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Robustness Tests for Ten Popular Stock Return Anomalies

In their March 2011 paper entitled “The Shrinking Space for Anomalies”, George Jiang and Andrew Zhang investigate the robustness of ten well-known anomalies by iteratively “shrinking the stock space” in two ways to determine whether and how the anomalies really work. The ten anomaly variables are: size, book-to-market ratio, momentum, two liquidity measures, idiosyncratic volatility, accrual, capital expenditure, sales growth and net share issuance. The first way of “shrinking the stock space” involves: (1) ranking the universe of stocks by each of the ten anomaly variables into deciles; (2) iteratively trimming deciles from side of a variable distribution that a hedge portfolio would sell and the side that a hedge portfolio would buy; and, (3) retesting the strength of the anomaly associated with the variable after each iterative trimming. The second way of “shrinking the stock space” involves: (1) trimming from the sample stocks with the smallest market capitalizations and the most extreme book-to-market ratios until size, book-to-market and momentum no longer have significant four-factor alphas for value-weighting and equal equal-weighting (thereby “perfecting” the sample for the four-factor model); and, (2) retesting the strength of the anomalies associated with the other seven variables using the perfected sample. This approach obviates weaknesses in alpha measurement via the commonly applied but imperfect three-factor (market, size, book-to-market) and four-factor (plus momentum) risk models. Using firm characteristics and trading data for all non-financial NYSE, AMEX, and NASDAQ common stocks over the period July 1962 through December 2007, they find that: Keep Reading

Interaction of Sentiment and Liquidity with Stock Return Anomalies

Are stock return anomalies strongest when investor sentiment is highest or liquidity lowest? In the January 2015 draft version of his paper entitled “What Explains the Dynamics of 100 Anomalies?”, Heiko Jacobs  addresses these questions. He first identifies, categorizes and replicates 100 well-known or recently discovered long-short stock return anomalies related to: violations of the law of one price, momentum, technical analysis, short-term and long-term reversal, calendar effects, lead-lag effects among economically linked firms, pairs trading, beta, financial distress, skewness, differences of opinion, industry effects, fundamental analysis, net stock issuance, capital investment and firm growth, innovation, accruals, dividend payments and earnings surprises. He measures the gross magnitude and direction of these anomalies via long-short extreme decile (stocks in top and bottom tenths as ranked by a specific variable) portfolios. He then examines how gross three-factor (market, size, book-to-market) alphas for these anomalies vary with:

Using monthly data as available for a broad sample of U.S. stocks, excluding those that are relatively small and illiquid, as available during August 1965 through December 2011 (many tests start much later and end January 2011), he finds that: Keep Reading

Testing U.S. Equity Anomalies Worldwide

Do widely acknowledged U.S. equity market anomalies exist in other stock markets? If so, why? In his November 2011 paper entitled “Equity Anomalies Around the World”, Steve Fan investigates whether a number of equity market anomalies found among U.S. stocks (asset growth, book-to-market ratio, investment-to-assets ratio, six-month momentum with skip-month, net stock issuance, size and total accruals) also occur in other equity markets and the degree to which such anomalies relate to stock-unique (idiosyncratic) risk. He measures raw anomaly strength based on gross returns from hedge (“zero-cost”) portfolios that are long and short equally weighted extreme quintiles of stocks ranked annually for each accounting variable and every six months for momentum (with overlapping momentum portfolios). To estimate alphas, he adjusts raw returns for the three Fama-French risk factors (market, book-to-market, size) or three alternative investment-based risk factors (market, investment, return on assets). Using monthly common stock return data and associated firm characteristics/accounting data for 43 country stock markets during 1989 through 2009, he finds that: Keep Reading

