Guidance to a Goldman Sachs alumnus Hedge Fund with $400 Billion-$500 Billion AUM: Alpha Trading Strategies Analysis, Maximizing Alpha for Hedge Funds, and, High Frequency Econometrics for Analyzing Price Impact of Trades, Liquidity, and, Market Microstructure

Analysis of the Effects of Security Breaches on Firm Value

10.51240/jibe.2018.2.1 ◽

2018 ◽

Vol 19 (2) ◽

pp. 1-25

Author(s):

Stoyu Ivanov

Keyword(s):

Market Efficiency ◽

Market Microstructure ◽

Hedge Funds ◽

High Frequency ◽

Firm Value ◽

Trading Strategies ◽

Average Price ◽

Scarce Resources ◽

Corporate Security ◽

Security Breaches

The purpose of this study is to examine, on intradaily market microstructure basis, fifteen recent occurrences of corporate security breaches to extend our understanding of market efficiency. We document minor average price responses to announcements of a security breach in the firms??target of an attack, contrary to many other corporate announcement studies, which document immediate price reaction to an announcement. Surprisingly, we find that the matching firms in our study have a stronger market microstructure response to the announcement of the attack instead. This study suggests to high-frequency investors, such as hedge funds, that they should focus their attention and scarce resources on developing trading strategies on other corporate events and announcements rather than on the announcement of security breaches.

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Hedge Funds for Retail Investors? An Examination of Hedged Mutual Funds

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109009090188 ◽

2009 ◽

Vol 44 (2) ◽

pp. 273-305 ◽

Cited By ~ 72

Author(s):

Vikas Agarwal ◽

Nicole M. Boyson ◽

Narayan Y. Naik

Keyword(s):

Mutual Funds ◽

Hedge Funds ◽

Rapid Growth ◽

Lower Cost ◽

Hedge Fund ◽

Trading Strategies ◽

Superior Performance ◽

Retail Investors

AbstractRecently, there has been rapid growth in the assets managed by “hedged mutual funds”—mutual funds mimicking hedge fund strategies. We examine the performance of these funds relative to hedge funds and traditional mutual funds. Despite using similar trading strategies, hedged mutual funds underperform hedge funds. We attribute this finding to hedge funds’ lighter regulation and better incentives. Conversely, hedged mutual funds outperform traditional mutual funds. Notably, this superior performance is driven by managers with experience implementing hedge fund strategies. Our findings have implications for investors seeking hedge-fund-like payoffs at a lower cost and within the comfort of a regulated environment.

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Hedge fund variables and short-run SEO returns

International Journal of Managerial Finance ◽

10.1108/ijmf-09-2017-0194 ◽

2018 ◽

Vol 14 (3) ◽

pp. 322-341 ◽

Cited By ~ 2

Author(s):

Robert Martin Hull ◽

Sungkyu Kwak ◽

Rosemary Walker

Keyword(s):

Stock Returns ◽

Hedge Funds ◽

Statistical Significance ◽

Hedge Fund ◽

Abnormal Returns ◽

Seasoned Equity Offerings ◽

Trading Strategies ◽

Content Type ◽

New Findings ◽

Short Run

Purpose The purpose of this paper is to determine if hedge fund variables (HFVs) are associated with short-run daily buy and hold abnormal returns (BHARs) for a 30-day window around announcement dates for seasoned equity offerings (SEOs). Design/methodology/approach This paper utilizes the event study metric that computes BHARs. These BHARs are used in a regression model as dependent variables with HFVs and nonhedge fund variables (NFVs) as independent variables. For regression tests, standard errors are clustered at the month level. Findings This paper offers three new findings. First, HFVs are significantly associated with SEO BHARs. Second, HFVs are capable being associated with stronger statistical significance compared to NFVs. Third, not using HFVs can produce an omitted-variable bias. Research limitations/implications This paper does not have information on which individual hedge funds use a strategy during the month of the offering but only the proportion of hedge funds that do. A research implication is the proportion can be associated with SEO BHARs in a fashion predicted based on a long or short position. Practical implications Hedge funds can use trading strategies to capitalize on established patterns of price behavior. Social implications Hedge funds enjoy a trading advantage over smaller investors. Originality/value This paper is the first study to document the association between hedge fund stratagems and stock returns around a major corporate event. It shows researchers should consider institutional trading strategies when studying the market response to a major corporate event.

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Kmart, Sears, and ESL: How a Hedge Fund Became One of the World's Largest Retailers

Kellogg School of Management Cases ◽

10.1108/case.kellogg.2016.000176 ◽

2017 ◽

pp. 1-22 ◽

Cited By ~ 1

Author(s):

David P. Stowell ◽

Paul Stowell

Keyword(s):

Decision Making ◽

Real Estate ◽

Hedge Funds ◽

Hedge Fund ◽

Trading Strategies ◽

Decision Making Process ◽

Investment Bankers ◽

Strategic Acquisition ◽

Important Position

Within 18 months of exiting bankruptcy, Kmart's position was sufficiently strong to launch an acquisition of Sears, once the nation's largest retailer and also a core holding of ESL. Looks at a number of compelling issues related to Kmart's bankruptcy, restructuring, and rebirth under the control of ESL, a large hedge fund. Presents some of the key metrics that Eddie Lampert, head of ESL, had available to him as he made two decisions: first, in 2002, to amass a controlling stake in Kmart's defaulted debt during the restructuring; and second, in 2004, to launch a takeover of Sears. The first deal illustrates the decision-making process for a financial buyer, including the downside protection of Kmart's real estate holdings, whereas the second deal represents a traditional strategic acquisition. Illustrates the innovative use of real estate as a “hedge” for ESL in the event that the retail combination does not produce the required financial results. Also focuses on the role of investment bankers and the increasingly important position that hedge funds and LBO funds have carved out in the M&A market.To outline the explosive growth in assets and influence of alternative investment managers, particularly LBO funds and hedge funds, and the transition of some larger hedge funds from shorter term trading strategies to longer term plays on distressed debt, restructurings, and turnarounds.

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Options Trading Costs Are Lower than You Think

Review of Financial Studies ◽

10.1093/rfs/hhaa010 ◽

2020 ◽

Vol 33 (11) ◽

pp. 4973-5014 ◽

Cited By ~ 3

Author(s):

Dmitriy Muravyev ◽

Neil D Pearson

Keyword(s):

High Frequency ◽

Trading Volume ◽

Fair Value ◽

Price Impact ◽

Trading Strategies ◽

Trading Costs ◽

Options Markets ◽

Price Changes ◽

Options Trading ◽

Effective Spread

Abstract Conventional estimates of the costs of taking liquidity in options markets are large. Nonetheless, options trading volume is high. We resolve this puzzle by showing that options price changes are predictable at high frequency, and many traders time executions by buying (selling) when the option fair value is close to the ask (bid). Effective spreads of traders who time executions are less than 40% of the size of conventional measures, and the overall average effective spread is one-quarter smaller than conventional estimates. Price impact measures are also affected. These findings alter conclusions about the after-cost profitability of options trading strategies.

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High-Frequency Financial Econometrics

10.23943/princeton/9780691161433.001.0001 ◽

2014 ◽

Cited By ~ 20

Author(s):

Yacine Aït-Sahalia ◽

Jean Jacod

Keyword(s):

High Frequency ◽

Practical Importance ◽

Financial Econometrics ◽

Trading Strategies ◽

Financial Data ◽

Jump Processes ◽

High Frequency Trading ◽

Microstructure Noise ◽

Econometric Methods ◽

Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

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