Long-Term Capital Management: the Dangers of Leverage
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DIVIDEND GROWERS DOMINATE OTHER DIVIDEND CATEGORIES June 7, 2021
DIVIDEND GROWERS DOMINATE OTHER DIVIDEND CATEGORIES June 7, 2021 Dividend growers outperformed all dividend categories for the past 48 1/4 years with less risk. This is our conclusion based on data provided by Ned Davis Research. The research focuses on dividend payers, non-dividend payers, and dividend cutters. Note that the pattern of dividend growers outperformance holds for both the entire period of analysis and each sub-period (First Table). It also holds that growers experienced lower volatility for both the entire period and each sub-period (Second Table). In this note, we address why we think this phenomenon exists and what it could mean to investors. RETURN BY DIVIDEND CATEGORY (ANNUAL) Period Increased Paid No Change Not Paid Cut 1973-79 5.9% 5.3% 4.9% 1.7% -3.9% 1980-89 17.8% 16.3% 12.8% 8.6% 8.4% 1990-99 13.2% 12.0% 9.4% 10.3% 6.3% 2000-09 3.5% 2.4% 0.3% -6.5% -12.2% 2010-19 12.9% 11.6% 7.9% 9.3% -1.1% 1973-April 21 10.6% 9.5% 7.0% 4.8% -0.8% VOLATILITY BY DIVIDEND CATEGORY (ANNUAL) Period Increased Paid No Change Not Paid Cut 1973-79 19.2% 19.5% 20.7% 25.5% 23.7% 1980-89 17.3% 17.6% 18.9% 21.2% 21.5% 1990-99 13.9% 13.9% 14.4% 18.5% 17.1% 2000-09 15.9% 18.1% 20.1% 27.2% 31.8% 2010-19 12.7% 13.4% 15.8% 16.1% 21.9% 1973-April 21 16.1% 16.9% 18.5% 22.2% 25.1% Source: Ned Davis Research; WCA WHY DIVIDEND GROWERS GENERATE SUPERIOR RISK-ADJUSTED RETURNS We believe there are five main reasons why investors should prefer dividend growers based on years of research and study. -
Progress Report on the Role of Communication During the Lifetime of Long‐Term Capital Management
Progress Report on the Role of Communication during the Lifetime of Long‐Term Capital Management Jessica Lo, Sophia Lu, Joseph Xi, Anthony Zhu (Red Team – 202) November 1, 2010 Introduction Long‐Term Capital Management (LTCM) was a hedge fund that was extremely successful in the early 1990s, but a series of unexpected events led to its abrupt downfall. While most commentary focuses on the technical aspects of LTCM, this report will examine the role of communication during the hedge fund’s lifetime. We will look at the ways in which LTCM’s internal and external communication with its clients and counterparties affected its success and failure. Specifically, we will analyze how LTCM’s communication practices changed over time in response to the changing financial environment. During the rise of LTCM, not only was there limited communication between LTCM’s traders, but LTCM was also unwilling to disclose much information to its investors. However, during the downfall of LTCM, LTCM had to reveal its positions and trades to the public in an attempt to raise capital and to save the fund from failing. It should be noted that the purpose of this report is not solely to attribute LTCM’s downfall to flaws in its communication practices. Instead, this report aims to identify communicative events, such as lack of communication and excessive communication, which contributed to LTCM’s downfall. In studying LTCM and its communication practices, we read and analyzed a variety of documents that offer differing perspectives on both the technical and social aspects of LTCM. We categorized the documents we read as either a primary document or secondary document, and provided a brief synopsis of each document. -
Use of Leverage, Short Sales, and Options by Mutual Funds
