Hedge Fund Trading Research Articles

Hedge funds and the positive idiosyncratic volatility effect

Abstract While it is established that idiosyncratic volatility is negatively priced in the cross-section of stock returns, the relation between idiosyncratic volatility and hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility earn higher future risk-adjusted returns of 6 percent p.a. than hedge funds with low idiosyncratic volatility. The outperformance arises because hedge funds trade high idiosyncratic volatility stocks wisely. They pick high volatility stocks when they are underpriced and short-sell high volatility stocks when they are overpriced. Our results support the notion that hedge funds’ idiosyncratic volatility is a measure of managerial skill.

The Impact of Different Trading Strategies on the Same Portfolio

The frequent occurrence of political events, the spread of the epidemic and changes in policies of various countries have made the market more volatile. Due to the instability of the overall economic environment, investors are increasingly focusing on using strategies to improve returns, so this article selects several hedge fund trading strategies, analyzes the same trading portfolio, and compares the performance of different trading portfolios. Different effects of the same combination give spirits to investors when making transactions. The market neutral trading strategy is effective for the trading combination of F and NVDA. Based on different data, the long-short equity trade strategies are not completely effective, and the credit arbitrage trade strategy is not suitable for this trading combination. Market neutral trading strategies and long-short equity trade strategies basically meet the combination of F and NVDA, but due to the possibility of credit rating fluctuations, credit arbitrage trade strategy does not meet this combination. These different effects reflect some of the underlying factors in the market, so the analysis in this article will improve investors' understanding of investment strategies and change their previous trading thoughts. Taking these effects into consideration can help investors effectively avoid potential risks, make better choices, or obtain more returns.

On the Other Side of Hedge Fund Equity Trades

Hedge funds earn positive ex post abnormal returns and avoid negative abnormal returns on their equity portfolios when trading in the opposite direction of highly diversified low-turnover institutional investors (quasi indexers). This pattern seems to be driven by the preferences of quasi indexers for high-market-beta stocks together with the ability of hedge funds to identify subsets of especially profitable trades. It remains pronounced when accounting for other determinants of hedge fund trades, such as stock liquidity, market anomalies, and major corporate events. Trading against other institutional investors or noninstitutions does not result in abnormal performance for hedge funds. This paper was accepted by David Sraer, finance. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4877 .

ETF Rebalancing, Hedge Fund Trades, and Capital Market

ETF Rebalancing, Hedge Fund Trades, and Capital Market

Sell‐side analyst recommendation revisions and hedge fund trading before and after regulation fair disclosure

AbstractWe examine institutional trading in relation to changes in consensus recommendations over time. We find that pre‐Reg FD's positive contemporaneous relation between hedge fund trading and change in consensus becomes negative after Reg FD, but the positive relation between nonhedge fund trading and change in consensus continues even after Reg FD. Furthermore, during post‐Reg FD, while the performance of hedge funds’ trades that contradict with analysts improve, nonhedge funds’ trades that agree with analysts significantly deteriorate. Our evidence suggests that hedge funds have better information processing skill and are more cognizant about the role of selective information in analyst recommendations.

Sell-side Analyst Recommendation Revisions and Institutional Trading before and after Regulation FD

We examine institutional trading in relation to changes in consensus recommendations over time. We find that pre-Reg FD’s positive contemporaneous relation between hedge fund trading and change in consensus becomes negative after Reg FD, but the positive relation between non-hedge fund trading and change in consensus continues even after Reg FD. Furthermore, during post-Reg FD, while the performance of hedge funds’ trades that contradict with analysts improve, non-hedge funds’ trades that agree with analysts significantly deteriorate. Our evidence suggests that hedge funds have better information processing skill and are more cognizant about the role of selective information in analyst recommendations.

Ethical and Political-Economic Dimensions and Potential Reforms of the Hybrid Leveraged, High Frequency, Artificial Intelligence Trading Model

The average annual profits before fees of the $10 billion plus Renaissance Technologies’ hybrid Medallion “Leveraged, High Frequency, Artificial Intelligence (LHFAI)” trading hedge fund between 1988 and 2019 were about 66 percent. Total trading profits during this period were over $100 billion. The fund has never had a losing year. The fund is not open to the general public. First, distinctions among, in more or less historical order, the traditional market-maker trading model, the hedge fund trading model, the artificial intelligence trading model, and the hybrid LHFAI trading model are discussed. Second, the micro components of the LHFAI trading model are explained in the context of Renaissance Technologies’ Medallion Fund. Third, key positive contributions of the model with respect to profitability, low annual volatility, market liquidity, and intellectual property development; negative ethical issues concerning exclusive access, tax fairness, financial transparency, shared responsibility for losses and systemic risk, and short vs. long-term capital allocation are discussed. Potential reforms that retain the positives, reduce the negatives, and that could positively transform the model are discussed. Fourth, potential impacts that the potential reforms might have on the macro LHFAI form of finance capitalism and the larger finance capitalism political-economic system are considered. Fifth, conclusions are offered and discussed.

