Joshua D. Coval

Jay O. Light Professor of Business Administration

Joshua Coval, Professor of Business Administration in the Finance Area, joined HBS in July 2001. Prior to joining HBS, Joshua was an Assistant Professor of Finance at the University of Michigan Business School where he was on the faculty since 1996.

Joshua's research focuses on the efficiency of security prices and examination of rational and behavioral sources of mispricing. His current research investigates the structured finance market and how investor reliance on ratings and unsound pricing models led to the spectacular rise and collapse thereof.  His research has been published in the Journal of Finance, the Journal of Financial Economics, the Journal of Political Economy, the Review of Financial Studies, the Journal of Business, and the Journal of Corporate Finance. His research awards include the 2000 and 2005 Smith-Breeden Prize for the best paper in the Journal of Finance. His research has been featured in The Economist, The Wall Street Journal, The New York Times, The Chicago Tribune, Time, Money Magazine, and The Financial Times.

Joshua received his B.A. and M.A. in Economics from the University of Chicago in 1992 and his Ph.D. in Business Economics from the Anderson School at UCLA in 1996.

2/09

 

 

Books

Journal Articles

  1. Do Powerful Politicians Cause Corporate Downsizing?

    This paper employs a new empirical approach for identifying the impact of government spending on the private sector. Our key innovation is to use changes in congressional committee chairmanship as a source of exogenous variation in state-level federal expenditures. In doing so, we show that fiscal spending shocks appear to significantly dampen corporate sector investment and employment activity. This retrenchment follows both Senate and House committee chair changes, occurs in large and small firms and within large and small states, and is most pronounced among geographically concentrated firms. The effects are economically meaningful and the mechanism-entirely distinct from the more traditional interest rate and tax channels-suggests new considerations in assessing the impact of government spending on private sector economic activity.

    Keywords: Spending; Private Sector; Taxation; Innovation and Invention; Interest Rates; Business and Government Relations; Investment; Employment; Power and Influence;

    Citation:

    Cohen, Lauren, Joshua Coval, and Christopher J. Malloy. "Do Powerful Politicians Cause Corporate Downsizing?" Journal of Political Economy 119, no. 6 (December 2011): 1015–1060. (Click here for a response to Snyder and Welch, click here for the data, and click here for the code.) View Details
  2. The Economics of Structured Finance

    This paper investigates the spectacular rise and fall of structured finance. The essence of structured finance activities is the pooling of economic assets like loans, bonds, and mortgages, and the subsequent issuance of a prioritized capital structure of claims, known as tranches, against these collateral pools. As a result of the prioritization scheme used in structuring claims, many of the manufactured tranches are far safer than the average asset in the underlying pool. This ability of structured finance to repackage risks and to create "safe" assets from otherwise risky collateral led to a dramatic expansion in the issuance of structured securities, most of which were viewed by investors to be virtually risk-free and certified as such by the rating agencies. At the core of the recent financial market crisis has been the discovery that these securities are actually far riskier than originally advertised. We examine how the process of securitization allowed trillions of dollars of risky assets to be transformed into securities that were widely considered to be safe. We highlight two features of structured finance products—the extreme fragility of their ratings to modest imprecision in evaluating underlying risks, and their exposure to systematic risks—that go a long way in explaining the spectacular rise and fall of structured finance. We conclude with an assessment of what went wrong and the relative importance of rating agency errors, investor credulity, and perverse incentives and suspect behavior on the part of issuers, rating agencies, and borrowers.

    Keywords: Financial Crisis; Asset Management; Debt Securities; Investment; Risk Management; Behavior;

    Citation:

    Coval, Joshua D., Jakub W. Jurek, and Erik Stafford. "The Economics of Structured Finance." Journal of Economic Perspectives 23, no. 1 (winter 2009): 3–25. View Details
  3. Corporate Financing Decisions When Investors Take the Path of Least Resistance

