Short Selling

Is optimal stock anomaly exploitation long-only or long-short? If not long-short, does shorting the market rather than individual stocks work as well as shorting individual stocks? In his November 2017 paper entitled “How Do Short Selling Costs and Restrictions Affect the Profitability of Stock Anomalies?”, Filip Bekjarovski explores effects of short selling costs and constraints on the viability of exploiting seven U.S. stock anomalies: size, value, profitability, investment, momentum, accruals and net issuance. He constructs all anomaly portfolios via market capitalization weighting of stocks sorted into tenths (deciles). He measures portfolio alphas relative to the market excess return (1-factor). He considers long-only (long the top decile), conventional long-short (long the top and short the bottom deciles) and hybrid long-short (long the two highest alpha deciles, tilted toward the highest, while short the market). Anomaly portfolio rebalancing is annual for all except momentum (monthly). He analyzes effects of shorting costs based on an April 2017 proprietary snapshot of institutional stock borrowing fees. He specifically estimates a shorting cost threshold above which investors should switch between long-short and hybrid long-short exploitation methods. Using the stock borrowing fee snapshot and data required to construct seven anomalies from a broad sample of U.S. common stocks during July 1963 through December 2016, he finds that: Keep Reading

Do legal, policy and practical constraints on short selling substantially detract from factor investing performance? In their February 2017 paper entitled “Factor Investing: The Rocky Road from Long Only to Long Short”, Marie Briere and Ariane Szafarz examine how severely constraints on short selling affect the attractiveness of factor investing. They consider 11 assets consisting of the value-weighted market and the separate long and short sides of five widely studied factors (size, value, profitability, investment and momentum). Short sides consist of the stocks typically shorted when quantifying factor returns, but they are not short portfolios. They use these 11 assets to construct five gross-of-costs mean-variance efficient sets of portfolios, all with net long exposure 100%:

1. FF [Fama-French] Benchmark: 100% long the market, plus exposures to the 10 factor long and short sides, with respective long and short sides exactly offsetting (one positive and one negative).
2. Global long-only: positive exposures only to all 11 assets.
3. Long-short market + long-only factors: positive or negative market exposure, but positive exposures only to the 10 factor long and short sides.
4. 130/30: total negative exposure to all assets capped at 30% of portfolio value.
5. Global long-short: positive or negative exposures to all 11 assets.

Their principal analysis compares the efficient frontiers of these sets of portfolios. Using monthly market and factor long and short side returns as specified during July 1963 through December 2015, they find that: Keep Reading

“When to Sell Equity Index Put Options” summarizes research finding that the “insurance” premium from systematically selling equity index out-of-the-money (OTM) put options concentrates during the last few days before expiration. An ancillary finding is that a similar, though weaker and more volatile, pattern holds for selling at-the-month (ATM) put options. To test the general finding, we therefore look at the monthly return pattern for the CBOE S&P 500 PutWrite Index (PUT). PUT sells a sequence of one-month, fully collateralized (cash covered) ATM S&P 500 Index put options and holds these options to expiration (cash settlement). Per the referenced research, PUT gains should noticeably concentrate in the week before monthly option expiration. Using daily levels of PUT during mid-July 1986 through mid-February 2017, we find that: Keep Reading

Can speculators squeeze the “insurance” premium from shorting equity index put options in just the few days before expiration? In their January 2017 paper entitled “The Timing of Option Returns”, Adriano Tosi and Alexandre Ziegler investigate the timing of returns from shorting out-of-the-money (OTM) S&P 500 Index put options. Specifically, they compute daily excess returns (accruing return on cash for open short positions) for the two front contracts (“front-month” and “back-month”) up through expiration. They translate findings into strategies that open equally weighted short positions in the most liquid OTM puts a certain number of days before expiration and hold to the cash-settled expiration. They also consider delta-hedged positions via long S&P 500 Index futures. In most calculations, they account for market frictions by opening (closing) short positions at the bid (ask). Using daily data for S&P 500 Index levels, options and futures, and contemporaneous stock and option pricing model factors, as available during January 1996 through August 2015, they find that: Keep Reading

Do short sellers who publicly attack their targets affect stock prices? How do they choose their targets? In his October 2016 paper entitled “Activist Short-Selling”, Wuyang Zhao studies short sellers who publish adverse research on and/or publicly disparage the stocks they short. To assess unique effects of the negative publicity on targeted stock prices, he compares performances of targeted stocks on negative publicity days with those of the same stocks, and of industry peers with the closest or highest contemporaneous levels of short interest or increases in short interest, on short interest release days (five separate benchmarks). To identify characteristics of firms that attract activist short sellers, he examines 12 indicators of stock overvaluation and nine measures of uncertainty about firm prospects. Based on initial tests, he constructs aggregate metrics for overvaluation (averaging seven of the overvaluation indicators) and uncertainty (averaging six of the uncertainty measures) for subsequent tests. Using stock prices and firm characteristics related to 6,197 cases of activist short selling reported in Seeking Alpha or Activist Shorts Research during mid-February 2006 through December 2015, he finds that: Keep Reading

Can investors exploit the combination of unusual changes in hedge fund long positions and unusual changes in short interest for individual stocks? In the February 2015 version of their paper entitled “Arbitrage Trading: The Long and the Short of It”, Yong Chen, Zhi Da and Dayong Huang examine the power of three variables to predict stock returns:

1. Abnormal hedge fund holdings (AHF), the current quarter aggregate hedge fund long positions in a stock divided by the total shares outstanding minus the average of this ratio over the four prior quarters.
2. Abnormal short interest (ASR), the current quarter short interest in a stock divided by the total number of shares outstanding minus the average of this ratio over the four prior quarters.
3. The difference between AHF and ASR as a measure of imbalance in hedge fund trading.