How Smart is Instiutional Trading?

We estimate daily aggregate order flow of individual stocks from institutional investors as well as for hedge funds and other institutions separately. We find that the estimated institutional order imbalance positively predicts stock return on the next day and outperforms other institutional order flow estimates. The institutional order flow from hedge funds generates greater and more persistent price impact than the order flow from other institutions. We also find that hedge funds trade on well-known anomalies while the other institutions do not. Our findings suggest that the superior trading skills of institutional investors can be largely attributed to hedge funds.

What Constrains Liquidity Provision? Evidence from Institutional Trades*

Abstract The article studies liquidity provision by institutional investors using trade-level data. We find that hedge fund trades are a more important predictor of stock-level liquidity than mutual fund trades. However, hedge funds’ liquidity provision is more exposed to financial conditions than that of mutual funds. Hedge funds that are more constrained in terms of leverage, age, asset illiquidity, and past performance exhibit a stronger shift toward liquidity consumption when funding condition tighten. Stocks with more exposure to constrained liquidity providing hedge funds suffered more during the financial crisis.

Pairs Trading Strategy with Geolocation Data—The Battle between Under Armour and Nike

In this article, the authors examine fundamental linkages between geolocation (human movement) data and financial market equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. The authors concentrate their study on a popular hedge fund trading strategy known as <i>pairs trading</i>. First, the authors collect data regarding Under Armour (UA) and Nike’s stock market price and volume. Second, they investigate the volume of people visiting each physical store of UA and Nike, as proxied from anonymous cell phone geolocation traffic. Third, they monitor the relative activities for tweets related to UA and Nike. Fourth, after combining all the data, the authors glean the following fascinating results: (1) Geolocation information is proven to be an important factor in a pairs trading strategy between UA and Nike, according to the results of feature selection and prediction accuracy for price ratio change; (2) both ensemble methods of machine learning and rolling analysis could significantly raise the prediction accuracy; and (3) a pairs trading strategy incorporating geolocation information can have a cumulative return of 13.72% from January 2018 to June 2018, with an annualized Sharpe ratio of 3.88. <b>TOPICS:</b>Statistical methods, simulations, big data/machine learning <b>Key Findings</b> • Geolocation data can encapsulate the relative influx and outflux of consumers from department stores. This is an extremely useful feature in training machine learning models that can be used in lucrative pairs-trading strategies. • In the era of big data, it is tempting to throw every feature into a model and assume that the model will always figure out which features are most important. However, the model will usually perform better when trained on a subset of distinct features. • Ensemble methods are an effective means of eliminating bias in machine learning models because they use the output from a series of independently trained submodels to generate one balanced result.

Pairs Trading Strategy with Geolocation Data: The Battle Between Under Armour and Nike

We investigate the fundamental linkages between “geolocation” (human movement) data and financial market’s equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. Our initial data set spans over 54 billion observations and was provided by Fysical. We focus our study on a popular hedge fund trading strategy known as “pairs trading.” First, we collect Under Armour (UA) and Nike’s stock price and volume data. Second, we investigate the relative activity of people visiting a particular physical store of UA and Nike, as proxied from anonymous cell phone foot traffic. Third, we collect the relative sentiment for tweets to UA and Nike. After combining all the data, we glean the following fascinating results: (1) geolocation information is an important variable in pairs trading strategy between UA and Nike, as evidenced by the results from our feature selection popularity methodology; (2) surprisingly, pairs trading only using geolocation data yields positive returns, and employing machine learning methods and rolling analysis enhances the returns; and (3) pairs trading strategy incorporating geolocation information yields a cumulative return of 13.7% from January, 2018 to June, 2018, with an Annualized Sharpe Ratio of 3.9. Some caveats are in order – our results are very sensitive to transaction costs assumptions and our data set has serious limitations.

