Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for October 2020 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for October 2020 (Final)
1st ETF 2nd ETF 3rd ETF

Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

How Canadian Pension Funds Outperform

Which institutional investors do best and why? In the September 2020 update of their paper entitled “The Canadian Pension Fund Model: A Quantitative Portrait”, Alexander Beath, Sebastien Betermier, Chris Flynn and Quentin Spehner compare performances of Canadian pension funds and those of other countries, focusing on Sharpe ratio of the fund assets, Sharpe ratio of the fund net portfolio (long assets and short liabilities) and correlation between fund assets and liabilities. They look at both large (over $10 billion U.S. dollars in assets as of 2018) and small funds. They consider two test periods, five years (2014-2018) and 15 years (2004-2018), excluding funds with missing annual data. The 5-five year sample has 250 funds from 11 countries. The 15-year sample has 105 funds. After comparing performance, they look for reasons why Canadian performance differs. Using performance data, asset allocation strategies and cost structures for the selected 250 pension funds, they find that:

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Choosing Investment Managers Poorly?

Do sophisticated investors choose investment managers wisely? In their July 2020 paper entitled “Choosing Investment Managers”, Amit Goyal, Sunil Wahal and Deniz Yavuz investigate how institutional investors select investment managers for public equity and fixed income portfolios. For each actual selection, they construct a group of non-selected investment managers competing in the same geographic region, style and year (average 94 for equity and 72 for fixed income). They focus on two selection criteria:

  1. Investment manager past performance (returns and assets under management for each product offered as collected by eVestment).
  2. Relationships among institutional investors, investment managers and consultants (as collected by Relationship Science).

Using 6,939 investment manager selections (5,005 equity and 1,934 fixed income) by 2,005 global institutions delegating over $1.6 trillion in assets to 775 unique managers during 2002 through 2017, they find that:

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Performance of Yield Enhancement Products

Should investors buy yield enhancement products (YEP), which typically offer higher-than-market yields from a package comprised of an underlying stock or equity index and a series of short put options? In the August 2020 version of her paper entitled “Engineering Lemons”, Petra Vokata examines gross and net performances of YEPs, which embed fees as a front-end discount (load) allocated partly to issuers and partly to distributing brokers as a commission. Using descriptions of underlying assets and cash flows before and at maturity for 28,383 YEPs linked to U.S. equity indexes or stocks and issued between January 2006 and September 2015, and contemporaneous Cboe S&P 500 PutWrite Index (PUT) returns as a benchmark, she finds that:

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Active Investment Managers and Market Timing

Do active investment managers as a group successfully time the stock market? The National Association of Active Investment Managers (NAAIM) is an association of registered investment advisors. “NAAIM member firms who are active money managers are asked each week to provide a number which represents their overall equity exposure at the market close on a specific day of the week (usually Wednesday). Responses can vary widely [200% Leveraged Short; 100% Fully Short; 0% (100% Cash or Hedged to Market Neutral); 100% Fully Invested; 200% Leveraged Long].” The association each week releases (usually on Thursday) the average position of survey respondents as the NAAIM Exposure Index (NEI).” Using historical weekly survey data and Thursday-to-Thursday weekly dividend-adjusted returns for SPDR S&P 500 (SPY) over the period July 2006 through August 2020 (736 surveys), we find that: Keep Reading

When Institutional Investors Seek Safety

How do mutual funds and hedge funds change their stock holdings in response to a sharp market crash? In their July 2020 paper entitled “Where Do Institutional Investors Seek Shelter when Disaster Strikes? Evidence from COVID-19”, Simon Glossner, Pedro Matos, Stefano Ramelli and Alexander Wagner analyze changes in institutional and retail stock holdings during the first quarter of 2020. Using a February-March 2020 snapshot of returns and firm accounting data for non-financial stocks in the Russell 3000 Index, institutional holdings of these stocks as percentages of shares outstanding during the fourth quarter of 2018 through the first quarter of 2020, and number of Robinhood clients (representing retail investors) holding these stocks on December 31, 2019 and March 31, 2020, they find that:

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Allocations and Returns of Endowments

How do U.S. non-profit endowment funds allocate and perform? In their November 2019 paper entitled “The Risk, Reward, and Asset Allocation of Nonprofit Endowment Funds”, Andrew Lo, Egor Matveyev and Stefan Zeume examine recent asset allocations and investment returns of U.S. public non-profit endowment funds. Due to the unstructured nature of asset reporting, they manually assign each asset in each fund to one of nine categories: (1) public equity; (2) fixed income; (3) private equity; (4) cash instruments; (5) hedge funds; (6) real estate; (7) real assets and real return; (8) trusts; and, (9) cooperative investments. Using tax return data encompassing 34,170 endowment funds during 2009 through 2018, they find that: Keep Reading