    We explore the consequences for corporate financial policy that arise when investors exhibit inertial behavior. One implication of investor inertia is that, all else equal, a firm pursuing a strategy of equity-financed growth will prefer a stock-for-stock merger to greenfield investment financed with an SEO. With a merger, acquirer stock is placed in the hands of investors, who, because of inertia, do not resell it all on the open market. If there is downward-sloping demand for acquirer shares, this leads to less price pressure than an SEO, and cheaper equity financing as a result. We develop a simple model to illustrate this idea, and present supporting empirical evidence. Both individual and institutional investors tend to hang on to shares granted them in mergers, with this tendency being much stronger for individuals. Consistent with the model and with this cross-sectional pattern in inertia, acquirers targeting firms with high institutional ownership experience more negative announcement effects and greater announcement volume. Moreover, the results are strongest when the overlap in target and acquirer institutional ownership is low and when the demand curve for the acquirer's shares appears to be steep.

    Keywords: Behavior; Investment; Policy; Corporate Finance;

    Citation:

    Baker, Malcolm, Joshua Coval, and Jeremy Stein. "Corporate Financing Decisions When Investors Take the Path of Least Resistance." Journal of Financial Economics 84, no. 2 (May 2007): 266–298. View Details
  4. Do Behavioral Biases Affect Prices?

    Keywords: Behavior; Prejudice and Bias; Price;

    Citation:

    Coval, Joshua D., and Tyler Shumway. "Do Behavioral Biases Affect Prices?" Journal of Finance 60, no. 1 (February 2005): 1–34. (

    Winner of Smith Breeden Prize. Best Paper For the best finance research paper published in the Journal of Finance presented by Smith Breeden Associates, Inc.​

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  5. Home Bias at Home: Local Equity Preference in Domestic Portfolios

    Keywords: Prejudice and Bias; Local Range; Investment;

    Citation:

    Coval, Joshua D., and Tobias J. Moskowitz. "Home Bias at Home: Local Equity Preference in Domestic Portfolios." Journal of Finance 54 (December 1999). (

    Winner of Smith Breeden Prize. Best Paper For the best finance research paper published in the Journal of Finance presented by Smith Breeden Associates, Inc.​

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Working Papers

Cases and Teaching Materials

  1. Tottenham Hotspur plc

    Tottenham Hotspur Football Club is a publicly-owned professional soccer team based in London, England. The club's chairman, Daniel Levy, is contemplating a significant investment in physical assets, including the development of a new stadium as well as the acquisition of a new player. The team must decide if the expected cash flows associated with adding the stadium, the player, or both, warrant the considerable required investments in these assets.

    Keywords: Valuation; Capital Budgeting; Decisions; Competency and Skills; Cash Flow; Investment; Assets; Buildings and Facilities; Sports; Sports Industry; London;

    Citation:

    Cohen, Lauren H., Joshua D. Coval, and Christopher J. Malloy. "Tottenham Hotspur plc." Harvard Business School Case 209-059, November 2008. (Revised August 2012.) View Details
  2. Samoa Tala

    This case examines currency risks faced by Microfinance Institutions, and evaluates strategies to hedge them in countries with pegged currency regimes and no derivatives markets. An MFI based in Western Samoa borrows in different currencies like the US dollar and the New Zealand dollar is worried about the additional variability in its cash flows due to unexpected currency fluctuations, and wants to explore strategies to hedge this risk in the absence of a derivatives market for the Samoan Tala. It seems to the president of the company that borrowing in different currencies, in proportions equal to the weights of the currencies in the basket peg, would reduce the currency risk. He wants to estimate the exact weights of the currencies in the basket peg and measure the reduction of currency risk using this strategy.

    Keywords: Cash Flow; Currency Exchange Rate; Microfinance; Risk and Uncertainty; Financial Services Industry; Samoa;

    Citation:

    Coval, Joshua D., Bhagwan Chowdhry, and Konark Saxena. "Samoa Tala." Harvard Business School Case 209-053, September 2008. View Details
  3. Dynamic Markets

    The Dynamic Markets course at Harvard Business School is organized around the hands-on application of financial decision making in a wide variety of capital market settings. The course relies heavily on in-class simulations of a range of market settings where students compete with their classmates for profits. The main pedagogical approach used in the course is what we call deriving by doing. The essential aspects of this pedagogy are dynamic decision settings, a strong reliance on competitive markets, and derivation of core concepts through active student decision-making. The upTick financial simulation software, developed at the Harvard Business School, is used to realistically recreate classic decision-settings in a competitive classroom setting. We convey the timing and uncertainty inherent in real-world finance problems by presenting the "case facts" sequentially (i.e., as they become available to the real-world decision maker), thereby allowing students to modify or reverse decisions as new information become available, and to respond strategically to the decisions of their competitors. Additionally, we clear student decisions in realistic capital markets, such that equilibrium outcomes are determined by competitive student interaction. Even though students participate in markets corresponding to a particular setting, the prices determined in the simulations are set by the participants and can depart from the historical prices within bounds set by the instructor.