Does the Timing of Central Bank Announcements Matter? Trade-Level Data on Hedge Fund Behavior Before Swiss National Bank Meetings

Just over three years after enacting a Minimum Exchange Rate policy for the Swiss Franc vs. Euro (EUR/CHF), the Swiss National Bank (SNB) removed it in a surprise announcement on January 15, 2015. The announcement shocked the FX market — EUR/CHF dropped 25.5 percent in the minutes that followed the press release. The JPMorgan Chase Institute leveraged a data asset of 395 million de-identified transactions executed by over 44,000 institutional investors to measure hedge fund net flows in EUR/CHF spot and forward transactions during the SNB’s Minimum Exchange Rate policy period. Our analysis uncovers two key findings related to hedge fund trading in EUR/CHF during the Minimum Exchange Rate policy period. First, we observe that after hedge funds executed the long EUR/CHF strategy, they then sold EUR/CHF in the days immediately preceding the next regularly scheduled SNB announcement, reducing their potential losses if the Minimum Exchange Rate policy was removed at that meeting. Second, hedge fund's confidence in the persistence of the SNB's Minimum Exchange Rate policy appears to have peaked in the four weeks before the policy was removed. Taken together, our findings have implications for central banks as they consider how their choices with respect to communicating policy changes might impact financial market stability. When choosing the most appropriate method to communicate policy changes, policymakers can use our results to help weigh market expectations with respect to both the timing of announcements and the policy outcomes in the context of the central bank's desired market impacts.

Hedge Fund Investing or Mutual Fund Investing: An Application of Multi-Attribute Utility Theory

This paper contrasts high-risk, hedge fund trading, with low-risk, mutual fund trading, in terms of their differing utility functions. We envision hedge funds, led by informed traders who use information to seek out investment opportunities, timing market conditions, with the expectation that prices will move in their favor. Directional hedge funds act to influence prices, while non-directional hedge funds do not act to influence prices. We present utility functions based on steeply-sloping Laplace distributions and hyperbolic cosine distributions, to describe the actions of directional hedge fund traders. Less steeply-sloping lognormal distributions, Coulomb wave functions, quadratic utility functions, and Bessel utility functions are used to describe the investing style of non-directional hedge fund traders. Flatter Legendre utility functions and inverse sine utility functions describe the modest profit-making aspirations of mutual fund traders. The paper’s chief contribution is to develop optimal prices quantitatively, by intersecting utility functions with price distributions. Price distributions for directional hedge fund returns are portrayed as sharp increases and decreases, in the form of jumps, in a discrete arrival Poisson-distributed process. Separate equations are developed for directional hedge fund strategies, including event-driven arbitrage, and global macro strategies. Non-directional strategies include commodity trading, risk-neutral arbitrage, and convertible arbitrage, with primarily lognormal pricing distributions, and some Poisson jumps. Mutual funds are perceived to be Markowitz portfolios, lying on the Capital Market Line, or the International Capital Market Line, tangent to the Efficient Frontier of minimum variance-maximum return portfolios.

Talking Your Book: Evidence from Stock Pitches at Investment Conferences

Using a novel dataset on investment conferences from 2008 to 2013, I show that hedge funds take advantage of the publicity of these conferences and strategically release their book information to drive market demand. Specifically, Hedge funds sell pitched stocks after the conferences to take profit and create room for better investment opportunities. However, pitched stocks still perform better than non-pitched stocks in the funds’ portfolios afterwards. Hedge funds do not pitch obviously bad stocks because maintaining a good reputation helps them raise more money. Pitched stocks earn a cumulative abnormal return of 20% over 18 months before the pitch and continue to outperform the benchmark by 7% over 9 months afterwards. Half the post-conference abnormal return reverts after another 9 months. Moreover, mutual funds exhibit opposite trading behaviors—selling before the pitches and buying afterwards—and possibly contribute to the post-pitch outperformance. Other hedge funds trade pitched stocks similarly to the funds that pitched, suggesting that they either run correlated strategies or share information with each other.