GMO Forecast Accuracy Test

A subscriber suggested an update of “GMO’s Stunningly Accurate Forecast?” with out-of-sample testing of GMO forecasts. To investigate, we test GMO’s 7-Year asset class real return forecasts of December 31, 2010 and July 31, 2013. We first match the 11 GMO asset classes covered in these forecasts to exchange-traded funds (ETF), as follows:

  1. U.S. equities (large cap) – SPDR S&P 500 ETF Trust (SPY).
  2. U.S. equities (small cap) – iShares Russell 2000 ETF (IWM).
  3. U.S. high quality – Invesco S&P 500 Quality ETF (SPHQ).
  4. International equities (large cap) – iShares MSCI EAFE ETF (EFA).
  5. International equities (small cap) – iShares MSCI EAFE Small-Cap ETF (SCZ).
  6. Emerging equities – iShares MSCI Emerging Markets ETF (EEM).
  7. U.S. bonds (government) – iShares 20+ Year Treasury Bond ETF (TLT).
  8. International bonds (government) – SPDR Bloomberg Barclays International Treasury Bond ETF (BWX).
  9. Emerging bonds – iShares J.P. Morgan USD Emerging Markets Bond ETF (EMB).
  10. Inflation-indexed bonds – iShares TIPS Bond ETF (TIP).
  11. Short-term U.S. Treasuries (30 days to 2 years) – iShares 1-3 Year Treasury Bond ETF (SHY).

We adjust monthly ETF returns for inflation using monthly changes in U.S. Consumer Price Index (CPI), with change in CPI for July 2020 estimated as the monthly average for the rest of the sample. In contrast, GMO apparently assumes constant annualized inflation of 2.5% for both forecasts. We then calculate real compound annual growth rates (CAGR) for each over specified forecast horizons. Using GMO forecasts, dividend-adjusted ETF prices and CPI data during December 2020 through July 2020, we find that: Keep Reading

Day Trading a Bust?

Can individual investors make a living by day trading? In the June 2020 update of their paper entitled “Day Trading for a Living?”, Fernando Chague, Rodrigo De-Losso and Bruno Giovannetti analyze performances of all Brazilian retail investors who begin trading futures on the main Brazilian stock index during 2013 through 2015 and persist in this trading for at least 300 sessions. They use data for 2012 to identify those who begin trading in 2013, and they use data for 2016-2017 to extend performance evaluations for at least two years of trading. They consider performance both gross and net of exchange and brokerage fees, but they ignore income taxes and expenses such as courses and trading platforms. They employ subsamples and regressions to measure learning while trading. Using trading records for the specified index futures for 19,646 individuals as described during 2012 through 2017, they find that: Keep Reading

Are WisdomTree Modern Alpha ETFs Attractive?

Is the WisdomTree approach to exchange-traded fund (ETF) cost efficiency and performance potential (Modern Alpha) attractive? To investigate, we compare performance statistics of six WisdomTree ETFs, all currently available, to those of “easy substitute” (widely used and very liquid) benchmark ETFs, as follows:

  1. WisdomTree U.S. Total Dividend Fund (DTD), with SPDR S&P 500 ETF Trust (SPY) as a benchmark.
  2. WisdomTree U.S. Earnings 500 Fund (EPS), with SPY as a benchmark.
  3. WisdomTree Europe Hedged Equity Fund (HEDJ), with Vanguard FTSE Europe Index Fund ETF Shares (VGK) as a benchmark.
  4. WisdomTree Yield Enhanced U.S. Aggregate Bond Fund (AGGY), with iShares Core U.S. Aggregate Bond ETF (AGG) as a benchmark.
  5. WisdomTree U.S. Multifactor Fund (USMF), with iShares Russell Mid-Cap ETF (IWR) as a benchmark.
  6. WisdomTree 90/60 U.S. Balanced Fund (NTSX), with 60%-40% SPY-iShares 7-10 Year Treasury Bond ETF (IEF) as a benchmark.

We focus on average return, standard deviation of returns, compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly dividend-adjusted returns for all specified ETFs since inceptions and for all benchmarks over matched sample periods through June 2020, we find that: Keep Reading

Realistic Expectations for Machine Learning for Asset Management

Will machine learning revolutionize asset management? In their January 2020 paper entitled “Can Machines ‘Learn’ Finance?”, Ronen Israel, Bryan Kelly and Tobias Moskowitz identify and discuss unique challenges in applying machine learning to asset return prediction, with the goal of setting realistic expectations for how much machine learning can improve asset management. Based on general characteristics of financial markets and machine learning algorithms, they conclude that: Keep Reading

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