Asset Pricing Under Asymmetric Information Bubbles & Limits To

Total Page:16

File Type:pdf, Size:1020Kb

Asset Pricing Under Asymmetric Information Bubbles & Limits To Asset Pricing under Asym. Information Limits to Arbitrage Historical Bubbles Symmetric Asset Pricing under Asymmetric Information Information Pricing Equation Bubbles & Limits to Arbitrage Ruling out Asymmetric Information Expected/Strong Bubble Markus K. Brunnermeier Necessary Conditions Limits to Princeton University Arbitrage Noise Trader Risk Synchronization August 17, 2007 Risk Asset Pricing under Asym. Information Limits to Overview Arbitrage Historical Bubbles Symmetric Information Pricing Equation • All agents are rational Ruling out Asymmetric • Bubbles under symmetric information Information • Bubbles under asymmetric information Expected/Strong Bubble Necessary Conditions • Interaction between rational arbitrageurs and behavioral Limits to traders - Limits to Arbitrage Arbitrage Noise Trader • Fundamental risk Risk Synchronization • Risk Noise trader risk + Endogenous short horizons of arbs • Synchronization risk Asset Pricing under Asym. Information Limits to Historical Bubbles Arbitrage Historical Bubbles Symmetric Information Pricing Equation Ruling out • 1634-1637 Dutch Tulip Mania (Netherlands) Asymmetric Information • Expected/Strong 1719-1720 Mississippi Bubble (France) Bubble Necessary • Conditions 1720 South Sea Bubble (England) Limits to • 1990 Japan Bubble Arbitrage Noise Trader Risk • 1999 Internet/Technology Bubble Synchronization Risk Asset Pricing under Asym. Information Limits to A Technology Company Arbitrage Historical Bubbles Symmetric Information Pricing Equation • Company X introduced a revolutionary wireless Ruling out communication technology. Asymmetric Information • It not only provided support for such a technology but also Expected/Strong Bubble Necessary provided the informational content itself. Conditions Limits to • It’s IPO price was $1.50 per share. Six years later it was Arbitrage Noise Trader traded at $ 85.50 and in the seventh year it hit $ 114.00. Risk Synchronization • Risk The P/E ratio got as high as 73. • The company never paid dividends. Asset Pricing under Asym. Information Limits to The Story of RCA in 1920’s Arbitrage Company: Radio Corporate of America (RCA) Historical Technology: Radio Bubbles Years: 1920s Symmetric Information Pricing Equation Ruling out Asymmetric Information Expected/Strong Bubble Necessary Conditions Limits to Arbitrage Noise Trader Risk Synchronization Risk Figure: RCA’s Stock Price from Dec 25 to Dec 50. • RCA peaked at $ 397 in Feb. 1929, down to $ 2.62 in May 1932 • RCA’s stock price was below $ 25 at least until 1950 Asset Pricing under Asym. Information Limits to NASDAQ and “Neuer Markt” Arbitrage Historical Bubbles Symmetric Information Pricing Equation Ruling out Asymmetric Information Expected/Strong Bubble Necessary Conditions Limits to Arbitrage Noise Trader Risk Synchronization Risk Figure: NASDAQ and Neuer Markt during “Technology Bubble”. Asset Pricing under Asym. Information Limits to Bubbles under Symmetric Information Arbitrage Historical Bubbles Symmetric • Keynes’ distinction between speculation and long-run Information Pricing Equation investment: Ruling out Asymmetric • Speculation: Buy “overvalued” in the hope to sell it to Information someone else at an even higher price Expected/Strong Bubble • Investing: Buy and hold strategy Necessary Conditions Limits to • Fundamental value: Was ist das? Arbitrage Noise Trader “highest WTP” if one forces agents to buy & hold the Risk Synchronization asset Risk no uncertainty: discounted value of dividends uncertainty w/ risk-neutral agent: expected discounted value uncertainty w/ risk-averse agents: take expectations