Bottom-up Anomalies vs. Top-down Portfolio Efficiency

How do widely recognized stock return anomalies (return variations unexplained by asset pricing models) mesh with efficient portfolio selection theory? In their paper entitled “Investing in Stock Market Anomalies”, Turan Bali, Stephen Brown and Ozgur Demirtas examine five prominent stock market anomalies whose existence is robust through time and across markets (size, book-to-market, short-term reversal, intermediate-term momentum and long-term reversion) in contexts of efficient portfolio selection via mean-variance and stochastic dominance methods. In other words, they test whether portfolios that apply these anomalies exhibit exceptionally good combinations of return and volatility, or obviously outperform on a purely statistical basis. Both these portfolio selection methods have shortcomings related to their inclusion of extreme, impractical choices. The authors consider relaxed (“Almost”) versions of these methods that prohibit such choices as “pathological.” The authors form value-weighted size and book-to-market portfolios annually and value-weighted reversal, momentum and reversion portfolios monthly. Using monthly data for July 1926 through December 2008 (990 months) for a broad sample of U.S. stocks to construct diversified anomaly portfolios, they find that: Keep Reading

Diversifying Across Equity Anomalies

Is diversification across equity anomalies beneficial? In his December 2009 preliminary paper entitled “Diversification Across Characteristics”, Erik Hjalmarsson combines long-short portfolios formed on seven stock anomalies:

  1. Short-term (one-month) reversal (ST-R)
  2. Medium-term (11 months plus skip-month) momentum (Mom)
  3. Long-term (four years plus skip-year) reversal (LT-R)
  4. Book-to-market value (B/M)
  5. Cash flow-to-price ratio (C/P)
  6. Earnings-to-price ratio (E/P)
  7. Market capitalization (Size)

The portfolio for each anomaly is long (short) on an equally weighted basis the tenth of stocks expected to generate the most positive (negative) returns, reformed each month. Using monthly firm characteristics and return data for all NYSE, AMEX and NASDAQ stocks over the period July 1951 through December 2008, he finds that: Keep Reading

Interaction of Calendar Effects with Other Anomalies

Do stock return anomalies exhibit January and month-of-quarter (first, second or third, excluding January) effects? In his February 2015 paper entitled “Seasonalities in Anomalies”, Vincent Bogousslavsky investigates whether the following 11 widely cited U.S. stock return anomalies exhibit these effects:

  1. Market capitalization (size) – market capitalization last month.
  2. Book-to-market – book equity (excluding stocks with negative values) divided by market capitalization last December.
  3. Gross profitability – revenue minus cost of goods sold divided by total assets.
  4. Asset growth – Annual change in total assets.
  5. Accruals – change in working capital minus depreciation, divided by average total assets the last two years.
  6. Net stock issuance – growth rate of split-adjusted shares outstanding at fiscal year end.
  7. Change in turnover – difference between turnover last month and average turnover the prior six months.
  8. Illiquidity – average illiquidity the previous year.
  9. Idiosyncratic volatility – standard deviation of residuals from regression of daily excess returns on market, size and book-to-market factors.
  10. Momentum – past six-month return, skipping the last month.
  11. 12-month effect – average return in month t−k*12, for k = 6, 7, 8, 9, 10.

Each month, he sorts stocks into tenths (deciles) based on each anomaly variable and forms portfolios that are long (short) the decile with the highest (lowest) values of the variable. He updates all accounting inputs annually at the end of June based on data for the previous fiscal year. Using accounting data and monthly returns for a broad sample of U.S. common stocks during January 1964 to December 2013, he finds that: Keep Reading

Beat the Market with Hot-Anomaly Switching?

Can investors beat the market by iteratively finding and exploiting the current hot anomaly? In the September 2009 update of his paper entitled “Real-Time Profitability of Published Anomalies: An Out-of-Sample Test”, Zhijian Huang investigates whether a trader can realize excess returns by repeatedly picking the anomaly with the best return during a rolling historical window from an expanding universe of anomalies as published, with a specific objective of suppressing data snooping bias. The universe includes anomalies that: (1) have been published in at least one of five top-ranked finance journals; (2) relate to the calendar or to cross-sectional predictability; and, (3) can be re-evaluated annually. Using monthly return data associated with 11 anomalies published during 1972-2005 (Monday/weekend effect, January effect and cross-sectional effects related to size, book-to-market ratio, momentum, earnings-price ratio, cash flow-price ratio, dividend yield, debt-equity ratio, sales growth and trading volume/turnover) as available from 1926 through 2008, he concludes that: Keep Reading

