Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for November 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for November 2021 (Final)
1st ETF 2nd ETF 3rd ETF

Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

Enhancement of Dual Momentum with Just Three Assets?

In response to “Review of Dual Momentum with Just Three Assets”, a subscriber suggested adding gold in competition with long-term U.S. Treasury bonds as a safe haven from equities. To test this potential enhancement of Accelerating Dual Momentum (ADM), we each month:

  1. Calculate for each of SPDR S&P 500 (SPY), iShares MSCI EAFE Small-Cap ETF (SCZ), iShares 20+ Year Treasury Bond (TLT) and SPDR Gold Shares (GLD) the sum of its 1-month, 3-month and 6-month past returns.
  2. If sums for both SPY and SCZ are negative, buy the one of TLT and GLD with the higher sum.
  3. If both sums for SPY and SCZ are not negative, buy the one with the higher sum.

Using end-of-month dividend-adjusted prices of these ETFs during December 2007 (limited by SCZ) through April 2020, we find that: Keep Reading

Tech Premium Boost for Simplest Asset Class Momentum Strategy?

In response to “Tech Equity Premium?”, a subscriber asked about substituting Invesco QQQ Trust (QQQ) for SPDR S&P 500 (SPY) in the “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPY or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months. To investigate, we run a horse race between the strategy executed with SPY (SPY-TLT) and the strategy executed with QQQ (QQQ-TLT). We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) as performance metrics and assess robustness across lookback intervals of one to 12 months. Using monthly dividend-adjusted prices for SPY, QQQ and TLT during July 2002 (limited by TLT) through April 2020, we find that: Keep Reading

SACEMS at Weekly and Biweekly Frequencies

A subscriber asked for an update on whether weekly or biweekly (every two weeks) measurement of asset class momentum works better than monthly measurement as used in “Simple Asset Class ETF Momentum Strategy (SACEMS)” (SACEMS). Do higher measurement frequencies respond more efficiently to market turns? To investigate, we compare performances of strategies based on monthly, weekly and biweekly frequencies with comparable lookback intervals. For this comparison, we align weekly and biweekly results with monthly results, though they differ somewhat due to mismatches between ends of weeks and ends of months. We consider portfolios of past ETF winners based on Top 1 and on equally weighted (EW) Top 2 and Top 3. Using weekly dividend-adjusted closing prices for the asset class proxies per baseline SACEMS and the yield for Cash during February 2006  through April 2020, we find that: Keep Reading

Multi-strategy Portfolio Design Approach

How should investors think about combining strategies into a broader portfolio that reliably exploits their interactions over time? In the March 2020 version of his paper entitled “Preferred Portfolios: An Improved Blueprint to Construct Multi Strategy Portfolios”, Lars Kestner discusses how to combine individual strategies into a portfolio that performs robustly out-of-sample base on five principles. His objective is to sift data with a systematic process, find small edges and fit them together into a reliable combination of return streams that in aggregate perform well under almost all market conditions. His process employs two sets of building blocks: (1) diverse quantitative strategies clustered into four categories; and, (2) nine asset markets/classes. Based on theoretical considerations and his experience as an investment manager, he concludes that:

Keep Reading

Federal Reserve Holdings and the U.S. Stock Market

Using quarterly data in their April 2013 preliminary paper entitled “Analyzing Federal Reserve Asset Purchases: From Whom Does the Fed Buy?” Seth Carpenter, Selva Demiralp, Jane Ihrig and Elizabeth Klee find that some categories of investors appear to sell U.S. Treasuries to the Federal Reserve and rebalance toward riskier assets (corporate bonds, commercial paper, and municipal debt). Are stocks a part of this process? To investigate, we relate weekly, monthly and quarterly U.S. stock market returns to changes in the Federal Reserve’s System Open Market Account (SOMA) holdings, comprised of U.S. Treasury bills, U.S. Treasury notes and bonds, U.S. Treasury Inflation-Protected Securities (TIP) and Mortgage-Backed Securities (MBS). The Federal Reserve reports these holdings with a small lag. Using weekly (Wednesday close) dividend-adjusted prices for SPDR S&P 500 (SPY) as a stock market proxy and total SOMA holdings during early July 2003 through mid-April 2020, we find that: Keep Reading

