Objective research to aid investing decisions
Value Allocations for Dec 2018 (Final)
Cash TLT LQD SPY
Momentum Allocations for Dec 2018 (Final)
1st ETF 2nd ETF 3rd ETF
CXO Advisory

Calendar Effects

The time of year affects human activities and moods, both through natural variations in the environment and through artificial customs and laws. Do such calendar effects systematically and significantly influence investor/trader attention and mood, and thereby equity prices? These blog entries relate to calendar effects in the stock market.

Page 7 of 15« First...3456789101112...Last »

Combining Seasonality and Trend Following by Asset Class

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

Day and Night Stock Returns Worldwide

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

Simple Tests of Sy Harding’s Seasonal Timing Strategy

Several readers have inquired over the years about the performance of Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death), which included the Seasonal Timing Strategy. This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. For robustness testing, we apply this strategy to SPDR S&P 500 (SPY) since its inception and consider several alternatives, as follows:

  1. SPY – buy and hold SPY.
  2. Seasonal-MACD – seasonal timing with MACD refinement.
  3. Seasonal Only – seasonal timing without MACD refinement.
  4. SMA200 – hold SPY (13-week U.S. Treasury bills (T-bills) when the S&P 500 Index is above (below) its 200-day simple moving average at the prior daily close. 

Using daily closes for the S&P 500 Index, daily dividend-adjusted closes for SPY and daily T-bill yields during 1/29/93 (SPY inception) through 9/25/15, we find that: Keep Reading

Overnight Momentum-informed Overnight Trading

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

Short-term VIX Calendar Effects

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

Interactions among Stock Size, Stock Price and the January Effect

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

Year-end Global Growth and Future Asset Class Returns

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find 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

VIX-VXX Seasonality

Does the S&P 500 Implied Volatility Index (VIX) exhibit exploitable seasonality? To check, we calculate average monthly change in VIX and and average iPath S&P 500 VIX Short-Term Futures ETN (VXX) monthly return by calendar month. Using monthly closes of VIX since January 1990 and monthly reverse split-adjusted closes for VXX since January 2009, both through December 2014, we find that: Keep Reading

Momentum Happens at Night?

Are overnight trading motivations systematically different from those that drive trading during normal trading hours? In the January 2015 version of their paper entitled “Tug of War: Overnight Versus Intraday Expected Returns”, flagged by a subscriber, Dong Lou, Christopher Polk and Spyros Skouras (1) decompose abnormal returns associated with well-known stock return predictors into overnight and intraday components and (2) investigate whether differences between institutional and other traders account for differences. Using return, firm characteristic and institutional ownership data for a broad sample of U.S. stocks (excluding low-priced and the smallest fifth of stocks) during 1993 through 2013, they find that: Keep Reading

Page 7 of 15« First...3456789101112...Last »
Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts
Popular Subscriber-Only Posts