    Keywords: Value Creation; Decision Making; Capital Markets; Competitive Strategy; Profit; Software; Information; Strategy; Price; Outcome or Result; Curriculum and Courses; Theory;

    Citation:

    Coval, Joshua D., and Erik Stafford. "Dynamic Markets." Harvard Business School Course Overview Note 208-143, March 2008. View Details
  4. Convertible Arbitrage

    The goal of this simulation is to understand how convertible bonds can be viewed as a portfolio of simpler securities and to introduce an over-the-counter market. The convertible bonds that are available during the simulation are at-the-money and in-the-money so that changing credit risk exposure is not much of an issue. A convertible bond can be viewed as a simple coupon paying corporate bond plus a conversion option. A bond pricing model discounts the promised payments at a rate that compensates for time, risk, and expected loss (maturity matched Treasury yield plus a credit rating matched yield spread). The conversion option can be valued using the Black-Scholes call option pricing formula. The key is to recognize that each conversion option (one per bond) is equivalent to several equity call options (the conversion ratio determines how many equity options are implicit in each bond).

    Keywords: Bonds; Investment Portfolio; Price; Risk Management; Mathematical Methods;

    Citation:

    Coval, Joshua, and Erik Stafford. "Convertible Arbitrage." Harvard Business School Background Note 208-116, January 2008. View Details
  5. Equity Derivatives

    The goal of these simulations is to understand the dynamic replication technique behind the Black-Scholes/Merton options model. The simulations focus on a single stock and a risk-free discount bond, which are used to replicate a contingent payoff. The underlying stock and bond prices are randomly generated from the assumptions of the model, so that this simulation is testing the student's understanding and ability to use the model, rather than testing whether the model accurately explains prices. In each of the four simulations that make up this lesson, students are trying to replicate a contingent payoff, which is specified in terms of the closing stock price in one month (European-style derivative). The students are essentially working on an equity derivatives desk at a large bank and are responsible for delivering a derivative payoff to a client. The desk has taken in a premium upfront for guaranteeing the contingent payoff in one month's time. In the Black-Scholes/Merton model, a trader should be able to exactly match the contractual payment at expiration. Therefore, students are penalized based on the absolute difference between their actual ending value and a target ending value (starting value + derivative payoff). In particular, this difference is cumulated across all four simulations and then subtracted from their account.

    Keywords: Equity; Bonds; Stocks; Price; Risk Management;

    Citation:

    Coval, Joshua, and Erik Stafford. "Equity Derivatives." Harvard Business School Background Note 208-117, January 2008. View Details
  6. Equity Options

    The goal of this simulation is to understand the reliance of option values on volatility. When an investor trades an option, they are essentially trading volatility. Therefore, much of the focus in this lesson is on forecasting volatility. Students are able to use two primary methods for forecasting volatility in this lesson-historical and implied. Students are provided with a historical dataset, from which they can estimate historical volatility of the stock returns. They can also use the dataset to study various statistical relations between the securities. In particular, two of the three securities behave independently of the others. Thus, students are able to analyze the dataset to form views of how the security prices are likely to evolve relative to each other.

