Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for July 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for July 2022 (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.

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:

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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:

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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:

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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

Combining the Smart Money Indicator with SACEMS and SACEVS

“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading

Effects of Execution Delay on SACEVS

How does execution delay affect the performance of the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS)? These strategies each month allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

To investigate, we compare 22 variations of each strategy with execution days ranging from end-of-month (EOM) per the baseline strategy to 21 trading days after EOM (EOM+21). For example, an EOM+5 variation computes allocations based on EOM but delays execution until the close five trading days after EOM. We include a benchmark that each month allocates 60% to SPY and 40% to TLT (60-40) to see whether variations are unique to SACEVS. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance statistics. Using daily dividend-adjusted closes for the above ETFs from the end of July 2002 through January 2020, we find that:

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