Calendar Effects

Do asset returns exhibit cyclic relative strength? In the December 2015 revision of their paper entitled “Return Seasonalities”, Matti Keloharju, Juhani Linnainmaa and Peter Nyberg examine 12-month relative strength cycles via a strategy that is each month long (short) assets with the highest (lowest) returns during the same calendar month over the past 20 years. They apply this strategy to individual U.S. stocks, factor and anomaly portfolios of U.S. stocks, industry portfolios of U.S. stocks, developed country stock indexes and commodity futures contract series. They also test a 5-day relative strength cycle across individual U.S. stocks. They perform ancillary tests to investigate sources and interactions of relative strength cycles. Using monthly and daily data for a broad sample of U.S. common stocks, industry portfolios and factor/anomaly portfolios mostly since July 1963 and monthly data for 24 commodity futures series and 15 country stock indexes since January 1970, all through December 2011, they find that: Keep Reading

Does the January (turn-of-the-year) stock return anomaly affect value and momentum strategies applied at the country stock market level? In his June 2015 paper entitled “The January Seasonality and the Performance of Country-Level Value and Momentum Strategies”, Adam Zaremba investigates this question using four value and two momentum firm/stock metrics. The four value metrics, each measured over four prior quarters with a one-quarter lag and weighted by company according to the methodology of the associated stock index, are:

1. Earnings-to-price ratio (EP).
2. Earnings before interest, taxes, depreciation and amortization (EBITDA)-to-enterprise value (EV) ratio (EBEV).
3. EBITDA-to-price ratio (EBP).
4. Sales-to-EV ratio (SEV).

The two momentum metrics are:

1. Stock index return from 12 months ago to one month ago (LtMom).
2. Stock index return from 12 months ago to six months ago (IntMom).

He assesses strategy performance via returns in U.S. dollars in excess of one-month U.S. Treasury bill yield from hedge portfolios that are each month long (short) the equally weighted fifth of country stock indexes with the highest (lowest) expected returns based on each metric. He first reviews performances for all months and then focuses on turn-of-the-year (December and January) performances. Using monthly data for 78 existing and discontinued country stock market indexes during June 1995 through May 2015, he finds that: Keep Reading

How good can factor investing get? In his May 2016 paper entitled “Quantitative Style Investing”, Mike Dickson examines strategies that:

1. Aggregate return forecasting power of four or six theoretically-motivated stock factors (or characteristics) via monthly multivariate regressions.
2. Use inception-to-date simple averages of regression coefficients, starting after the first 60 months and updating annually, to suppress estimation and sampling error.
3. Create equally weighted portfolios that are long (short) the 50%, 20%, 10%, 4%, 2% or 1% of stocks with the highest (lowest) expected returns.

The six stock characteristics are: (1) market capitalization; (2), book-to-market ratio; (3) gross profit-to-asset ratio; (4) investment (annual total asset growth); (5) last-month return; and, (6) momentum (return from 12 months ago to two months ago). He considers strategies employing all six characteristics (Model 1) or just the first four, slow-moving ones (Model 2). He considers samples with or without microcaps (capitalizations less than the 20% percentile for NYSE stocks). He estimates trading frictions as 1% of the value traded each month in rebalancing to equal weight. Using monthly data for a broad sample of U.S. common stocks during July 1963 through December 2013 (with evaluated returns commencing July 1968), he finds that: Keep Reading

Do the 12 yearly signs of the Chinese Zodiac cycle (Rabbit, Dragon, Snake, Horse, Goat, Monkey, Rooster, Dog, Pig, Rat, Ox, Tiger) relate individually to stock market behavior? In their January 2016 paper entitled “The Zodiac Calendar and Equity Factor Returns”, Janice Phoeng and Laurens Swinkels calculate four annual equity factor returns for each of the Zodiac signs: (1) market minus the risk-free rate; (2) small capitalization minus big capitalization; (3) value minus growth; and, (4) high momentum versus low momentum. They start each year on the first day of the Zodiac New Year and end at the last day of the same Zodiac year. Using daily U.S. equity factor returns from Kenneth French’s data library during early February 1927 through mid-February 2015, they find that: Keep Reading