w.r.t. EMM Asset Pricing under Asym. Information Limits to Bubbles under Symmetric Information Arbitrage Historical Bubbles Symmetric Information Pricing Equation Ruling out • Problem of Keynes’ buy and hold definition of Asymmetric Information fundamental value: Expected/Strong Bubble • Retrade does also occur to dynamically complete the Necessary Conditions market (not only for speculation). Limits to • With retrade a different allocation can be achieved and Arbitrage Noise Trader hence the EMM is different. Risk Synchronization • Allow for retrade and take EMM which leads to highest Risk fundamental value. Asset Pricing under Asym. Information Limits to Bubbles under Symmetric Information Arbitrage ∗ Historical • with stochastic discount factor mt (or pricing kernel m ) Bubbles t the price of an asset is given by Symmetric Information Pricing Equation Ruling out mt pt = Et [mt+1 (pt+1 + dt+1)] Asymmetric Information where m is related to MRS (divided by prob. of state) Expected/Strong t+1 Bubble Necessary • Alternatively one can also write pricing equation in terms Conditions Limits to of the equivalent martingale measure Arbitrage Noise Trader Risk Qˆ 1 Synchronization p = E (p + d ) Risk t t f t+1 t+1 1 + rt • Securities with Finite Maturity • Reiterate pricing equation • Backwards induction rules out bubbles Asset Pricing under Asym. Information Limits to Bubbles under Symmetric Information Arbitrage • Historical Securities with Infinite Maturity Bubbles • Backwards induction argument fails since there is no well Symmetric defined final period Information Pricing Equation • “Lack of market clearing at t = ∞” Ruling out f • Split the price in a fundamental component pt and a Asymmetric Information bubble component bt . Expected/Strong Bubble • By pricing equation, we get the following expectational Necessary Conditions difference equation Limits to ˆ 1 Arbitrage Q bt = Et bt+1 Noise Trader f Risk 1 + rt Synchronization Risk • Example 1: deterministic bubble ⇒ has to grow at the risk-free rate • Example 2 (Blanchard & Watson 1982): (risk-neutral investors) • bubble bursts in each period with prob. (1 − π), persists with prob. π f 1+rt • ⇒ bubble has to grow by a factor π (if it doesn’t burst) Asset Pricing under Asym. Information Limits to Bubbles under Symmetric Information Arbitrage • Historical How can we rule out bubbles? Bubbles • Negative bubbles (Blanchard & Watson 1982, Diba & Symmetric Grossman 1988) Information • For b < 0 difference equation implies that p will become Pricing Equation t t Ruling out negative. Asymmetric • Free disposal rules out negative prices. Information Expected/Strong • Positive bubbles on assets with positive net supply if g < r Bubble Necessary (Brock, Scheinkman, Tirole 85, Santos & Woodford 97) Conditions • Argument: (bubbles would outgrow the economy if r > g) Limits to Arbitrage • At any point in time t + τ, the aggregate wealth of the Noise Trader economy contains bubble component b . Risk τ Synchronization • NPVt of aggregate wealth Wt+τ does not converge to Risk zero as τ → ∞ • If aggregate consumptiont+τ is bounded or grows at a rate g < r, NPVt+τ (Ct+τ ) → 0 as τ → ∞. • Household wealth exceeds PV of C for all t + τ sufficiently far in the future. • This is inconsistent with optimization since household would consume part of wealth. Asset Pricing under Asym. Information Limits to Bubbles under Symmetric Information Arbitrage Historical Bubbles Symmetric Information • 5 Counter Examples (Santos and Woodford (1997)): Pricing Equation Ruling out • Example 1: fiat money (=bubble) in OLG models Asymmetric • allows (better) intergeneral transfers Information Expected/Strong • without bubble households want to save more and hence Bubble Necessary MRS “implicit r”< g Conditions (can lead to overaccumulation of private capital and Limits to Arbitrage hence, dynamic inefficiency (see also Abel et al. (1989))) Noise Trader Risk • Example 2: ... Synchronization Risk • Common theme: Pure existence of a bubble enlarges the trading space. leads to different allocation and EMM Asset Pricing under Asym. Information Limits to Bubbles under Asymmetric Information Arbitrage Historical Bubbles Symmetric Information • “Dynamic Knowledge Operator” Pricing Equation Ruling out Asymmetric i n dynamic t o Information Kt (E) = ω ∈ Ω : Pi (ω) ⊆ E Expected/Strong Bubble Necessary Conditions • Expected Bubbles versus Strong Bubbles Limits to Arbitrage • expected bubble: Noise Trader Risk pt > every agents marginal valuation at a date state (t, ω) Synchronization Risk • strong bubble: (arbitrage?) pt > all agents know that no possible dividend realization can justify this price. Asset Pricing under Asym. Information Limits to Necessary Conditions for Bubbles Arbitrage Historical Bubbles Symmetric • Model setup in Allen, Morris & Postlewaite 1993: Information Pricing Equation risky asset pays dividend dT (ω):Ω 7−→ + at t = T Ruling out R Asymmetric • Necessary Conditions for Expected Bubbles Information Expected/Strong 1 Initial allocation (interim) Pareto inefficient (Tirole 1982) Bubble Necessary Conditions • rational traders is not willing to buy “bubble asset” since Limits to some traders have realized their gains leaving a negative Arbitrage sum game for the buyers Noise Trader Risk Synchronization 2 Short-sale constraint strictly binds at some future time in Risk some contingency for all i • only don’t sell to the position limit now, since shorting might be more profitable in the future Asset Pricing under Asym. Information Limits to Necessary Conditions for Bubbles Arbitrage Historical Bubbles • Additional Necessary Conditions for Strong Bubbles Symmetric Information 1 asymmetric information is necessary since Pricing Equation Ruling out traders must believe that the other traders do not know Asymmetric this fact. Information Expected/Strong 2 Net trades of all traders cannot be CK (since CK of Bubble Necessary actions negates asymmetric info about events)
Recommended publications
  • INSTITUTIONAL FINANCE Lecture 07: Liquidity, Limits to Arbitrage – Margins + Bubbles DEBRIEFING - MARGINS
    INSTITUTIONAL FINANCE Lecture 07: Liquidity, Limits to Arbitrage – Margins + Bubbles DEBRIEFING - MARGINS $ • No constraints Initial Margin (50%) Reg. T 50 % • Can’t add to your position; • Not received a margin call. Maintenance Margin (35%) NYSE/NASD 25% long 30% short • Fixed amount of time to get to a specified point above the maintenance level before your position is liquidated. • Failure to return to the initial margin requirements within the specified period of time results in forced liquidation. Minimum Margin (25%) • Position is always immediately liquidated MARGINS – VALUE AT RISK (VAR) Margins give incentive to hold well diversified portfolio How are margins set by brokers/exchanges? Value at Risk: Pr (-(pt+1 – pt)¸ m) = 1 % 1% Value at Risk LEVERAGE AND MARGINS j+ j Financing a long position of x t>0 shares at price p t=100: Borrow $90$ dollar per share; j+ Margin/haircut: m t=100-90=10 j+ Capital use: $10 x t j- Financing a short position of x t>0 shares: Borrow securities, and lend collateral of 110 dollar per share Short-sell securities at price of 100 j- Margin/haircut: m t=110-100=10 j- Capital use: $10 x t Positions frequently marked to market j j j payment of x t(p t-p t-1) plus interest margins potentially adjusted – more later on this Margins/haircuts must be financed with capital: j+ j+ j- j- j j+ j- j ( x t m t+ x t m t ) · Wt , where x =xt -xt 1 J with perfect cross-margining: Mt ( xt , …,xt ) · Wt 3.