Use of Leverage, Short Sales, and Options by Mutual Funds Paul Calluzzo, Fabio Moneta, and Selim Topaloglu* This draft: March 2017 Abstract We study the use of leverage, short sales, and options by equity mutual funds. Consistent with agency-induced motives for the use of these complex instruments, we find that they are often used by poorly monitored funds, and are associated with poor outcomes for investors such as lower performance, higher idiosyncratic risk, more negative skewness, greater kurtosis, and higher fees. Consistent with moral hazard, we also find that mutual funds that use these instruments hold riskier equity positions. Mutual funds attempt to use complex instruments to reduce the risk of their portfolios but in an imperfect and costly way. JEL Classification: G11, G23 Keywords: mutual funds, leverage, short sales, options, complex instruments, fees, performance, risk * Smith School of Business, Queen's University, 143 Union Street, Kingston, Ontario K7L 3N6, Canada. Emails: [email protected], [email protected], and [email protected]. We thank George Aragon, Laurent Barras and seminar participants at the 2nd Smith-Ivey Finance Workshop and the 2016 Telfer Annual Conference on Accounting and Finance for their helpful comments. Electronic copy available at: https://ssrn.com/abstract=2938146 I can resist anything except temptation. -Oscar Wilde 1. Introduction Over the past fifteen years there has been a rise in the complexity of mutual funds as more funds are given the authority to use leverage, short sales, and options. Over this period 42.5% of domestic equity funds have reported using at least one of these instruments. -
2017 Financial Services Industry Outlook
NEW YORK 535 Madison Avenue, 19th Floor New York, NY 10022 +1 212 207 1000 SAN FRANCISCO One Market Street, Spear Tower, Suite 3600 San Francisco, CA 94105 +1 415 293 8426 DENVER 999 Eighteenth Street, Suite 3000 2017 Denver, CO 80202 FINANCIAL SERVICES +1 303 893 2899 INDUSTRY REVIEW MEMBER, FINRA / SIPC SYDNEY Level 2, 9 Castlereagh Street Sydney, NSW, 2000 +61 419 460 509 BERKSHIRE CAPITAL SECURITIES LLC (ARBN 146 206 859) IS A LIMITED LIABILITY COMPANY INCORPORATED IN THE UNITED STATES AND REGISTERED AS A FOREIGN COMPANY IN AUSTRALIA UNDER THE CORPORATIONS ACT 2001. BERKSHIRE CAPITAL IS EXEMPT FROM THE REQUIREMENTS TO HOLD AN AUSTRALIAN FINANCIAL SERVICES LICENCE UNDER THE AUSTRALIAN CORPORATIONS ACT IN RESPECT OF THE FINANCIAL SERVICES IT PROVIDES. BERKSHIRE CAPITAL IS REGULATED BY THE SEC UNDER US LAWS, WHICH DIFFER FROM AUSTRALIAN LAWS. LONDON 11 Haymarket, 2nd Floor London, SW1Y 4BP United Kingdom +44 20 7828 2828 BERKSHIRE CAPITAL SECURITIES LIMITED IS AUTHORISED AND REGULATED BY THE FINANCIAL CONDUCT AUTHORITY (REGISTRATION NUMBER 188637). www.berkcap.com CONTENTS ABOUT BERKSHIRE CAPITAL Summary 1 Berkshire Capital is an independent employee-owned investment bank specializing in M&A in the financial services sector. With more completed transactions in this space than any Traditional Investment Management 6 other investment bank, we help clients find successful, long-lasting partnerships. Wealth Management 9 Founded in 1983, Berkshire Capital is headquartered in New York with partners located in Cross Border 13 London, Sydney, San Francisco, Denver and Philadelphia. Our partners have been with the firm an average of 14 years. We are recognized as a leading expert in the asset management, Real Estate 17 wealth management, alternatives, real estate and broker/dealer industries. -
Examination of Var After Long Term Capital Management
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Munich RePEc Personal Archive MPRA Munich Personal RePEc Archive Examination of VaR after long term capital management Hakan Yalincak and Yu Li and Mike Tong New York University 2. May 2005 Online at https://mpra.ub.uni-muenchen.de/40152/ MPRA Paper No. 40152, posted 18. July 2012 21:04 UTC _______________________________________________________ Examination of VaR After Long Term Capital Management by Hakan Yalincak, Yu Li and Mike Tong* New York University Risk Management In Financial Institutions (Spring 2005) __________________________________________________________________ Abstract The 1998 failure of Long-Term Capital Management (‘LTCM’), a very large and prominent Greenwich, Connecticut based hedge fund, is said to have nearly brought down the world financial system. Over the years, few financial debacles such as LTCM, have been so often written about or discussed without a firm conclusion on what went wrong. What brought the “genius” managers of LTCM to their knees? Was it hubris, or was it something more? Various commentators have jumped on LTCM’s significant leverage ratio or engaged in second-guessing of management’s decision in 1997 to return $2.7 billion of investor capital to increase leverage, and thereby, returns. Others have faulted the lack of transparency at LTCM or faulted regulators for a lack of oversight, criticized regulators for arranging the bailout, while others still have pinpointed the debacle on the failure of LTCM’s risk management prowess. This paper avoids the blame and identifies the multiple factors, both management risk management blunders, as well as inherent flaws in the risk metric used by LTCM – Value at Risk (VaR) – a commonly used risk metric in the financial industry today. -
