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

Investing Research Articles

Page 4 of 24712345678910...Last »

Not the Simplest Asset Class ETF Momentum Strategy

Does adding international equity exposure and an escape to “cash” enhance performance of a relative momentum strategy that switches between stock and U.S. Treasury bond exchange-traded funds (ETF)? In his February 2018 paper entitled “Simple and Effective Market Timing with Tactical Asset Allocation Part 2 – Choices”, Lewis Glenn updates and considers two extensions to a strategy summarized in “Simplest Asset Class ETF Momentum Strategy?” that each month holds SPDR S&P 500 (SPY) or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months. Specifically, the three strategies are:

  1. Pair Switching (PS) – the original strategy as described above.
  2. Quint Switching (QS) – adds iShares MSCI EAFE (EFA), PowerShares QQQ (QQQ) and iShares MSCI Emerging Markets (EEM) to the asset universe, each month picking the top performer.
  3. Quint Switching Filtered (QSF) – modifies QS by adding a rule that if any of SPY, TLT, EFA, QQQ and EFA have non-positive returns over the lookback interval, switch to iShares Barclays 7-10 Year Treasury (IEF) . 

For all strategies, he includes 0.1% switching frictions for each buy and sell action. He focuses on compound annual growth rate (CAGR) and maximum drawdown (DDDmax) as key strategy performance metrics. He considers momentum ranking (lookback) intervals of 1 to 5 months to determine the optimal interval for the two strategy extensions. Using monthly dividend-adjusted closes of the specified funds during April 2004 through January 2018, he finds that:

Keep Reading

Do High-dividend Stock ETFs Beat the Market?

A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate from a practical perspective, we compare the performance of five high-dividend stock exchange-traded funds (ETFs) with relatively long histories to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The five high-dividend stock ETFs are:

iShares Select Dividend (DVY), with inception November 2003.
PowerShares Dividend Achievers ETF (PFM), with inception September 2005.
SPDR S&P Dividend ETF (SDY), with inception November 2005.
WisdomTree Dividend ex-Financials ETF (DTN), with inception June 2006.
Vanguard High Dividend Yield ETF (VYM), with inception November 2006.

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of total monthly returns, monthly reward-to-risk ratio (average monthly return divided by standard deviation of monthly returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) to those for SPY over matched sample periods. We also look at alphas and betas for the five ETFs based on simple regressions of monthly returns versus SPY returns. Using monthly total returns for the five high-dividend stock ETFs and SPY over the available sample periods through February 2018, we find that:

Keep Reading

Warren Buffett on Investing

Does Warren Buffett consistently keep Berkshire Hathaway in market-beating form? If so, how does he do it? In his annual letters to stockholders, he includes company performance and benchmark data and describes in general terms how he goes about investing. He sometimes shares his thoughts on the current state of and prospects for the U.S. equity market. Using annual performance data from his 2017 letter to Berkshire Hathaway shareholders for 1965 through 2017 (53 years) and the investing approach/outlooks described in his letters of 1977 through 2017, we find that: Keep Reading

Methods for Mitigating Data Snooping Bias

What methods are available to suppress data snooping bias derived from testing multiple strategies/strategy variations on the same set of historical data? Which methods are best? In their March 2018 paper entitled “Systematic Testing of Systematic Trading Strategies”, Kovlin Perumal and Emlyn Flint survey statistical methods for suppressing data snooping bias and compare effectiveness of these methods on simulated asset return data and artificial trading rules. They choose a Jump Diffusion model to simulate asset return data, because it reasonably captures volatility and jumps observed in real markets. They define artificial trading rules simply in terms of probability of successfully predicting next-interval return sign. They test the power of each method by: (1) measuring its ability not to choose inaccurate trading rules; and, (2) relating confidence levels it assigns to strategies to profitabilities of those strategies. Using the specified asset return data and trading rule simulation approaches, they conclude that: Keep Reading

Will the November 2016-December 2017 Run-up in U.S. Stocks Stick?

Is the strong gain in the U.S. stock market following the November 2016 national election rational or irrational? In their February 2018 paper “Why Has the Stock Market Risen So Much Since the US Presidential Election?”, flagged by a subscriber, Olivier Blanchard, Christopher Collins, Mohammad Jahan-Parvar, Thomas Pellet and Beth Anne Wilson examine sources of the 25% U.S. stock market advance during November 2016 through December 2017. They consider four sources: (1) increases in actual and expected dividends; (2) perceived probability and the fact of a reduction in the corporate tax rate; (3) decrease in the U.S. equity risk premium; and, (4) an irrational price bubble. For the impact of the tax rate reduction on corporate income, they use estimates from the Joint Congressional Committee on Taxation. For the relationship between dividends and the equity risk premium, they assume the difference between dividend-price ratio and risk-free rate equals equity risk premium minus expected dividend growth rate. They also consider the effect of U.S. and European economic policy uncertainty on the U.S. equity risk premium. Using the specified data during November 2016 (and earlier for validation) through December 2017, they find that: Keep Reading

Weekly Summary of Research Findings: 3/12/18 – 3/16/18

Below is a weekly summary of our research findings for 3/12/18 through 3/16/18. 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

Rise and Fall of the Fed Model?