    [Show full text]
  • Noise Traders - Detailed Derivation
    University of California, Berkeley Summer 2010 ECON 138 Noise Traders - Detailed Derivation A common response to the behavioral anomalies we have discussed in this course is that they have little impact on the long run, because investors with such anomalies are not maximizing optimally, and would thus be driven out of the market. We shall show, however, that noise traders—traders who are not maximizing—can indeed survive even in a very simple market setting. The model is taken out of De Long et al, "Noise Trader Risk in Financial Markets," The Journal of Political Economy, Aug. 1990. I. Setting . Two-period model. Invest in the first period, consume in the second . CARA utility . One risk-free asset with return r and infinite supply . One risky asset with price and pays a dividend of r. Supply is fixed to 1. noise traders and rational traders, . Noise traders misperceive the expected price of the risky asset in the second period by II. Derivation 1. Utility Function We start off with CARA utility Where is the rate of absolute risk aversion and is wealth in dollars.1 If the return of the risky asset is normally distributed, is also normally distributed, and thus utility is log-normally distributed. As we did in the first lecture, we can take log of the utility function and use to get Dividing the above by gives us . 1 This is a bit different from the CARA utility we have seen before, as we are not having a as the numerator. This is alright because is a positive constant as long as the investors are risk averse.
    [Show full text]
  • Time-Varying Noise Trader Risk and Asset Prices∗
    Time-varying Noise Trader Risk and Asset Prices∗ Dylan C. Thomasy and Qingwei Wangz Abstract By introducing time varying noise trader risk in De Long, Shleifer, Summers, and Wald- mann (1990) model, we predict that noise trader risk significantly affects time variations in the small-stock premium. Consistent with the theoretical predictions, we find that when noise trader risk is high, small stocks earn lower contemporaneous returns and higher subsequent re- turns relative to large stocks. In addition, noise trader risk has a positive effect on the volatility of the small-stock premium. After accounting for macroeconomic uncertainty and controlling for time variation in conditional market betas, we demonstrate that systematic risk provides an incomplete explanation for our results. Noise trader risk has a similar impact on the distress premium. Keywords: Noise trader risk; Small-stock premium; Investor sentiment JEL Classification: G10; G12; G14 ∗We thank Mark Tippett for helpful discussions. Errors and omissions remain the responsibility of the author. ySchool of Business, Swansea University, U.K.. E-mail: [email protected] zBangor Business School. E-mail: [email protected] 1 Introduction In this paper we explore the theoretical and empirical time series relationship between noise trader risk and asset prices. We extend the De Long, Shleifer, Summers, and Waldmann (1990) model to allow noise trader risk to vary over time, and predict its impact on both the returns and volatility of risky assets. Using monthly U.S. data since 1960s, we provide empirical evidence consistent with the model. Our paper contributes to the debate on whether noise trader risk is priced.
    [Show full text]
  • Arbitrage, Noise-Trader Risk, and the Cross Section of Closed-End Fund Returns
    Arbitrage, Noise-trader Risk, and the Cross Section of Closed-end Fund Returns Sean Masaki Flynn¤ This version: April 18, 2005 ABSTRACT I find that despite active arbitrage activity, the discounts of individual closed-end funds are not driven to be consistent with their respective fundamentals. In addition, arbitrage portfolios created by sorting funds by discount level show excess returns not only for the three Fama and French (1992) risk factors but when a measure of average discount movements across all funds is included as well. Because the inclusion of this later variable soaks up volatility common to all funds, the observed inverse relationship between the magnitude of excess returns in the cross section and the ability of these variables to explain overall volatility leads me to suspect that fund-specific risk factors exist which, were they measurable, would justify what otherwise appear to be excess returns. I propose that fund-specific noise-trader risk of the type described by Black (1986) may be the missing risk factor. ¤Department of Economics, Vassar College, 124 Raymond Ave. #424, Poughkeepsie, NY 12604. fl[email protected] I would like to thank Steve Ross for inspiration, and Osaka University’s Institute for Social and Economic Studies for research support while I completed the final drafts of this paper. I also gratefully acknowledge the diligent and tireless research assistance of Rebecca Forster. All errors are my own. Closed-end mutual funds have been closely studied because they offer the chance to examine asset pricing in a situation in which all market participants have common knowledge about fundamental valuations.
