Investing Research Articles

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Path Dependence of Satisfying Returns

What makes investors happy with investment returns? In the April 2015 version of their paper entitled “All’s Well That Ends Well? On the Importance of How Returns Are Achieved”, Daniel Grosshans and Stefan Zeisberger employ a series of surveys to investigate how investor satisfaction depends on investment price path. Their main survey asks participants to imagine that they bought three winner stocks (10% terminal gain) and three loser stocks (10% terminal loss) one year ago, with the three in each set having distinct price paths: (1) down-up, (2) straight line (monotonic) and (3) up-down (see the figures below). It also asks how likely participants would be to hold or sell each stock, their minimum selling price and an estimate of the stock’s price after one more year. Using results from surveys of participants recruited via Amazon Mechanical Turk (MTurk) and of students in advanced finance courses, they find that: Keep Reading

“What Works Best?” Update

We’ve updated “What Works Best?” to incorporate some recent research summaries and adjust interpretation of the body of research.

Weekly Summary of Research Findings: 4/20/15 – 4/24/15

Below is a weekly summary of our research findings for 4/20/15 through 4/24/15. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Long-term Tests of Simple X% Rules

A subscriber requested long-term tests of simple versions of the strategy described by Jason Kelly in The 3% Signal: The Investing Technique that Will Change Your Life, We start with a general strategy targeting an X% quarterly increase in a stock fund, as follows:

  1. Initiate X% rules with either 80%-20% or 60%-40% allocations to a stock fund and a bond fund.
  2. If over the next quarter the stock fund increases by more than X%, transfer the excess from the stock fund to the bond fund.
  3. If over the next quarter the stock fund increases by less than X%, make up the shortfall by transferring money from the bond fund to the stock fund.
  4. If at the end of any quarter the bond fund does not have enough money to make up a shortfall in the stock fund: either draw the bond fund down to 0 and add cash to make up the rest of the shortfall; or, draw the bond fund down to 0 and bear the rest of the shortfall in the stock fund.
  5. Consider two benchmarks: a 100% allocation to the stock fund (B&H); and, 60%-40% allocations to the stock and bond funds, rebalanced quarterly (60-40). Whenever adding cash to the bond fund per Step 4, add equal amounts to the benchmarks.

We consider for X% a range of 2% to 4% in increments of 0.5%. We employ stock and bond mutual funds with long histories: Fidelity Magellan (FMAGX) and Fidelity Investment Grade Bond (FBNDX). We assume there are no trading frictions when adding or withdrawing money from these funds. Using quarterly returns for these funds from the first quarter of 1972 (limited by FBNDX) through the first quarter of 2015 (43.25 years), we find that:

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Effects of Execution Delay on Simple Asset Class ETF Value Strategy

“Effects of Execution Delay on Simple Asset Class ETF Momentum Strategy” investigates how delaying signal execution affects strategy performance. How does execution delay affect the performance of the complementary Best Value version of the “Simple Asset Class ETF Value Strategy”? This latter strategy each quarter allocates all funds to the one of the following asset class exchange-traded funds (ETF) associated with the most undervalued risk premium (term, credit or equity), or to cash if none are undervalued:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

To investigate, we compare 23 variations of the strategy that all use end-of-quarter (EOQ) to determine the best value asset but shift execution from the contemporaneous EOQ to the next open or to closes over the next 21 trading days (about one month). For example, an EOQ+5 Close variation uses an EOQ cycle to determine winners but delays execution until the close five trading days after EOQ. Using daily dividend-adjusted opens and closes for the risk premium proxies and the yield for Cash from the end of September 2002 through the end of March 2015 (51 quarters), we find that:

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Effects of Execution Delay on Simple Asset Class ETF Momentum Strategy

“Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?” investigates whether using a monthly cycle other than end-of-month (EOM) to determine the winning asset improves performance of the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

In response, a subscriber asked whether sticking with an EOM cycle for determining the winner, but delaying signal execution, affects strategy performance. To investigate, we compare 23 variations of the strategy that all use EOM to determine the winning asset but shift execution from the contemporaneous EOM to the next open or to closes over the next 21 trading days (about one month). For example, an EOM+5 Close variation uses an EOM cycle to determine winners but delays execution until the close five trading days after EOM. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash from the end of July 2002 (or inception if not available then) through the end of March 2015 (153 months), we find that: Keep Reading

Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?

As explored for a 10-month simple moving average (SMA) in “Optimal Cycle for Monthly SMA Signals?”, subscribers have inquired whether there is a best time of the month for measuring momentum in the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. Using daily dividend-adjusted closes for the asset class proxies and the yield for Cash from late July 2002 (or inception if not available then) through early April 2014 (about 153 months), we find that: Keep Reading

Timing of Asset Class Allocations by Multi-class Funds

Do multi-class mutual funds exhibit good asset class allocation timing? In  their April 2015 paper entitled “Multi-Asset Class Mutual Funds: Can They Time the Market? Evidence from the US, UK and Canada”, Andrew Clare, Niall O’Sullivan, Meadhbh Sherman and Steve Thomas investigate whether mutual fund managers time allocations across asset classes skillfully. They focus on three asset classes: equities, government bonds and corporate bonds. They apply two alternative methodologies: (1) returns-based, relating each asset class beta for a fund to next-month return for that class; and, (2) holdings-based, relating changes in asset class weights within a fund to next-month class returns. Using monthly returns and holdings for 617 U.S., UK and Canadian multi-asset class mutual funds during 2000 through 2012, they find that:

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Weekly Summary of Research Findings: 4/13/15 – 4/17/15

Below is a weekly summary of our research findings for 4/13/15 through 4/17/15. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for March 2015. The actual total (core) inflation rate for March is about the same as (a little higher than) forecasted.

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Current Momentum Winners

ETF Momentum Signal
for April 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
15.1% 15.8%
Top 3 ETFs SPY
15.3% 7.7%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 2nd Quarter 2015 (Final)

Cash

IEF

LQD

SPY

The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
13.7% 9.6% 8.6%
Strategy Overview
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