Safaricom 2018: The Emerging-Markets Payments Battle

The case discusses the decision of a hypothetical London-based hedge fund manager, Greg Rubin, who manages a fund primarily investing in emerging and frontier markets to decide whether to buy (or short) Safaricom, a Kenya-based telecom and financial services company that became globally known more than a decade ago (2007) for its use of technology to facilitate payments via mobile phones. There was a real need in Kenya for a different system of payments and money transfers, given the large underbanked population (almost 80% lacking a formal bank account) and the widespread use of cash. M-Pesa, the system introduced by Safaricom was an astounding success and quickly achieved a dominant position in the Kenyan marketplace. Kenyans embraced the use of mobile phones to transfer money and pay bills. More than 10 years later, at the time of the case (March 2018), Safaricom's dominance is challenged by a series of missteps expanding abroad (e.g., to South Africa), increased competition at home, as well as the introduction of 3G and advanced smartphone technology. The case allows for an examination of the investment thesis for Safaricom and its valuation. This requires the analysis of payment systems and their evolution in frontier markets as well as the analysis of country/political risk, among others. It is the combination of an innovative company from a frontier market and the introduction of new technologies that make this case interesting (and challenging) to analyze—by no means an obvious decision for Rubin. Excerpt UVA-F-1843 Rev. Aug. 22, 2018 Safaricom 2018: The Emerging-Markets Payments Battle SAFCOM is one of the best companies this author has ever covered over a fairly long career as a financial analyst, yet it is also one of the most challenging to value, owing to the dual nature of its business—on the one hand, an increasingly commoditized telecom business, where SAFCOM continues to deliver growth above the industry average; and, on the other, a fast-growing financial services business with a positive growth outlook. —Renaissance Capital, October 2017 In March 2018, Greg Rubin sat on his hedge fund's trading floor in Mayfair, London, working on an investment memo he planned to present to fellow members of his investment committee later that day. The investment option on the table for Rubin's absolute return emerging market fund was Safaricom, a telecommunications and financial services company based in Kenya. Ever since the company had achieved extraordinary success with the launch of its mobile payments solution, M-Pesa, a decade earlier, Rubin had considered the company a viable investment option. Even so, he still had more information to digest on the prospects for economic growth within this region of Africa and potential threats to the business before making a final recommendation to his colleagues. . . .

Editor’s Letter

1. Hossein Kazemi 1. Editor-in-Chief This issue of The Journal of Alternative Investments covers two broad topics: illiquid real assets, and hedge fund trading strategies and risk management. Illiquid real assets have received increased attention from institutional investors in recent years

Short selling meets hedge fund 13F: An anatomy of informed demand

The existing literature treats the short side (i.e., short selling) and the long side of hedge fund trading (i.e., fund holdings) independently. The two sides, however, complement each other: opposite changes in the two are likely to be driven by information, whereas simultaneous increases (decreases) of the two may be motivated by hedging (unwinding) considerations. We use this intuition to identify informed demand and document that it exhibits highly significant predictive power over returns (approximately 10% per year). We also find that informed demand forecasts future firm fundamentals, suggesting that hedge funds play an important role in information discovery.

Market Efficiency and Hedge Fund Trading Strategies

Stock and option markets can at times reflect differing information. We identify three reasons for the presence of these periods of ’disagreement’ between the cash and derivatives markets: 1) high volatility and noise trading; 2) high level of risk aversion; 3) speculation versus hedging trades. This paper investigates the role that hedge funds, a proxy for sophisticated investors, play in the price discovery process between stock and option markets and the disagreement/agreement periods. We observe that a disparity in information between the two markets is often associated with deleveraging in directional exposures and reversal strategies. Posterior to the event, active tactical asset allocation in small and value factor investing takes place. We investigate four specific macro events which resulted in significant rebalancing by hedge fund manegers: the Thai Baht depreciation, the Dot-com bubble, the credit crunch and the Nasdaq correction.

Acquiring and Trading on Complex Information: How Hedge Funds Use the Freedom of Information Act

Using the Freedom of Information Act, hedge funds receive records from the Food and Drug Administration about new product approvals, factory inspections, and complaints. We use the funds' receipt of this information to empirically test implications of theories about investors with bounded rationality acquiring complex information for trading purposes. Consistent with theory, we find evidence that the magnitude of hedge fund trades is positively related to the funds' prior knowledge about the target firm and the FOIA process, and to the short-term abnormal stock returns derived from trading.

Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand

The existing literature treats the short side (i.e., short selling) and long side of hedge fund trading (i.e., changes in holdings) independently. The two sides, however, complement each other in revealing important economic motivations of trading: opposite changes in short interest and hedge fund holdings are likely to be driven by information, whereas simultaneous increases (decreases) in short interest and hedge fund holdings may be motivated by hedging (unwinding) considerations. We use this intuition to identify informed demand, and document that it exhibits highly significant predictive power on returns: stocks with informed long demand can outperform stocks with informed short demand by approximately 10% per year. We also find that informed demand forecasts future firm fundamentals (e.g., ROA, earnings surprise, analyst revision) but that it is less related to mutual fund flows or liquidity provision. These findings suggest that information discovery about firm fundamentals could be among the most important drivers for informed demand.