    [Show full text]
  • Limits of Arbitrage and Corporate Financial Policies*
    LIMITS OF ARBITRAGE AND CORPORATE * FINANCIAL POLICIES Massimo Massa INSEAD Urs Peyer INSEAD Zhenxu Tong INSEAD This Version: July 2006 Abstract: We focus on an exogenous event that changes the cost of equity of the firm—the addition of its stock to the S&P 500 index—and we use it to test capital structure theories in a controlled experiment, where the effect of the index addition on the stock price is exogenous from a manager’s point of view. We investigate how firms modify their corporate financial and investment policies as a reaction to the addition to the index. Consistent with both traditional theories and Stein’s (1996) market timing theory, we find bigger increases in equity issues and investment - partly through more acquisitions – in response to bigger drops in the cost of equity. However, in the 24 months after the index addition, firms that issue equity and increase investment display negative abnormal returns and they perform worse than firms that issue but do not increase investment. This finding is consistent only with the market timing theory of Stein (1996) and supports a “limits of arbitrage” story in which stocks display a downward sloping demand curve and firms themselves act as “arbitrageurs” taking advantage of the window of opportunity provided by the stock price change around the S&P500 index addition. JEL Classification: G32; G30. Keywords: index addition, corporate policy, behavioral finance, limits of arbitrage. *Department of Finance, INSEAD. Please address all correspondence to Massimo Massa, INSEAD, Boulevard de Constance, 77300 Fontainebleau France, Tel: +33160724481, Fax: +33160724045 Email: [email protected].
    [Show full text]
  • 10.4 Excess Volatility in the Models of Financial Markets We
    David Romer July 2016 Preliminary – please do not circulate! 10.4 Excess Volatility In the models of financial markets we have considered so far – the partial-equilibrium models of consumers’ asset allocation and the determinants of investment in Sections 8.5 and 9.7, and the general-equilibrium model of Section 10.1 – asset prices equal their fundamental values. That is, they are the rational expectations, given agents’ information and their stochastic discount factors, of the present value of the assets’ payoffs. This case is the natural benchmark. It is generally a good idea to start by assuming rationality and no market imperfections. Moreover, many participants in financial markets are highly sophisticated and have vast resources at their disposal, so one might expect that any departures of asset prices from fundamentals would be small and quickly corrected. On the other hand, there are many movements in asset prices that, at least at first glance, cannot be easily explained by changes in fundamentals. And perfectly rational, risk neutral agents with unlimited resources standing ready to immediately correct any unwarranted movements in asset prices do not exist. Thus there is not an open-and-shut theoretical case that asset prices can never differ from fundamentals. This section is therefore devoted to examining the possibility of such departures. We start by considering a model, due to DeLong, Shleifer, Summers, and Waldmann (1990), that demonstrates what is perhaps the most economically interesting force making such departures 21 possible. DeLong et al. show departures of prices from fundamentals can be self-reinforcing: the very fact that there can be departures creates a source of risk, and so limits the willingness of rational investors to trade to correct the mispricings.
    [Show full text]
  • On the Survival of Overcon"Dent Traders in a Competitive Securities Marketଝ
    Journal of Financial Markets 4 (2001) 73}84 On the survival of overcon"dent traders in a competitive securities marketଝ David Hirshleifer!, Guo Ying Luo" * !The Ohio State University, College of Business, Department of Finance, 740A Fisher Hall, 2100 Neil Avenue, Columbus, OH 43210-1144, USA "Department of Finance, Faculty of Management, Rutgers University, 94 Rockafeller Rd., Piscataway, NJ 08854-8054, USA Abstract Recent research has proposed several ways in which overcon"dent traders can persist in competition with rational traders. This paper o!ers an additional reason: overcon"- dent traders do better than purely rational traders at exploiting mispricing caused by liquidity or noise traders. We examine both the static pro"tability of overcon"dent versus rational trading strategies, and the dynamic evolution of a population of overcon"dent, rational and noise traders. Replication of overcon"dent and rational types is assumed to be increasing in the recent pro"tability of their strategies. The main result is that the long-run steady-state equilibrium always involves overcon"dent traders as a substantial positive fraction of the population. ( 2001 Elsevier Science B.V. All rights reserved. JEL classixcation: G00; G14 Keywords: Survivorship; Natural selection; Overcon"dent traders; Noise traders 1. Introduction Several recent papers have argued that investor overcon"dence or shifts in con"dence o!er a possible explanation for a range of anomalous empirical ଝWe are grateful to the anonymous referee, Ivan Brick, Kent Daniel, Sherry Gi!ord, Avanidhar Subrahmanyam and the seminar participants at the Northern Finance Association Annual Meeting in Calgary, 1999. for their helpful comments.