What is the historical relationship between U.S. stock market earnings yield (E/P) and U.S. government bond yield (Y)? In their February 2018 paper entitled “Stock Earnings and Bond Yields in the US 1871 – 2016: The Story of a Changing Relationship”, Valeriy Zakamulin and Arngrim Hunnes examine the relationship between E/P Y over the long run, with focus on structural breaks, causes of breaks and direction of causality. They employ a vector error correction model that allows multiple structural breaks. In assessing causes of breaks, they consider inflation, income taxes and Federal Reserve Bank monetary policy. Using quarterly S&P Composite Index level, index earnings, long-term government bond yield and inflation data during 1871 through 2016, along with contemporaneous income tax rates and Federal Reserve monetary actions, they find that:

Keep Reading

Simple Stock Index Option Strategies

Do simple stock index option strategies (stock-covered calls, cash-covered puts and collars) outperform the underlying index? To investigate, we examine first the performance of the CBOE S&P 500 BuyWrite Index (BXM), the CBOE S&P 500 PutWrite Index (PUT) and the CBOE S&P 500 95-110 Collar Index (CLL), with the S&P 500 Total Return Index SPTR) as a benchmark. Since these series are modeled indexes rather than tradable assets, we then examine the comparatively short records of exchange-traded funds (ETF) and notes (ETN) designed to track BXM, iPath CBOE S&P 500 BuyWrite Index ETN (BWV) and PowerShares S&P 500 BuyWrite (PBP), with SPDR S&P 500 (SPY) as a benchmark. We focus on monthly return statistics, compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for comparisons. Using end-of-month levels/total returns for SPTR, BXM, PUT and CLL since June 1986, and for SPYBWV and PBP since December 2007 (limited by inception of PBP), all through February 2018, we find that:

Keep Reading

Data Perturb/Replay to Test Strategy Sensitivities

How can investment advisors apply historical asset performance data to address client views regarding future market/economic conditions? In their February 2018 paper entitled “Matching Market Views and Strategies: A New Risk Framework for Optimal Selection”, Adil Reghai and Gaël Riboulet present an approach for quantitatively relating historical asset return statistics to investor views. They intend this approach to address the widespread problem of backtest overfitting, whereby researchers discover good performance by fitting strategy features to noise in an historical dataset. Specifically, they:

  1. Collect historical return data for assets of interest and run backtests of alternative strategies on these data.
  2. Perturb historical average return, volatility, skewness and pairwise correlations up or down for these assets and rerun backtests of alternative strategies on multiple perturbations.
  3. Analyze relationships between directions of these perturbations and performance of alternative strategies.
  4. Match investor views first to directions of perturbations and then to strategies responding favorably (or least unfavorably) to these directions.

They apply this approach to generic algorithmic strategies (equal weight, momentum, mean reversion and carry). Based on mathematical derivations and examples, they conclude that: Keep Reading

T-note Yield Divergence from Trend and Future Stock Market Return

A subscriber requested review of a finding that deviation of 10-year constant maturity U.S. Treasury note (T-note) yield from an intermediate-term linear trend predicts U.S. stock market return. Specifically, when weekly yield is more than one standard deviation of weekly trend divergences below (above) a weekly 70-week linear extrapolation, next-week S&P 500 Index return is on average unusually high (low). To confirm and test usefulness of this finding, we each week:

  1. Perform a linear extrapolation of past T-note yields to forecast next-week T-note yield, but using a 52-week rolling window rather than a 70-week window. A 52-week lookback aligns with an annual inflation cycle, while a 70-week lookback seems arbitrary and may be snooped.
  2. Calculate the difference between next-week actual and forecasted T-note yields.
  3. Calculate the standard deviation of these differences over the 52-week rolling window.

We then segment weekly actual minus forecasted T-note yield differences into: those more than one standard deviation below forecasted yield (Below Lower); those between one standard deviation below and above forecasted yield (Between); and, those more than one standard deviation above forecasted yield (Above Upper). Next, we calculate next-week S&P 500 Index returns for these three segments. Limited by availability of weekly T-note yield data, return calculations commence January 1964. To check robustness of results, we also consider a recent subsample commencing January 2008. To test economic value of findings, we examine a Dynamic Weighted strategy that modifies a benchmark 60% allocation to SPDR S&P 500 (SPY) and 40% allocation to iShares Barclays 7-10 Year Treasuries (IEF), rebalanced weekly, to 80% SPY when T-note condition the prior week is Below Lower and 40% SPY when Above Upper. The strategy backtest commences with inception of IEF at the end of July 2002 and focuses on weekly return statistics, compound annual growth rate (CAGR) and maximum drawdown (MaxDD), ignoring rebalancing/reallocation frictions. Using weekly T-note yields (average of daily values measured on Friday) and contemporaneous S&P 500 Index levels since January 1962, and weekly dividend-adjusted levels of SPY and IEF since July 2002, all through January 2018, we find that: Keep Reading

Page 4 of 24712345678910...Last »
Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts
Popular Subscriber-Only Posts