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Value Investing Strategy (Strategy Overview)

Allocations for December 2023 (Final)

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Allocations for December 2023 (Final)
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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.

Shorting Costs Kill Stock Return Anomalies?

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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SACEMS Hedge Portfolios

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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Surprise in Short Interest as Stock Return Predictor

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

Persistently High Stock Loan Fee as Return Predictor

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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Stock Loan Fee as Return Predictor

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

Shorting Costs and Exploitation of Stock Anomalies

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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Update on Shorting Leveraged ETF Pairs

“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

Long/short Equity Mutual Fund Performance Update

How well have long/short equity mutual funds done in recent years? In their April 2019 paper entitled “Hedge Funds Versus Hedged Mutual Funds: An Examination of Long/Short Funds; A Performance Update”, David McCarthy and Brian Wong present an out-of-sample update of a prior performance assessment of long/short equity mutual funds (see “Multialternative Mutual Fund Performance”). They track the same universe as the prior paper and therefore do not include funds launched after January 2013. They construct an equally weighted index of long/short equity mutual funds, rebalanced monthly. They compare performance of this index to those of the S&P 500 Total Return Index, HFRI Equity Hedge Fund Index (HFRI Index) and the Dow Jones Credit Suisse Long/Short Equity Hedge Fund Index (DJ-CS Index). Using monthly returns of 26 live, 14 dead and 4 changed (up to date of change) long/short equity mutual funds established as of January 2013 along with contemporaneous returns for benchmark indexes during July 2013 through December 2018, they find that:

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Momentum and Bubble Stocks

Do “bubble” stocks (those with high shorting demand and small borrowing supply) exhibit unconventional momentum behaviors? In their December 2018 paper entitled “Overconfidence, Information Diffusion, and Mispricing Persistence”, Kent Daniel, Alexander Klos and Simon Rottke examine how momentum effects for bubble stocks differ from conventional momentum effects. They each month sort stocks into groups independently as follows:

  1. Momentum winners (losers) are the 30% of stocks with the highest (lowest) returns from one year ago to one month ago, incorporating a skip-month.
  2. Stocks with high (low) shorting demand are those with the top (bottom) 30% of short interest ratios.
  3. Stocks with small (large) borrowing supply are those with the top (bottom) 30% of institutional ownerships.

They then use intersections of these groups to reform 27 value-weighted portfolios. Bubble (constrained) stocks are those in the intersection of high shorting demand and low institutional ownership, including both momentum winners and losers. For purity, they further split bubble losers into those that were or were not also bubble winners within the past five years. Using monthly and daily returns, market capitalizations and trading volumes for a broad sample of U.S. common stocks, monthly short interest ratios and quarterly institutional ownership data from SEC Form 13F filings during July 1988 through June 2018, they find that: Keep Reading

Exploiting Informed Long and Short Trades

In the June 2018 draft of their paper entitled “An Information Factor: Can Informed Traders Make Abnormal Profits?”, Matthew Ma, Xiumin Martin, Matthew Ringgenberg and Guofu Zhou construct and test a long-short information factor (INFO) based on observed trading of firm insiders, short sellers and option traders. Specifically, the INFO portfolio:

  • Is each month long the 10% (decile) of stocks with the highest levels of net buying (purchases minus sales) by top managers scaled by the average number of shares held by all top managers over the calendar year.
  • Is each month short stocks based on both short interest (number of shares short divided by shares outstanding) and associated option trading activity (volume of liquid put and call options divided by volume of associated stock). They sort stocks independently on short interest and option trading activity, add the two ranks for each stock and short the decile of stocks with the highest combined ranks.

They further examine whether INFO is a key driver of hedge fund returns. Using monthly data for specified variables, monthly returns for a broad sample of U.S. stocks priced over $5 and monthly returns for 13 hedge fund indexes and 5,565 individual U.S. equity hedge funds during February 1996 (limited by options data) through December 2015, they find that: Keep Reading

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