    [Show full text]
  • Hedge Funds, Signaling, and Optimal Lockups∗
    Hedge funds, signaling, and optimal lockups∗ Juhani T. Linnainmaa Alan Moreira May 2018 Abstract Many hedge funds restrict investors' ability to redeem their investments. We show that lockups alleviate a delegation friction. In our model hedge funds can enter a long-term trade that increases expected returns but lowers short-term returns. Investors who rationally learn from returns may mis- take a skilled manager who pursues the long-term trade for an unskilled manager. Skilled managers therefore have an incentive to avoid the long-term trade to enhance short-term returns. The tradeoff between the benefits of the long-term trade and investors' fears of being stuck with an unskilled manager determines the optimal lockup. We calibrate the model to hedge fund data and show that arbitrage remains limited even with optimal lockups; the average manager sacrifices 146 basis points in expected returns per year to improve short-term returns. ∗Linnainmaa is with the University of Southern California and NBER. Moreira is with the University of Rochester. We thank Jaroslav Borovicka, Douglas Diamond, Michael Gofman, Lars Hansen, Zhiguo He, John Heaton, Christopher Hrdlicka, Ralph Koijen, Mathias Kronlund, Hong Liu (discussant), Asaf Manela, Tobias Moskowitz, Diogo Palhares, Benjamin Pugsley, Savina Rizova, Alexi Savov, Pietro Veronesi, Robert Vishny, and seminar and conference participants at University of Chicago, University of California at Los Angeles, City University of Hong Kong, Yale University, and 2018 American Finance Association meetings for helpful comments. 1 Introduction Many hedge funds impose an initial lockup period to restrict investors' ability to redeem their invest- ments. Funds with lockup restrictions significantly outperform open-ended funds.1 This performance difference implies that managers also benefit from the self-imposed lockup restriction|if not, man- agers could enhance their own compensation by removing the restriction.
    [Show full text]
  • Behavioral Finance$
    Pacific-Basin Finance Journal 11 (2003) 429–437 www.elsevier.com/locate/econbase Behavioral finance$ Jay R. Ritter* Department of Finance, School of Business Administration, University of Florida, PO Box 117168, Gainesville, FL 32611-7168, USA Abstract This article provides a brief introduction to behavioral finance. Behavioral finance encompasses research that drops the traditional assumptions of expected utility maximization with rational investors in efficient markets. The two building blocks of behavioral finance are cognitive psychology (how people think) and the limits to arbitrage (when markets will be inefficient). The growth of behavioral finance research has been fueled by the inability of the traditional framework to explain many empirical patterns, including stock market bubbles in Japan, Taiwan, and the US. D 2003 Elsevier B.V. All rights reserved. JEL classification: G14; D81 Keywords: Behavioral finance; Arbitrage; Psychology; Market efficiency 1. Introduction Behavioral finance is the paradigm where financial markets are studied using models that are less narrow than those based on Von Neumann–Morgenstern expected utility theory and arbitrage assumptions. Specifically, behavioral finance has two building blocks: cognitive psychology and the limits to arbitrage. Cognitive refers to how people think. There is a huge psychology literature documenting that people make systematic errors in the way that they think: They are overconfident, they put too much weight on recent experience, etc. Their preferences may also create distortions. Behavioral finance uses this body of knowledge rather than taking the arrogant approach that it should be ignored. $ A modified version of this paper was given as a keynote address at the July 2002 APFA/PACAP/FMA meetings in Tokyo.