CHAPTER 16 ESTIMATING EQUITY VALUE PER SHARE in Chapter 15, We Considered How Best to Estimate the Value of the Operating Assets of the Firm
1 CHAPTER 16 ESTIMATING EQUITY VALUE PER SHARE In Chapter 15, we considered how best to estimate the value of the operating assets of the firm. To get from that value to the firm value, you have to consider cash, marketable securities and other non-operating assets held by a firm. In particular, you have to value holdings in other firms and deal with a variety of accounting techniques used to record such holdings. To get from firm value to equity value, you have to determine what should be subtracted out from firm value – i.e, the value of the non-equity claims in the firm. Once you have valued the equity in a firm, it may appear to be a relatively simple exercise to estimate the value per share. All it seems you need to do is divide the value of the equity by the number of shares outstanding. But, in the case of some firms, even this simple exercise can become complicated by the presence of management and employee options. In this chapter, we will measure the magnitude of this option overhang on valuation and then consider ways of incorporating the effect into the value per share. The Value of Non-operating Assets Firms have a number of assets on their books that can be categorized as non- operating assets. The first and obvious one is cash and near-cash investments – investments in riskless or very low-risk investments that most companies with large cash balances make. The second is investments in equities and bonds of other firms, sometimes for investment reasons and sometimes for strategic ones. -
Dynamic Strategic Arbitrage∗
Dynamic Strategic Arbitrage∗ Vincent Fardeauy Frankfurt School of Finance and Managment This version: June 11, 2014 Abstract Asset prices do not adjust instantaneously following uninformative demand or supply shocks. This paper offers a theory based on arbitrageurs' price impact to explain these slow-moving cap- ital dynamics. Arbitrageurs recognizing their price impact break up trades, leading to gradual price adjustments. When shocks are anticipated, prices exhibit a V-shaped pattern, with a peak at the realization of the shocks. The dynamics of market depth and price adjustments depend on the degree of competition between arbitrageurs. When shocks are uncertain, price dynamics depend on the interaction between arbitrageurs' competition and the total risk-bearing capacity of the market. JEL codes: G12, G20, L12; Keywords: Strategic arbitrage, liquidity, price impact, limits of arbitrage. ∗I benefited from comments from Miguel Ant´on,Bruno Biais, Maria-Cecilia Bustamante, Amil Dasgupta, Michael Gordy, Martin Oehmke (discussant), Matt Pritsker, Jean-Charles Rochet, Yuki Sato, Kostas Zachariadis, Jean-Pierre Zigrand and seminar and conference participants at LSE, INSEAD, FRB, the University of Zurich and the 2013 AFA meetings in San Diego. I am grateful to Dimitri Vayanos and Denis Gromb for supervision and insights. Jay Im provided excellent research assistance. This paper is the first part of a paper previously circulated as \Strategic Arbitrage with Entry". All remaining errors are my own. yDepartment of Finance, Frankfurt School of Finance and Management, Sonnemannstrasse 9-11, 60314 Frankfurt am Main, Germany. Email: [email protected]. 1 1 Introduction Empirical evidence shows that prices recover slowly from demand or supply shocks that are unre- lated to future earnings and that these patterns are due to imperfections in the supply of capital. -
Leverage Networks and Market Contagion
