Riding the Yield Curve: Diversification of Strategies

Riding the yield curve, the fixed-income strategy of purchasing a longer-dated security and selling before maturity, has long been a popular means to achieve excess returns compared to buying-and-holding, despite its implicit violations of market efficiency and the pure expectations hypothesis of the term structure. This paper looks at the historic excess returns of different strategies across three countries and proposes several statistical and macro-based trading rules which seem to enhance returns even more. While riding based on the Taylor Rule works well even for longer investment horizons, our empirical results indicate that, using expectations implied by Fed funds futures, excess returns can only be increased over short horizons. Furthermore, we demonstrate that duration-neutral strategies are superior to standard riding on a risk- adjusted basis. Overall, our evidence stands in contrast to the pure expectations hypothesis and points to the existence of risk premia which may be exploited consistently.Term Structure, Interest Rates, Market Efficiency, Taylor Rule

Uses and Misuses of the Black-Litterman Model in Portfolio Construction

The Black-Litterman model has gained popularity in applications in the area of quantitative equity portfolio management. Unfortunately, many recent applications of the Black-Litterman to novel aspects of quantitative portfolio management have neglected the rigor of the original Black-Litterman modelling. In this article, we critically examine some of these applications from a Bayesian perspective. We identify three reasons why these applications may create losses to investors. These three reasons are: (1) Using a prior without anchoring the prior to an equilibrium model, (2) Using a prior and an equilibrium model that conflict with one another, and (3) Ignoring the implications of the estimation error of the variance-covariance matrix. We also quantify the loss first analytically, and also numerically based on historical data on 10 major world stock market indices. Our conservative estimate of the loss is around a 1% reduction in the annualized return of the portfolio

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal

A rare analytical look at the financial crisis using simple analysis The economic crisis that began in 2008 revealed the numerous problems in our financial system, from the way mortgage loans were produced to the way Wall Street banks leveraged themselves. Curiously enough, however, most of the reasons for the banking collapse are very similar to the reasons that Long-Term Capital Management (LTCM), the largest hedge fund to date, collapsed in 1998. The Crisis of Crowding looks at LTCM in greater detail, with new information, for a more accurate perspective, examining how the subsequent hedge funds started by Meriwether and former partners were destroyed again by the lapse of judgement in allowing Lehman Brothers to fail. Covering the lessons that were ignored during LTCM\u27s collapse but eventually connected to the financial crisis of 2008, the book presents a series of lessons for hedge funds and financial markets, including touching upon the circle of greed from homeowners to real estate agents to politicians to Wall Street. Guides the reader through the real story of Long-Term Capital Management with accurate descriptions, previously unpublished data, and interviews Describes the lessons that hedge funds, as well as the market, should have learned from LTCM\u27s collapse Explores how the financial crisis and LTCM are a global phenomena rooted in failures to account for risk in crowded spaces with leverage Explains why quantitative finance is essential for every financial institution from risk management to valuation modeling to algorithmic trading Is filled with simple quantitative analysis about the financial crisis, from the Quant Crisis of 2007 to the failure of Lehman Brothers to the Flash Crash of 2010 A unique blend of storytelling and sound quantitative analysis, The Crisis of Crowding is one of the first books to offer an analytical look at the financial crisis rather than just an account of what happened. Also included are a layman\u27s guide to the Dodd-Frank rules and what it means for the future, as well as an evaluation of the Fed\u27s reaction to the crisis, QE1, QE2, and QE3.https://repository.usfca.edu/read_books/1050/thumbnail.jp

Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management

Quantitative Equity Portfolio Management is a comprehensive guide to the entire process of constructing and managing a high-yield quantitative equity portfolio. This detailed handbook begins with the basic principles of quantitative active management and then clearly outlines how to build an equity portfolio using those powerful concepts. Financial experts Ludwig Chincarini and Daehwan Kim provide clear explanations of topics ranging from basic models, factors and factor choice, and stock screening and ranking…to fundamental factor models, economic factor models, and forecasting factor premiums and exposures. Readers will also find step-by-step coverage of portfolio weights… rebalancing and transaction costs…tax management…leverage…market neutral…Bayesian _…performance measurement and attribution…the back testing process…and portfolio performance. Filled with proven investment strategies and tools for developing new ones, Quantitative Equity Portfolio Management features: A complete, easy-to-apply methodology for creating an equity portfolio that maximizes returns and minimizes risks The latest techniques for building optimization into a professionally managed portfolio An accompanying CD with a wide range of practical exercises and solutions using actual historical stock data An excellent melding of financial theory with real-world practice A wealth of down-to-earth financial examples and case studies Each chapter of this all-in-one portfolio management resource contains an appendix with valuable figures, tables, equations, mathematical solutions, and formulas. In addition, the book as a whole has appendices covering a brief history of financial theory, fundamental models of stock returns, a basic review of mathematical and statistical concepts, an entertaining explanation and quantitative approach to the casino game of craps, and other on-target supplemental materials. An essential reference for professional money managers and students taking advanced investment courses, Quantitative Equity Portfolio Management offers a full array of methods for effectively developing high-performance equity portfolios that deliver lucrative returns for clients.https://repository.usfca.edu/read_books/1051/thumbnail.jp

Uses and Misuses of the Black-Litterman Model in Portfolio Construction

The Black-Litterman model has gained popularity in applications in the area of quantitative equity portfolio management. Unfortunately, many recent applications of the Black-Litterman to novel aspects of quantitative portfolio management have neglected the rigor of the original Black-Litterman modelling. In this article, we critically examine some of these applications from a Bayesian perspective. We identify three reasons why these applications may create losses to investors. These three reasons are: (1) Using a prior without anchoring the prior to an equilibrium model, (2) Using a prior and an equilibrium model that conflict with one another, and (3) Ignoring the implications of the estimation error of the variance-covariance matrix. We also quantify the loss first analytically, and also numerically based on historical data on 10 major world stock market indices. Our conservative estimate of the loss is around a 1% reduction in the annualized return of the portfolio