Short Selling

How can the low-volatility effect, whereby stocks with low past volatility tend to outperform the market on a risk-adjusted basis (but lag during long bull markets), help achieve common investment goals? In their October 2024 paper entitled “Leveraging the Low-Volatility Effect”, Lodewijk van der Linden, Amar Soebhag and Pim van Vliet test ways to use the low-volatility effect to support five distinct investment goals. Their low-volatility benchmark strategy each month holds the 100 of the 1,000 largest U.S. stocks with the lowest 36-month volatilities. They consider ways to exploit the effect in five ways:

1. To safely boost return, they integrate value (net payout yield) and momentum (return from 12 months ago to one month ago) with low-volatility by each month: (1) selecting the 500 of the 1,000 largest U.S. stocks with the lowest 36-month volatilities; and, (2) picking the 100 of these stocks with the highest combined net payout yield and momentum. 
2. To beat a conventional 60-40 stocks-bonds portfolio, they consider: (1) replacing 10% of stocks and 5% of bonds with a 15% allocation to Strategy 1; (2) assigning equal weights to stocks, bonds and Strategy 1; or, (3) allocating 70% to Strategy 1 and 30% to bonds.
3. To beat the stock market, they target a market beta of 1.00 via a 140% long position in Strategy 1, financed either by: (1) borrowing 40%, with credit spread plus the T-bill rate as the borrowing cost; or, (2) using equity market index futures, with annual return slippage and implicit costs 0.2%.
4. For absolute returns, they consider a 100% position in Strategy 1, offset by: (1) 48% short positions in speculative stocks (high volatility, low net payout yield and low momentum), assuming 2% annual shorting costs; or, (2) a 72% position in short equity market index futures, with 0.2% annual costs.
5. For crash protection compared to 5% out-of-the-money 1-month put options, they target a market beta of -0.50 by combining: (1) a 30% long position in the low-volatility benchmark with a 50% short position in speculative stocks, with credit spread over the T-bill rate as the borrowing cost; or, (2) a 70% long position in the low-volatility benchmark with a 100% short position in equity market index futures, with 0.2% annual costs.

In general, portfolio rebalancing is monthly. Using monthly data for the largest 1,000 U.S. stocks and for the other asset types specified above during 1990 through 2023, they find that: Keep Reading

Is shorting leveraged exchange-traded funds (LETF) reliably attractive? In their March 2024 paper entitled “Investigating Long-Term Short Pairing Strategies for Leveraged Exchange-Traded Funds Using Machine Learning Techniques”, Hamed Khadivar, Elaheh Nikbakht and Thomas Walker test the profitability of continually shorting seven portfolios of matched pairs of bull and bear LETFs:

1. 100% bull.
2. 75% bull and 25% bear.
3. 67% bull and 33% bear.
4. 50% bull and 50% bear.
5. 33% bull and 67% bear.
6. 25% bull and 75% bear.
7. 100% bear.

They test both quarterly and annual rebalancing and explore some market conditions that affect shorted-pair performance. Their sample consists of 44 bull/bear pairs of U.S. LETFs that each have the same offeror, same underlying index, same leverage and sufficient data during 2012 through 2020 (including daily prices with no gaps in volume longer than 50 days). The underlying index for each pair (its benchmark) must have been available before 2012. They look at different levels of rebalancing frictions and shorting costs to determine profitability breakeven points. Using daily returns and other data for the 44 pairs of LETFs and their underlying indexes during 2012 through 2020, they find that: Keep Reading

Do short-selling hedge funds consistently extract alpha from exuberant retail traders? In their March 2024 paper entitled “Short-Selling Hedge Funds”, Jialin Qian, Zhen Shi and Baozhong Yang examine the performance of hedge funds engaged in short-selling, as follows:

1. Which hedge funds are likely short-sellers, and how do they compare with other hedge funds?
2. What factors contribute to the performance of short-selling hedge funds?
3. How has the 2021 Meme stock phenomenon affected short-selling hedge funds?

They each month identify short-selling hedge funds as those with positive return betas over the past 24 months versus a monthly rebalanced portfolio of short stock positions with weights proportional to their respective short interests. They relate behaviors of short-selling funds to those of other hedge funds and to those of retail traders. Using monthly data for 11,054 U.S. hedge funds, returns and short interests for a broad sample of U.S. stocks and data to measure retail stock trading/sentiment during 2010 through 2022, they find that: Keep Reading

Do stock borrowing fees (shorting costs) inherent in long-short strategies constructed to exploit stock return anomalies kill those anomalies? In their September 2022 paper entitled “Anomalies and Their Short-Sale Costs”, Dmitriy Muravyev, Neil Pearson and Joshua Pollet investigate effects of shorting costs on gross profits generated by published stock return anomalies. Since shorting costs are not available until July 2006, and discovery samples for 83% of selected anomalies end before 2006, their analyses are largely out-of-sample. For each anomaly, they sort stocks into tenths, or deciles, such that expected average return of the bottom (top) decile is lowest (highest). They compute monthly equal-weighted average abnormal returns of decile portfolios relative to characteristics-matched equal-weighted benchmark portfolios. They then analyze impacts of shorting costs on anomaly profitability in two ways:

1. Including all stocks, they adjust the monthly return for each stock to account for the monthly borrowing fee for that stock. 
2. They re-calculate anomaly returns after excluding stock-months with annualized borrowing fees exceeding 1%.