Comparing Ivy 5 Allocation Strategy Variations

A subscriber requested comparison of four variations of an “Ivy 5” asset class allocation strategy, as follows:

  1. Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
  2. Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
  3. Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
  4. Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

To perform the tests, we employ the following five asset class proxies:

iShares 7-10 Year Treasury Bond (IEF)
SPDR S&P 500 (SPY)
Vanguard REIT ETF (VNQ)
iShares MSCI EAFE Index (EFA)
PowerShares DB Commodity Index Tracking (DBC)

We consider monthly performance statistics, annual performance statistics, and full-sample compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Annual Sharpe ratio uses average monthly yield on 3-month U.S. Treasury bills (T-bills) as the risk-free rate. The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and T-bill yield as return on cash during February 2006 through March 2020, we find that:

Keep Reading

Testing Zweig’s Combined Super Model

A subscriber requested testing Martin Zweig’s Combined Super Model, which each month specifies an equity allocation based on a system that assigns up to eight points from his Monetary Model and 0 or 2 points from his Four Percent Model. We consider two versions of the Combined Super Model:

  1. Zweig-Cash – Allocate to Fidelity Fund (FFIDX) as equities, with the balance in cash earning the 3-month U.S. Treasury bill (T-bill) yield.
  2. Zweig-FGOVX – Allocate to FFIDX as equities, with the balance in Fidelity Government Income Fund (FGOVX)

The benchmark is buying and holding FFIDX. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio, with average monthly T-bill yield during a year as the risk-free rate for that year. We ignore impediments to mutual fund trading and any issues regarding timeliness of allocation changes for end-of-month rebalancing. Using monthly Combined Super Model allocations and monthly fund returns/T-bill yield during December 1986 through March 2020, we find that: Keep Reading

Equity Factor Performance During the 2010s

Are equity factors used in leading models of stock returns reliable performers in practice? In his March 2020 paper entitled “Factor Performance 2010-2019: A Lost Decade?”, David Blitz measures performances of factors tracked in the Kenneth French data library and the q-factor model library during 2010-2019 and compares results to their performances in prior decades. Using data from these libraries for 32 U.S. equity factors and six global non-U.S. factors over available sample periods through 2019, he finds that:

Keep Reading

SACEMS with SMA Filter

A subscriber asked whether applying a simple moving average (SMA) filter to “Simple Asset Class ETF Momentum Strategy” (SACEMS) winners improves strategy performance. SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each winner pass an SMA10 filter by comparing performances for three scenarios:

  1. Baseline – SACEMS as presented at “Momentum Strategy”.
  2. With SMA10 Filter – Run Baseline SACEMS and then apply SMA10 filters to dividend-adjusted prices of winners. If a winner is above (below) its SMA10, hold the winner (Cash). This rule is inapplicable to Cash as a winner.
  3. With Half SMA10 Filter – Same as scenario 2, but, if a winner is above (below) its SMA10, hold the winner (half the winner and half cash).

We focus on compound annual growth rates (CAGR), annual Sharpe ratios and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. To calculate Sharpe ratios, we use average monthly 3-month U.S. Treasury bill (T-bill) yield during a year as the risk-free rate for that year. Using monthly dividend-adjusted closing prices for the asset class proxies and the (T-bill) yield for Cash over the period February 2006 through February 2020, we find that: Keep Reading

COVID-19 Crash Questions

Subscribers are posing questions about the 2019 coronavirus (COVID-19) as a driver of current market conditions that are difficult to address with evidence-based analyses. Here are some questions and thoughts: Keep Reading

Login
Daily Email Updates
Filter Research
  • Research Categories (select one or more)