Objective research to aid investing decisions
Value Allocations for October 2019 (Final)
Momentum Allocations for October 2019 (Final)
1st ETF 2nd ETF 3rd ETF

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.

Global Factor Premiums Over the Very Long Run

Do very old data confirm reliability of widely accepted asset return factor premiums? In their January 2019 paper entitled “Global Factor Premiums”, Guido Baltussen, Laurens Swinkels and Pim van Vliet present replication (1981-2011) and out-of-sample (1800-1908 and 2012-2016) tests of six global factor premiums across four asset classes. The asset classes are equity indexes, government bonds, commodities and currencies. The factors are: time series (intrinsic or absolute) momentum, designated as trend; cross-sectional (relative) momentum, designated as momentum; value; carry (long high yields and short low yields); seasonality (rolling “hot” months); and, betting against beta (BAB). They explicitly account for p-hacking (data snooping bias) and further explore economic explanations of global factor premiums. Using monthly global data as available during 1800 through 2016 to construct the six factors and four asset class return series, they find that:

Keep Reading

Rebalance Timing Noise

Does choice of multi-asset portfolio rebalance date(s) materially affect performance? In their October 2018 paper entitled “Rebalance Timing Luck: The Difference Between Hired and Fired”, Corey Hoffstein, Justin Sibears and Nathan Faber investigate effects of varying portfolio rebalance date on performance. Specifically, they quantify noise (luck) from varying annual rebalance date for a 60% S&P 500 Index-40% 5-year constant maturity U.S. Treasury note (60-40) U.S. market portfolio. Using monthly total returns for these two assets during January 1922 through June 2018, they find that: Keep Reading

Crude Oil Seasonality

Does crude oil exhibit an exploitable price seasonality? To check, we examine three monthly series:

  1. Spot prices for West Texas Intermediate (WTI) Cushing, Oklahoma crude oil since the beginning of 1986 (32 years).
  2. Nearest expiration futures prices for crude oil since April 1983 (35+ years).
  3. Prices for United States Oil (USO), an exchange-traded implementation of short-term crude oil futures since April 2006 (12+ years).

We focus on average monthly returns by calendar month and variabilities of same. Using monthly prices from respective inceptions of these series through December 2018, we find that: Keep Reading

Stock Market and the Super Bowl

Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index levels for 1967 through 2018 (52 events), we find that: Keep Reading

U.S. Stock Market Performance by Intra-year Phase

The full-year Trading Calendar indicates that the U.S. stock market has three phases over the calendar year, corresponding to calendar year trading days 1-84 (January-April), 85-210 (May-October) and 211-252 (November-December). What are typical stock market returns and return variabilities for these phases? Using daily S&P 500 Index closes during 1950 through 2018 (69 years), we find that: Keep Reading

Sector Performance by Calendar Month

Trading Calendar presents full-year and monthly cumulative performance profiles for the overall U.S. stock market (proxied by the S&P 500 Index) based on its average daily behavior since 1950. Do monthly behaviors of U.S. stock market sectors deviate from the overall market profile? To investigate, we consider the nine Select Sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF), all of which originate in December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using monthly dividend-adjusted closing prices for these ETFs, along with contemporaneous data for SPDR S&P 500 (SPY) as a benchmark, during December 1998 through December 2018 (20 years), we find that: Keep Reading

Stock Returns Around New Year’s Day

Does the New Year’s Day holiday, a time of replanning and income tax positioning, systematically affect investors in a way that translates into U.S. stock market returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the five trading days before and the five trading days after the holiday. Using daily closing levels of the S&P 500 Index around New Year’s Day for 1951-2018 (68 observations), we find that: Keep Reading

Stock Returns Around Christmas

Does the Christmas holiday, a time of putative good will toward all, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during five trading days before through five trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950-2017 (68 events), we find that: Keep Reading

Does the Sunspot Cycle Predict Grain Prices?

As a follow-up to “Sunspot Cycle and Stock Market Returns” a reader asked: “Sunspot activity does have a direct relationship to weather. Could one speculate on the agriculture market using the sunspot cycle?” To investigate, we relate sunspot activity to the fairly long U.S. Producer Price Index (PPI) for grains. Using monthly averages of daily sunspot counts and monthly PPI for grains during January 1926 (limited by PPI data) through October 2018, we find that: Keep Reading

Sunspot Cycle and Stock Market Returns

A reader asked whether Charles Nenner, self-described as “the talk of Wall Street since accurately predicting some of the biggest moves in the Markets over the past few years,” accurately forecasts equity and commodity markets. We consider the following:

  • In his July 2007 discussion of the “Nenner Methodology at the Bloomberg Studio”, Charles Nenner cites sunspot activity as a specific key indicator for equity returns. Per this source, he believes that the sunspot cycle correlates strongly with equity markets via the predictable effects of magnetic field disturbances on investors.
  • In “Sunspots Predict ‘Major Crisis’ After 2013: Chartist”, he states: “If there is a high intensity of sunspots, markets rise, if their intensity lowers, markets go down because sunspots affect people’s mood.”

Is there a reliable relationship between sunspot activity and stock market returns? Using monthly averages of daily sunspot counts and monthly levels of Shiller’s S&P Composite Index (also monthly averages of daily levels) during January 1871 (limited by the Shiller data) through October 2018, we find that: Keep Reading

Daily Email Updates
Research Categories
Recent Research
Popular Posts