Leverage Networks and Market Contagion Jiangze Bian, Zhi Da, Dong Lou, Hao Zhou∗ First Draft: June 2016 This Draft: March 2019 ∗Bian: University of International Business and Economics, e-mail: [email protected]. Da: University of Notre Dame, e-mail: [email protected]. Lou: London School of Economics and CEPR, e-mail: [email protected]. Zhou: PBC School of Finance, Tsinghua University, e-mail: [email protected]. We are grateful to Matthew Baron (discussant), Markus Brunnermeier (discussant), Adrian Buss (discus- sant), Agostino Capponi, Vasco Carvalho, Tuugi Chuluun (discussant), Paul Geertsema (discussant), Denis Gromb (discussant), Harald Hau, Zhiguo He (discussant), Harrison Hong, Jennifer Huang (discussant), Wenxi Jiang (discussant), Bige Kahraman (discussant), Ralph Koijen, Guangchuan Li, Fang Liang (discus- sant), Shu Lin (discussant), Xiaomeng Lu (discussant), Ian Martin, Per Mykland, Stijn Van Nieuwerburgh, Carlos Ramirez, L´aszl´oS´andor(discussant), Andriy Shkilko (discussant), Elvira Sojli, and seminar partic- ipants at Georgetown University, Georgia Institute of Technology, London School of Economics, Michigan State University, Rice University, Southern Methodist University, Stockholm School of Economics, Texas Christian University, Tsinghua University, UIBE, University of Hawaii, University of Houston, University of Miami, University of South Florida, University of Zurich, Vienna Graduate School of Finance, Washington University in St. Louis, 2016 China Financial Research Conference, 2016 Conference on the Econometrics -
Growth, Profitability and Equity Value
Growth, Profitability and Equity Value Meng Li and Doron Nissim* Columbia Business School July 2014 Abstract When conducting valuation analysis, practitioners and researchers typically predict growth and profitability separately, implicitly assuming that these two value drivers are uncorrelated. However, due to economic and accounting effects, profitability shocks increase both growth and subsequent profitability, resulting in a strong positive correlation between growth and subsequent profitability. This correlation increases the expected value of future earnings and thus contributes to equity value. We show that the value effect of the growth-profitability covariance on average explains more than 10% of equity value, and its magnitude varies substantially with firm size (-), volatility (+), profitability (-), and expected growth (+). The covariance value effect is driven by both operating and financing activities, but large effects are due primarily to operating shocks. One implication of our findings is that conducting scenario analysis or using other methods that incorporate the growth-profitability correlation (e.g., Monte Carlo simulations, decision trees) is particularly important when valuing small, high volatility, low profitability, or high growth companies. In contrast, for mature, high profitability companies, covariance effects are typically small and their omission is not likely to significantly bias value estimates. * Corresponding author; 604 Uris Hall, 3022 Broadway, New York, NY 10027; phone: (212) 854-4249; [email protected]. -
Optimal Convergence Trading with Unobservable Pricing Errors
OPTIMAL CONVERGENCE TRADING WITH UNOBSERVABLE PRICING ERRORS SÜHAN ALTAY, KATIA COLANERI, AND ZEHRA EKSI Abstract. We study a dynamic portfolio optimization problem related to convergence trading, which is an investment strategy that exploits temporary mispricing by simultane- ously buying relatively underpriced assets and selling short relatively overpriced ones with the expectation that their prices converge in the future. We build on the model of Liu and Timmermann [22] and extend it by incorporating unobservable Markov-modulated pricing errors into the price dynamics of two co-integrated assets. We characterize the optimal portfolio strategies in full and partial information settings both under the assumption of unrestricted and beta-neutral strategies. By using the innovations approach, we provide the filtering equation that is essential for solving the optimization problem under partial information. Finally, in order to illustrate the model capabilities, we provide an example with a two-state Markov chain. 