    Keywords: Volatility; Forecasting and Prediction; Stock Options; Investment Return; Price; Market Transactions; Mathematical Methods; Value;

    Citation:

    Coval, Joshua, and Erik Stafford. "Equity Options." Harvard Business School Background Note 208-118, January 2008. View Details
  7. Index Options

    The goal of this simulation is to understand the patterns in index option prices that are not predicted by the Black-Scholes model. In particular, the simulation focuses on two properties of options prices. First, at-the-money implied volatilities from index options tend to be larger than the realized volatility. Second, the implied volatilities from index options are increasing as the strike price falls relative to the current index level (i.e., out-of-the-month call options have larger implied volatilities than at-the-money call options). Students are given a dataset of relevant market information to analyze. From these materials, students are expected to develop an investment strategy that attempts to deliver low-risk profits from the index options market. The actual simulation is fairly short and simple. Students trade 1-month put and call options on the S&P 500 (SPX) at three different strike prices (10% out-of-the-money, at-the-money, and 10% in-the-money). The simulation covers five months of calendar time (5 sets of options) in about 35 minutes.

    Keywords: Volatility; Stock Options; Investment; Price; Profit; Risk Management; Mathematical Methods;

    Citation:

    Coval, Joshua, and Erik Stafford. "Index Options." Harvard Business School Background Note 208-119, January 2008. View Details
  8. Valuing Risky Debt

    This lesson develops the classical structural approach to pricing and hedging credit risk: Merton's (1974) contingent claims model of debt and equity claims. This model is used to make investment and risk management decisions in an over-the-counter (OTC) market for distressed bonds.

    Keywords: Borrowing and Debt; Credit; Investment; Price; Risk Management; Mathematical Methods; Valuation;

    Citation:

    Coval, Joshua, and Erik Stafford. "Valuing Risky Debt." Harvard Business School Background Note 208-111, January 2008. View Details
  9. Collateralized Debt Obligations (CDOs)

    This lesson integrated Merton's (1974) contingent claims model of debt and equity claims with the CAPM, which allows us to examine the risks and pricing of credit portfolios and the derivative claims issued against them. In particular, this model is used to make investment and risk management decisions in the market for collateralized debt obligations (CDOs).

    Keywords: Decision Choices and Conditions; Borrowing and Debt; Credit Derivatives and Swaps; Investment Portfolio; Risk Management;

    Citation:

    Coval, Joshua, and Erik Stafford. "Collateralized Debt Obligations (CDOs)." Harvard Business School Background Note 208-113, January 2008. View Details
  10. Asset Allocation I

    The goal of these simulations is to understand the mathematics of mean-variance optimization and the equilibrium pricing of risk if all investors use this rule with common information sets. Simulation A focuses on five to 10 years of monthly sector returns that are initially drawn from a known multivariate normal distribution. Mean-variance optimization is designed to produce the highest ratio of excess portfolio return to portfolio standard deviation (i.e. the highest Sharpe ratio) in this setting. Simulation B alters the setting by allowing students to determine expected returns through a simultaneous auction. We continue to have agreement over the covariance matrix, and implicitly over expected payoffs, but allow students to set market prices. The average portfolio weights across the 10 sectors is calculated and is used as the vector of market capitalization weights. With these market weights (w) and the given covariance matrix, the capital asset pricing model (CAPM) implied expected returns are calculated for each sector and compared with the student set expected returns.

    Keywords: Asset Pricing; Capital; Investment Return; Risk Management; Mathematical Methods;

    Citation:

    Coval, Joshua D., Erik Stafford, Rodrigo Osmo, John Jernigan, Zack Page, and Paulo Passoni. "Asset Allocation I." Harvard Business School Background Note 208-086, November 2007. View Details
  11. Event Arbitrage

    The event arbitrage module includes two simulation sessions. The first simulation focuses on analyzing and evaluating individual merger transactions, while the second simulation emphasizes managing a portfolio of individual positions and the limitations of arbitrage investing in real-world capital markets. The underlying data and information are derived from actual merger transactions and have been disguised to prevent students from knowing the outcome ahead of time.

    Keywords: Mergers and Acquisitions; Capital Markets; Financial Management; Investment Portfolio; Risk Management;

    Citation:

    Coval, Joshua D., and Erik Stafford. "Event Arbitrage." Harvard Business School Background Note 208-090, November 2007. View Details
  12. Bayesian Estimation & Black-Litterman

    Describes a practical method for asset allocation that is more robust to estimation errors than the traditional implementation of mean-variance optimization with sample means and covariances. The Bayesian inspired Black-Litterman model is described after introducing the intuition of the Bayesian approach to inference in a univariate setting.