    [Show full text]
  • The Material Sociology of Arbitrage
    Edinburgh Research Explorer The Material Sociology of Arbitrage Citation for published version: Hardie, I & MacKenzie, D 2012, The Material Sociology of Arbitrage. in K Knorr Cetina & A Preda (eds), The Oxford Handbook of the Sociology of Finance: Oxford Handbooks in Business and Management. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199590162.013.0011 Digital Object Identifier (DOI): 10.1093/oxfordhb/9780199590162.013.0011 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: The Oxford Handbook of the Sociology of Finance General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 The Material Sociology of Arbitrage Iain Hardie and Donald MacKenzie “Arbitrage” is a term with different meanings, but this chapter follows market practitioners in defining it as trading that aims to make low-risk profits by exploiting discrepancies in the price of the same asset or in the relative prices of similar assets.1 A classic example historically was gold arbitrage. If the price of gold in Saudi Arabia exceeds its price in New York by more than the cost of transportation, arbitrageurs can profit by buying gold in New York and selling it in Saudi Arabia (or vice versa if gold is cheaper in Saudi Arabia).
    [Show full text]
  • The Causal Effect of Limits to Arbitrage on Asset Pricing Anomalies
    NBER WORKING PAPER SERIES THE CAUSAL EFFECT OF LIMITS TO ARBITRAGE ON ASSET PRICING ANOMALIES Yongqiang Chu David Hirshleifer Liang Ma Working Paper 24144 http://www.nber.org/papers/w24144 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 December 2017 For helpful comments and suggestions, we thank Karl Diether (Rodney L. White conference discussant), Lukasz Pomorski (Rodney L. White conference discussant), Jeffrey Pontiff (AFA discussant), Lin Sun, and participants at the 2016 Rodney L. White Center for Financial Research Conference on Financial Decisions and Asset Markets at Wharton and the 2017 American Finance Association Meeting. This research has received financial support from the Moore School Research Grant Program. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2017 by Yongqiang Chu, David Hirshleifer, and Liang Ma. 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. The Causal Effect of Limits to Arbitrage on Asset Pricing Anomalies Yongqiang Chu, David Hirshleifer, and Liang Ma NBER Working Paper No. 24144 December 2017 JEL No. G12,G18,G4 ABSTRACT We examine the causal effect of limits to arbitrage on 11 well-known asset pricing anomalies using Regulation SHO, which relaxed short-sale constraints for a random set of pilot stocks, as a natural experiment.
    [Show full text]
  • Investor Sentiment and Short Selling: Do Animal Spirits Sell Stocks?
    Investor sentiment and short selling: do animal spirits sell stocks? A Thesis Presented to The Faculty of the Department of Financial Economics Radboud University In Partial Fulfilment Of the Requirements for the Degree of Master of Science By Paul Reesink July, 2020 Abstract This paper examines the relationship between investor sentiment and short selling. This is done by calculating daily investor sentiment for every stock in the S&P 500 between January 1990 and December 2019 and group the stocks into four portfolios based on their investor sentiment. The effect of relative overpricing of stocks on their short interest ratio is compared for the different sentiment portfolios. Main result is that the relationship between relative overpricing and the short interest ratio is strongly positive for the highest investor sentiment portfolio and becomes less positive when relative investor sentiment declines. Taking economic recession periods into account does not seem to change the relationship between investor sentiment and short selling, although the effect of the relative overpricing on the short interest ratio becomes less positive for all sentiment portfolios. Keywords: Behavioural finance, firm-specific investor sentiment, short interest ratio, return anomalies 2 Table of contents Page Abstract 2 Table of contents 3 Chapter I. Introduction 4 II. Literature 5 III. Research method 12 IV. Results 17 V. Conclusion and discussion 20 References 23 3 I. Introduction In classical finance, with rational investors and full information, deviations from fundamental value would be immediately exploited by arbitrageurs who bring back the value to their fundamentals. However, episodes like the Great Depression, Black Monday and the dotcom-bubble make it unlikely that stock prices are entirely based on fundamentals.
    [Show full text]