Short Selling

Are there reliable paths to success in short selling? Is short selling activity a useful indicator for investors/traders? Does it mean “stay away” or “squeeze coming?” These blog entries cover the short side of the market.

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Leveraged ETF Pair Shorting Strategies

“Shorting Leveraged ETF Pairs” looks at shorting leveraged long/short pairs of exchange-traded funds (ETF) and letting the short positions “melt away” over long holding periods. Findings suggest that the approach may be profitable, with most of the gain coming when market volatility is high. What about more active strategies of continually renewed short positions? To investigate, we consider monthly renewal of short positions in the ProShares Ultra S&P500 (SSO) / ProShares UltraShort S&P500 (SDS) 2X/-2X pair and the ProShares UltraPro S&P500 (UPRO)ProShares UltraPro Short S&P500 (SPXU) 3X/-3X pair. Using monthly adjusted closes for these ETFs and for the S&P 500 Volatility Index (VIX) from respective inceptions through June 2015, we find that: Keep Reading

Exploiting Unusual Changes in Hedge Fund Holdings and Short Interest

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

Days-to-cover Short Interest as a Stock Return Predictor

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:

Keep Reading

Aggregate Short Interest and Future Stock Market Returns

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

Exploiting Interaction of Hedge Fund Holdings and Short Interest

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

Realistic Long-short Strategy Performance

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

Aggregate Short Interest as a Stock Market Indicator

Does aggregate short interest serve as an intermediate-term stock market indicator based on either momentum (shorting begets shorting) or reversion (covering follows shorting)? To investigate, we relate the behavior of NYSE aggregate short interest with that of SPDR S&P 500 (SPY). Prior to September 2007, NYSE aggregate short interest is monthly (as of the middle of each month). Since September 2007, measurements are approximately biweekly (as of the middle and end of each months). There is a delay of about two weeks between short interest measurement and release, and new releases sometimes revise prior releases. Using monthly/biweekly short interest data culled from NYSE news releases and contemporaneous dividend-adjusted SPY price for the period January 2002 through February 2014 (69 monthly followed by 154 biweekly observations), we find that: Keep Reading

Avoiding the Momentum Crash Crowd

Is there a way to avoid the stock momentum crashes that occur when the positive feedback loop between past and future returns breaks down? In his November 2013 paper entitled “Crowded Trades, Short Covering, and Momentum Crashes, Philip Yan investigates the power of the interaction between short interest and institutional trading activity to explain stock momentum crashes and thereby offer a way to avoid these crashes. Each month he sorts stocks into ranked tenths (deciles) based on returns from 12 months ago to one month ago (skipping the most recent month to avoid reversals). He reforms each month baseline winner and loser portfolios from the value-weighted deciles of extreme high and low returns, respectively. He then segments the loser portfolio into crowded losers (stocks that are most shorted and have the highest institutional exit rate) and non-crowded losers (stocks that are most shorted but do not have the highest institutional exit rate). The most shorted losers are those within the fifth of stocks with the highest short interest ratios (short interest divided by shares outstanding). The losers with the highest institutional exit rates are those within the fifth of stocks with the most shares completely liquidated by institutional investors divided by shares outstanding. He defines three value-weighted long-short portfolios: (1) the baseline portfolio buys the baseline winners and shorts the baseline losers; (2) the crowded portfolio buys the baseline winners and shorts the crowded losers; and, (3) the “non-crowded portfolio buys the baseline winners and shorts the non-crowded losers”. Using daily and monthly stock return, monthly short interest and quarterly institutional ownership data during January 1980 through September 2012, high-frequency short sales data during 2005 through 2012, and monthly price data for 63 futures contract series as available during January 1980 through June 2013, he finds that: Keep Reading

Shorting Fee as a Stock Return Predictor

Does the cost of borrowing shares of a stock for shorting predict its future returns? In their January 2014 paper entitled “The Shorting Premium and Asset Pricing Anomalies”, Itamar Drechsler and Qingyi (Freda) Drechsler investigate shorting fees as a predictor of stock returns. For analysis, they sort stocks at the end of each month into equally weighted tenths (deciles) based on their shorting fee and then examine average future performance of the deciles, both gross and net of shorting costs. They also analyze how shorting fees affect returns to seven known stock return anomalies: value-growth, momentum, idiosyncratic volatility, composite equity issuance, financial distress (likelihood of bankruptcy), max return, and net stock issuance. Using monthly stock shorting fees aggregated across a large number of participants in the stock loan market (from Markit Security Finance), monthly stock returns and firm characteristics for a broad sample of U.S. stocks during January 2004 through October 2012, they find that: Keep Reading

Lendable Share Supply a Roadblock to Shorting Strategies?

Does the limited supply of lendable shares substantially inhibit successful short selling? In the November 2013 draft of their paper entitled “In Short Supply: Equity Overvaluation and Short Selling”, Messod Beneish, Charles Lee and Craig Nichols examine the profitability of shorting U.S. stocks based on the supply of shares available for lending. They note that the short interest ratio (SIR), the ratio of shares shorted to total shares outstanding, masks the importance of lendable supply. SIR may be low either because few investors have negative views, or because the supply of lendable shares is small. They focus on a proprietary measure of stock lendability from Data Explorer Limited called the Daily Cost of Borrowing Score (DCBS), which ranks stocks from 1 (low cost) to 10 (high cost) based on data collected from a consortium of more than 100 institutional lenders. They define stocks with DCBS of 1 or 2 (3 or greater) as easy/cheap (hard/costly) to borrow. They apply basic findings to assess the realism of short-side returns for the following nine published trading strategies:

  1. Gross profitability
  2. Asset growth
  3. Investment‐to‐assets (underperformance of stocks that overinvest)
  4. Net operating assets (underperformance of stocks with high net operating assets)
  5. Total accruals
  6. Payout percentage
  7. Net quarterly profitability (underperformance of stocks with low quarterly net income divided by assets)
  8. Financial distress (underperformance of stocks with high probability of bankruptcy)
  9. Probability of fraud

Using prices, accounting data and lendable/borrowed shares data for stocks representing about 90% of U.S. equity market capitalization during July 2004 through October 2011 (88 months), they find that: Keep Reading

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