Are moody investors prone to avoid risk on Monday and accept it on Friday? In his January 2016 paper entitled “Day of the Week and the Cross-Section of Returns”, Justin Birru examines how long-short U.S. stock anomaly portfolio returns vary by day of the week. His hypothesis is that pessimistic (optimistic) mood on Monday (Friday) leads to relatively low (high) returns for speculative stocks. His analysis focuses on 14 anomalies arguably tied to investor sentiment, with one side (short or long) speculative and the other side non-speculative, based on idiosyncratic volatility, lottery-like, firm age, distress, profitability, payouts, size or illiquidity. He also tests anomalies arguably unrelated to investor sentiment based on momentum, book-to-market, and asset growth. Using anomaly variable and return data for a broad sample of U.S. common stocks during July 1963 through December 2013, he finds that: Keep Reading

Does seasonality usefully combine with trend following for timing asset markets? In his January 2016 paper entitled “Multi-Asset Seasonality and Trend-Following Strategies”, Nick Baltas examines seasonal patterns (based on same calendar month over the past ten years) for four asset classes: commodities, government bonds, currency exchange rates and country equity markets. He then tests whether identified seasonal patterns enhance a simple trend-following strategy that is long (short) the inverse volatility-weighted assets within a class that have positive (negative) excess returns over the past 12 months. Specifically, he closes any long (short) trend positions in the bottom (top) fifth of seasonality rankings. To assess net performance, he considers trading frictions ranging from 0.05% to 0.25%. Using spot and front futures return data for 19 commodity price indexes and spot return data for 16 10-year government bonds, 10 currency exchange rates and 18 country equity total return indexes as available through December 2014, he finds that: Keep Reading

Do stocks worldwide generate most of their total return while the market is open or closed? In their October 2015 paper entitled “Making Money While You Sleep? Anomalies in International Day and Night Returns”, Kevin Aretz and Sohnke Bartram decompose returns and factor premiums into day and night components. When aggregating returns across countries, they first average within countries and then across countries. They estimate factor premiums in each country by ranking stocks into fifths (quintiles) using the same sorting rules as Fama and French and then calculating differences in value-weighted or equal-weighted average returns between extreme quintiles. Using total returns and accounting variables needed to construct factor returns for 48,413 stocks from 35 countries during 1993 (limited by availability of opening prices) through 2011, they find that: Keep Reading

Can investors refine and exploit the upward bias of overnight stock returns? In the July 2015 version of her paper entitled “Night Trading: Lower Risk but Higher Returns?”, Marie-Eve Lachance presents a way of sorting stocks by strength of overnight return bias and investigates gross and net profitability of associated overnight-only investment strategies. Specifically, she each month regresses daily overnight returns on total returns over the past year to measure an Overnight Bias Parameter (OBP) for each stock. She then forms portfolios based on monthly OBP sorts, focusing on the portfolio of stocks with significantly positive OBPs. She estimates trading frictions by: (1) assuming market-on-open and market-on-close trades, avoiding bid-ask spreads; and, (2) estimating broker charges from the lowest fees available in the U.S. in 2014. Using daily overnight (close-to-open) and intraday (open-to-close) total returns, trading data and characteristics for a broad sample of reasonably liquid U.S. stocks during 1995 through 2014, she finds that: Keep Reading

Does the S&P 500 implied volatility index (VIX) exhibit systematic behaviors by day of the week, around turn-of-the-month (TOTM) or around options expiration (OE)? If so, are the behaviors exploitable? Using daily closing levels of VIX since January 1990, daily opening levels of VIX since January 1992 and daily reverse split-adjusted opening and closing levels of iPath S&P 500 VIX Short-Term Futures ETN (VXX) since February 2009, all through early July 2015, we find that: Keep Reading

Is there an exploitable interaction between a stock’s market capitalization and its price? In their February 2015 paper entitled “Nominal Prices Matter”, Vijay Singal and Jitendra Tayal examine the relationship between stock prices and returns after: (1) controlling for market capitalization (size); (2) isolating the month of January; and, (3) excluding very small stocks. They each year perform double-sorts based on end-of-November data first into ranked tenths (deciles) by size and then within each size decile into price deciles. They calculate returns for January and for the calendar year with and without January. Using monthly prices and end-of-November market capitalizations for the 3,000 largest U.S. common stocks during December 1962 through December 2013, quarterly institutional ownership data for each stock during December 1980 through December 2013, and actual number of shareholders for each stock during 2004 through 2012, they find that: Keep Reading