1. Introduction Convergence-type trading strategies have become one of the most popular trading strate- gies that are used to capitalize on market inefficiencies, or deviations from “equilibrium,” especially with the rapid developments in algorithmic and high-frequency trading. For a typical convergence trade, temporary mispricing is exploited by simultaneously buying rel- atively underpriced assets and selling short relatively overpriced assets in anticipation that at some future date their prices will have become closer. Thus one can profit by the extent of the convergence. A prime example of a convergence trade is a pairs trading strategy that involves a long position and a short position in a pair of similar stocks that have moved together historically and hence an investor can profit from the relative value trade arising arXiv:1910.01438v2 [q-fin.PM] 7 Oct 2019 from the cointegration between asset price dynamics involved in the trade. -
Leverage and the Beta Anomaly
Downloaded from https://doi.org/10.1017/S0022109019000322 JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS COPYRIGHT 2019, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195 doi:10.1017/S0022109019000322 https://www.cambridge.org/core Leverage and the Beta Anomaly Malcolm Baker, Mathias F. Hoeyer, and Jeffrey Wurgler * . IP address: 199.94.10.45 Abstract , on 13 Dec 2019 at 20:01:41 The well-known weak empirical relationship between beta risk and the cost of equity (the beta anomaly) generates a simple tradeoff theory: As firms lever up, the overall cost of cap- ital falls as leverage increases equity beta, but as debt becomes riskier the marginal benefit of increasing equity beta declines. As a simple theoretical framework predicts, we find that leverage is inversely related to asset beta, including upside asset beta, which is hard to ex- plain by the traditional leverage tradeoff with financial distress that emphasizes downside risk. The results are robust to a variety of specification choices and control variables. , subject to the Cambridge Core terms of use, available at I. Introduction Millions of students have been taught corporate finance under the assump- tion of the capital asset pricing model (CAPM) and integrated equity and debt markets. Yet, it is well known that the link between textbook measures of risk and realized returns in the stock market is weak, or even backward. For example, a dollar invested in a low beta portfolio of U.S. stocks in 1968 grows to $70.50 by 2011, while a dollar in a high beta portfolio grows to just $7.61 (see Baker, Bradley, and Taliaferro (2014)). -
Chapter 13 Lecture: Leverage
CHAPTERCHAPTER 13:13: LEVERAGE.LEVERAGE. (The use of debt) 1 The analogy of physical leverage & financial leverage... A Physical Lever... 500 lbs 2 feet 5 feet 200 lbs LIFTS "Leverage Ratio" = 500/200 = 2.5 “Give me a place to stand, and I will move the earth.” - Archimedes (287-212 BC) 2 Financial Leverage... $4,000,000 EQUITY BUYS INVESTMENT $10,000,000 PROPERTY "Leverage Ratio" = $10,000,000 / $4,000,000 = 2.5 Equity = $4,000,000 Debt = $6,000,000 3 Terminology... “Leverage” “Debt Value”, “Loan Value” (L) (or “D”). “Equity Value” (E) “Underlying Asset Value” (V = E+L): "Leverage Ratio“ = LR = V / E = V / (V-L) = 1/(1-L/V) (Not the same as the “Loan/Value Ratio”: L / V,or “LTV” .) “Risk” The RISK that matters to investors is the risk in their total return, related to the standard deviation (or range or spread) in that return. 4 Leverage Ratio & Loan-to-Value Ratio 25 20 1 LR = 1−LTV 15 LR 10 5 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% LTV 5 Leverage Ratio & Loan-to-Value Ratio 100% 90% 80% 70% 1 60% LTV =1− LR 50% LTV 40% 30% 20% 10% 0% 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 LR 6 Effect of Leverage on Risk & Return (Numerical Example)… Example Property & Scenario Characteristics: Current (t=0) values (known for certain): E0[CF1] = $800,000 V0 = $10,000,000 Possible Future Outcomes are risky (next year, t=1): "Pessimistic" scenario (1/2 chance): $11.2M CF1 = $700,000; V1 = $9,200,000.