Interaction of Firm News and Stock Return Anomalies

Does firm news reliably interact with stock return anomalies? In their July 2015 paper entitled “Anomalies and News”, Joseph Engelberg, David McLean and Jeffrey Pontiff compare anomaly returns on days with and without firm-specific news releases. They consider 97 anomalies published in 80 academic papers. For some analyses, they segregate these anomalies into four categories: (1) firm event-related (such as stock issuance); (2) market (such as momentum); (3) valuation (such as earnings-price ratio); and, (4) fundamental (such as acruals). They measure each anomaly using the extreme fifths (quintiles) of monthly stock sorts to specify a long side and short side. They calculate returns in three-day intervals around news days. Using stock and firm data required to construct anomaly portfolios, 489,996 earnings announcements and 6,223,007 Dow Jones news items during 1979 through 2013, they find that: Keep Reading

Chapter 5: Checking for Market Adaptation

The market is a complex system with many interacting parts, and external influences. As in other social settings, there are two aspects to market evolution: (1) adaptation to changes in external influences; and, (2) adaptation to adjust internal imbalances.

External influences include economic forces, political shifts, monetary policies, regulatory initiatives and information technology enhancements. For example:

  • Economic globalization broadens the universe of assets available in the market, but tends to increase co-movement of assets.
  • Political shifts may favor one industry over another or affect portfolio-level after-tax profitability of investing.
  • Loose monetary policy may favor the financial industry.
  • Regulatory actions on broker fees, quote granularity, short selling and margin levels impact investment frictions (profitability of trading) and cash requirements (portfolio-level returns).
  • Mass availability of historical data and investing knowledge, computing power, analysis software and real-time trading accelerate market identification of and response to all market opportunities.

Investor adaptation to such influences is generally strategic.

Some investors continuously strive to identify and exploit internal market imbalances (pricing anomalies) through fundamental and technical analysis, both asset-specific and marketwide. They express perceived imbalances in different ways, such as:

  • Undervalued versus overvalued
  • Overbought versus oversold
  • Too fearful versus too complacent
  • Risk-on versus risk-off
  • Informed versus noise

When many investors compete in exploiting an imbalance, they supply negative feedback that suppresses it. When more investors compete, suppression is faster. More generally and abstractly, acts of exploiting characteristics of an inferred distribution of investing returns change the distribution. (There is an extensive body of countering research that attributes perceived internal market “imbalances” to rational equilibriums based on actual, but sometimes subtle, risks. The counter-counter is a proposition that people are not even grossly rational, let alone subtly rational.)

The following sections discuss ways to detect and deal with market adaptation. Keep Reading

Interaction of Investor Sentiment and Stock Return Anomalies

Does aggregate investor sentiment affect the strength of well-known U.S. stock return anomalies? In their January 2011 paper entitled “The Short of It: Investor Sentiment and Anomalies”, Robert Stambaugh, Jianfeng Yu and Yu Yuan explore the interaction of aggregate investor sentiment with 11 cross-sectional stock return anomalies. Their approach reflects expectations that: (1) overpricing of stocks is more common than underpricing due to short-sale constraints; and, (2) a high sentiment level amplifies overpricing. Specifically, they consider the effect of investor sentiment on hedge portfolios that are long (short) the highest(lowest)-performing) value-weighted deciles of stocks sorted on: financial distress (two measures), net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profit-to-assets, asset growth, return-on-assets and investment-to-assets. They use a long-run sentiment index derived from principal component analysis of six sentiment measures: trading volume as measured by NYSE turnover; the dividend premium; the closed-end fund discount; the number of and first-day returns on Initial Public Offerings; and, the equity share in new issues. They measure anomaly alphas relative to the three-factor model (adjusting for market, size, book-to-market). Using monthly sentiment and stock return anomaly data as available over the period July 1965 through January 2008, they find that: Keep Reading

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