They also examine how AHFSR interacts with ten widely used stock return predictors: book-to-market ratio; gross profitability; operating profit; momentum; market capitalization; asset growth; investment growth; net stock issuance; accruals; and, net operating assets. To measure the effectiveness of each predictor, they each quarter rank stocks into fifths (quintiles) based on the predictor and then calculate the difference in average gross excess (relative to the risk-free rate) returns of extreme quintiles. Using quarterly hedge fund SEC Form 13F filings and short interest data for a broad sample of U.S. stocks (excluding small and low-priced stocks), along with data required to compute stock return predictors and risk factors for these stocks, during 1990 through 2012, they find that: Keep Reading

Does accounting for the difficulty of covering short positions enhance the power of short interest to predict stock returns? In the February 2015 draft of their paper entitled “Days to Cover and Stock Returns”, Harrison Hong, Weikai Li, Sophie Ni and Jose Scheinkman examine days-to-cover short interest (DTC) of individual stocks as a return predictor. Their basic metric for DTC is monthly short interest divided by same-month average daily share turnover. They hypothesize that:

1. Short-sellers prefer positions they can close quickly without dominating trading volume.
2. A large DTC indicates that doing so would be difficult.
3. When DTC is high, short sellers must therefore believe strongly that the stock is overpriced.

The main approach of the study is to measure the performance of a hedge portfolio that is each month long (short) the equally weighted or value-weighted tenth or decile of stocks with the lowest (highest) DTC or short interest ratio (SR). Using monthly returns, short interest, shares outstanding, turnover, stock loan fees, stock/firm characteristics and institutional ownership and daily trading volumes for NYSE/AMEX/NASDAQ stocks as available during January 1988 through December 2012, they find that:

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Are short sellers on average well-informed, such that aggregate equity short interest usefully predicts stock market returns? In the January 2015 draft of their paper entitled “Short Interest and Aggregate Stock Returns”, David Rapach, Matthew Ringgenberg and Guofu Zhou investigate the relationship between aggregate equity short interest and future stock market performance. They aggregate short interest as the equally weighted average of short interests as percentage of shares outstanding across individual stocks. They next detrend the aggregate short interest series to remove an upward linear trend. They then standardize the series to have a standard deviation of one and designate the result as the Short Interest Index (SII). Finally, they relate SII to future S&P 500 Index excess (relative to the one-month U.S. Treasury bill yield) returns at horizons of one, three, six and 12 months. They also compare SII to 14 other widely used stock market return predictors. Using monthly (mid-month) short interest data for U.S. stocks (excluding very small firms and low-priced stocks, but including REITs and ETFs), data for 14 other widely used U.S. stock market return predictors and S&P 500 Index excess returns during January 1973 through December 2012, they find that: Keep Reading

Do changes in hedge fund holdings and short interest in a stock together predict its returns? In their January 2015 paper entitled “Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand”, Yawen Jiao, Massimo Massa and Hong Zhang test whether joint changes in aggregate hedge fund holdings and short interest of a stock relate to its future returns. They define a contemporaneous increase (decrease) in aggregate hedge fund holdings and decrease (increase) in short interest as indicative of informed long (short) demand for a stock. They relate informed demand to abnormal return, the return of the stock relative to that of its style benchmark based on size, book-to-market and prior-period return. Using size/value characteristics, monthly returns, quarterly short interest and holdings from quarterly SEC Form 13F filings of 1,397 hedge funds for 5,357 U.S. stocks during 2000 through 2012, they find that: Keep Reading

How well do long-short stock strategies work, after accounting for all costs? In their February 2014 paper entitled “Assessing the Cost of Accounting-Based Long-Short Trades: Should You Invest a Billion Dollars in an Academic Strategy?”, William Beaver, Maureen McNichols and Richard Price examine the net attractiveness of several long-short strategies as stand-alone investments (as for a hedge fund) and as diversifiers of the market portfolio. They also consider long-only versions of these strategies. Specifically, they consider five anomalies exposed by the extreme tenths (deciles) of stocks sorted by:

1. Book-to-Market ratio (BM) measured annually.
2. Operating Cash Flow (CF) measured annually as a percentage of average assets.
3. Accruals (AC) measured annually as earnings minus cash flow as a percentage of average assets.
4. Unexpected Earnings (UE) measured as year-over-year percentage change in quarterly earnings.
5. Change in Net Operating Assets (ΔNOA) measured annually as a percentage of average assets.

For strategies other than UE, they reform strategy portfolios (long the “good” decile and short the “bad” decile) annually at the end of April using accounting data from the prior fiscal year. For UE, they reform the portfolio at the ends of March, June, September and December using prior-quarter data. They highlight cost of capital, financing costs and rebates received on short positions, downside risk and short-side contribution to performance. They assume that the same amount of capital supports either a long-only portfolio, or a portfolio with equal long and short sides (with the long side satisfying Federal Reserve Regulation T collateral requirements for the short side). They account for shorting costs as fees for initiating short positions plus an ongoing collateral rate set at least as high as the federal funds rate, offset by a rebate of 0.25% per year interest on short sale proceeds. They estimate stock trading costs as the stock-by-stock percentage bid-ask spread. They consider two samples (including delistings): (1) all U.S. listed stocks; and, (2) the 20% of stocks with the largest market capitalizations. Using accounting data as described above for all non-ADR firms listed on NYSE, AMEX and NASDAQ for fiscal years 1992 through 2011, and associated monthly stock returns during May 1993 through April 2013, they find that: Keep Reading