    Keywords: Asset Management; Investment Portfolio; Mathematical Methods;

    Citation:

    Coval, Joshua D., and Erik Stafford. "Bayesian Estimation & Black-Litterman." Harvard Business School Background Note 208-085, November 2007. View Details
  13. Price Formation (TN)

    Teaching Note to (2-205-076) and (2-205-077).

    Keywords: Price;

    Citation:

    Coval, Joshua D., and Erik Stafford. "Price Formation (TN)." Harvard Business School Teaching Note 205-078, June 2005. (Revised October 2007.) View Details
  14. Price Formation

    Investigates how prices are formed in competitive capital markets. Focuses on a single security called AOE. Students compete with computer traders and each other for market making and informed trading profits. Participants receive a variety of public news in the form of a research report on AOE, as well as subscriptions to news announcements and public quarterly earnings forecasts and releases. Participants also have access to costly private information in the form of one-week-ahead price targets for a per-use fee. The market structure is one with a centralized limit order book, but the ability to place limit orders is limited. The simulation of AOE is based on an actual security that has been disguised in time and industry to prevent students from anticipating the price path. All public news and contextual market information presented to students during the simulation correspond to actual information available to market participants in the real world at the time.

    Keywords: Capital Markets; Price; Profit; Corporate Disclosure; Newsletters; Industry Structures; Business Processes; Competitive Strategy;

    Citation:

    Coval, Joshua D., and Erik Stafford. "Price Formation." Harvard Business School Background Note 208-040, October 2007. View Details
  15. Market Efficiency

    Covers how prices react to information, the incentives for bringing information into prices, and the paradox of market efficiency in equilibrium--for investors to work hard keeping markets efficient, they must always be somewhat inefficient at the margin. Uses separate financial market simulation software.

    Keywords: Market Design; Price;

    Citation:

    Coval, Joshua D., Erik Stafford, Rodrigo Osmo, John Jernigan, Zachary Page, and Paul Passoni. "Market Efficiency." Harvard Business School Background Note 205-081, June 2005. (Revised October 2007.) View Details
  16. The Law of One Price

    Demonstrates the Law of One Price in practice. Using synthetic securities, students should observe opportunities to earn profits when spreads emerge between portfolios that offer identical payoffs. Uses separate uptick financial simulation software.

    Keywords: Price;

    Citation:

    Coval, Joshua D., Erik Stafford, Rodrigo Osmo, John Jernigan, Zack Page, and Paulo Passoni. "The Law of One Price." Harvard Business School Background Note 205-079, June 2005. (Revised October 2007.) View Details
  17. Partners Healthcare

    Focuses on the portfolio allocation decision of a passive fund manager. Provides a setting to study portfolio theory, including mean-variance analysis, the capital market line, and the efficient frontier.

    Keywords: Investment Portfolio; Capital Markets; Business or Company Management; Decisions; Health Industry;

    Citation:

    Coval, Joshua D. "Partners Healthcare." Harvard Business School Case 206-005, August 2005. (Revised May 2007.) View Details
  18. Williams, 2002

    Williams, a Tulsa, Oklahoma-based firm in various energy businesses, must decide whether to accept a financing package offered by Berkshire Hathaway and Lehman Brothers. The proposed one-year credit facility would provide the firm with financial resources in a difficult period.

    Keywords: Financial Management; Crisis Management; Credit; Capital Structure; Financial Strategy; Financing and Loans; Financial Instruments; Energy Industry; United States;

    Citation:

    Coval, Joshua, Robin Greenwood, and Peter Tufano. "Williams, 2002." Harvard Business School Case 203-068, December 2002. (Revised October 2013.) View Details
  19. Note on Credit Markets

    Covers various aspects of credit markets, including discounting and pricing, team structure, and default.

    Keywords: Financial Markets; Marketing Strategy; Price; Groups and Teams; Organizational Structure; Credit;

    Citation:

    Coval, Joshua D., Peter Tufano, and Ivo Welch. "Note on Credit Markets." Harvard Business School Background Note 203-069, December 2002. (Revised January 2004.) View Details