Business, 2019 – present Moore Fellow University of South Carolina Associate Professor of Finance Darla Moore School of Business, 2014 – 2018 (with tenure) University of South Carolina Assistant Professor of Finance Rady School of Management, 2007 – 2014 University of California, San Diego Research Associate Quantitative Asset Management 2003 Strategy, Sanford Bernstein, NY Intern Global Research Center, 2002 Deutsche Bank Asset Management, NY Editorship Associate Editor, Journal of Empirical Finance, 2017-present Published/Accepted Papers Valuation of new trademarks (with Po-Hsuan Hsu, Qin Li, Siew Hong Teoh, and Kevin Tseng) — forthcoming, Management Science (presented at 2019 Financial Accounting and Reporting Section Midyear Meeting, CICF 2019) More cash, less innovation: The effect of the American jobs creation act on patent value (with Heitor Almeida, Po-Hsuan Hsu, and Kevin Tseng), forthcoming, Journal of Financial and Quantitative Analysis (semi-finalist for Best Paper Award in Corporate Finance, FMA 2018; presented at SFS Cavalcade 2018) Innovative originality, profitability, and stock returns (with David Hirshleifer and Po-Hsuan Hsu), Review of Financial Studies, 2018, 31, 2553-2605 (presented at WFA 2015) 1 Capital investment, innovative capacity, and stock returns (with Praveen Kumar), Journal of Finance, 2016, 71, 2059-2094 (presented at CFEA 2012) Innovative efficiency and stock returns (with David Hirshleifer and Po-Hsuan Hsu), Journal of Financial Economics, 2013, 107, 632-654 (presented at AFA 2012, EFA 2011) Distinguishing rational and behavioral models of momentum, Quarterly Journal of Finance, 2013, 3, 1-30.
    [Show full text]