Using rules for 162 published stock return anomalies and associated daily stock returns and shorting costs during July 2006 through December 2020, they find that:

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A subscriber asked about performance of Simple Asset Class ETF Momentum Strategy (SACEMS) hedge portfolios, which each month buy the asset class exchange-traded funds (ETF) in the SACEMS universe with the highest past returns and sell (short) those with the lowest. To investigate, we look at three hedge portfolios:

• Top 1 – Bottom 1: long the ETF with the highest past return and short the ETF with the lowest.
• EW Top 2 – EW Bottom 2: long the equal-weighted (EW) two ETFs with the highest past returns and short the two with the lowest.
• EW Top 3 – EW Bottom 3: long the equal-weighted three ETFs with the highest past returns and short the three with the lowest. 

For each portfolio, monthly rebalancing sets the long and short sides to equal dollar amounts. We consider monthly gross portfolio  performance statistics (ignoring any rebalancing and shorting frictions), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio. To calculate annual excess returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. SACEMS Top 1, EW Top 2 and EW Top 3 SACEMS long-only portfolios serve as benchmarks. Using monthly gross returns for SACEMS ETFs (and cash) by rank during July 2006 through October 2021, we find that:

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Do surprising fluctuations in short interest ratios of stocks indicate new information from short sellers that predicts returns of these stocks? In their November 2020 paper entitled “Surprise in Short Interest”, Matthias Hanauer, Pavel Lesnevski and Esad Smajlbegovic examine standardized unexpected short interest ratio as a stock return predictor. They define this variable as current short interest ratio minus 12-month simple moving average of monthly short interest ratios divided by standard deviation of short interest ratios over the past 12 months, calculated monthly for each stock. Using stock short interest data, associated stock returns and firm accounting data for U.S. publicly listed common stocks, excluding those priced less than $5 or in the bottom 5% of NYSE market capitalizations, as available during March 1980 through December 2013, they find that: Keep Reading

Do stocks with high borrowing costs (loan fees) exhibit predictably low short-term returns? In their November 2020 paper entitled “Borrowing Fees and Expected Stock Returns”, Kaitlin Hendrix and Gavin Crabb explore whether stock loan fees contain reliable and useful information about short-term stock returns worldwide. To isolate borrowing activity most likely related to short selling, they require: (1) no naked short selling allowed in the market; (2) covered short selling allowed throughout the sample period; and, (3) low likelihood of lending securities around dividends for tax reasons. They focus on stocks that are expensive to borrow, small-capitalization stocks with loan fee thresholds determined country by country. They each day form market capitalization-weighted portfolios of stocks not on loan, stocks with low loan fees and stocks with high loan fees based on lending activity the prior trading day. They also consider lending activity the prior three or five days, with not-on-loan and high-fee stocks meeting requirements each of the three or five days. Using proprietary mutual fund stock loan data from Dimensional Fund Advisors across 14 developed and emerging markets during 2011 through 2017 and associated daily stock returns through 2018, they find that:

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Do stocks with high borrowing costs reliably underperform? In their October 2020 paper entitled “The Loan Fee Anomaly: A Short Seller’s Best Ideas”, Joseph Engelberg, Richard Evans, Gregory Leonard, Adam Reed and Matthew Ringgenberg examine equity loan fees (stock borrowing costs) as a predictor of stock returns. For perspective, they compare returns of their loan fee anomaly portfolio (short stocks with highest fees and long stocks with lowest fees) to those of 102 other anomalies individually and in aggregate (based on difference for each stock between number of long signals and number of short signals). They consider four long-short anomaly portfolios based on extreme 1%, 2%, 5% and 10% (deciles) of stocks ranked by the metric for each anomaly. They exclude stocks with share price below $5 and those below the 5th percentile of NYSE market capitalization. Using modeled loan fees, monthly total returns for associated stocks and monthly total returns for 102 other anomalies during 2006 through 2019, they find that: Keep Reading

Do anomaly portfolios that are long (short) the tenth, or decile, of stocks with the highest (lowest) expected value-weighted returns based on some firm accounting variable or stock behavior really work on a net basis? In the May 2019 version of their paper entitled “Shorting Costs and Profitability of Long-Short Strategies”, Dongcheol Kim and Byeung Joo Lee examine profitability of such portfolios after adjusting for: (1) unavailability of stocks to borrow for shorting as indicated; and, (2) stock loan fees paid to share lenders. They consider 14 value-weighted anomalies based on: return on assets, return on equity (ROE), momentum, net operating assets, investment-to-asset ratio, abnormal capital investment, accruals, asset growth, net stock issuance, composite equity issues, O-score, failure probability, gross profit and post-earnings announcement drift. They do not consider trading frictions (broker fees, bid-ask spread, impact of trading) incurred due to periodic reformation of anomaly portfolios. Using monthly stock prices and returns, data to construct value-weighted long-short anomaly portfolios, and share loan availability and fee data from Markit for a broad sample of U.S. stocks priced at least $1 during January 2006 through December 2017, they find that:

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“Monthly Rebalanced Shorting of Leveraged ETF Pairs” finds that shorting some pairs of leveraged ETFs may be attractive. How has the strategy worked recently and how sensitive are findings to execution costs? To investigate, we consider three pairs of monthly reset equal short positions in:

1. ProShares Ultra S&P500 (SSO) and ProShares UltraShort S&P500 (SDS)
2. ProShares UltraPro S&P500 (UPRO) and ProShares UltraPro Short S&P500 (SPXU)
3. ProShares UltraPro QQQ (TQQQ) and ProShares UltraPro Short QQQ (SQQQ)

We take initially, and at the end of each month renew, a -$100,000 short position in each pair member. This strategy generates an initial $200,000 cash in the portfolio and subsequently adds to or subtracts from this cash monthly based on short position performance. We initially assume return on cash covers any costs (transaction fees, bid/ask spread and interest on borrowed positions), but then test sensitivity to net carrying cost. Using monthly adjusted closes for these ETFs from respective inceptions through January 2020, we find that: Keep Reading