Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2021 (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.

Researcher Motives

Do motives of financial market researchers justify strong skepticism of their findings? In his brief August 2021 paper entitled “Be Skeptical of Asset Management Research”, Campbell Harvey argues that economic incentives undermine belief in findings of both academic and practitioner financial market researchers. Based on his 35 years as an academic, advisor to asset management companies and editor of a top finance journal, he concludes that: Keep Reading

Should the “Anxious Index” Make Investors Anxious?

Since 1990, the Federal Reserve Bank of Philadelphia has conducted a quarterly Survey of Professional Forecasters. The American Statistical Association and the National Bureau of Economic Research conducted the survey from 1968-1989. Among other things, the survey solicits from experts probabilities of U.S. economic recession (negative GDP growth) during each of the next four quarters. The survey report release schedule is mid-quarter. For example, the release date of the third quarter 2021 report is August 13, 2021, with forecasts through the third quarter of 2022. The “Anxious Index” is the probability of recession during the next quarter. Are these forecasts meaningful for future U.S. stock market returns? Rather than relate the probability of recession to stock market returns, we instead relate one minus the probability of recession (the probability of good times). If forecasts are accurate, a relatively high (low) forecasted probability of good times should indicate a relatively strong (weak) stock market. Using survey results and quarterly S&P 500 Index levels (on survey release dates as available, and mid-quarter before availability of release dates) from the fourth quarter of 1968 through the third quarter of 2021 (212 surveys), we find that:

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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, July 31, 2013 and June 30, 2014. 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). 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 2010 through July 2021, we 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 90%-10% 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 July 2021, we find that: Keep Reading

Performance of Derivatives Traders

How well do derivatives traders perform, and why? In the July 2021 version of their paper entitled “Derivatives Leverage is a Double-Edged Sword”, Avanidhar Subrahmanyam, Ke Tang, Jingyuan Wang and Xuewei Yang study the performance of Chinese derivatives (futures) traders across 1,086 contracts on 51 underlying assets. They consider gross and net daily trader returns, turnover and degree of leverage implied by contracts held. They further investigate sources of profits/losses for these traders. To identify clearly skilled (unskilled) traders, they identify those in the top (bottom) 5% of Sharpe ratios who trade on at least 24 days during the first year of the sample period and isolate those with statistically extreme performance. They then analyze trading behaviors and results for these extreme performers the next two years. Using data from a major futures broker in China, including transaction histories, end-of-day holdings and account flows (injections and withdrawals) for 10,822 traders (315 institutional) during January 2014 through December 2016, they find that:

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Financial Markets Flouters of Statistical Principles

Should practitioners and academics doing research on financial markets be especially careful (compared to researchers in other fields) when employing statistical inference. In the July 2021 version of their paper entitled “Finance is Not Excused: Why Finance Should Not Flout Basic Principles of Statistics”, David Bailey and Marcos Lopez de Prado argue that three aspects of financial research make it particularly prone to false discoveries:

  1. Due to intense competition, the probability of finding a truly profitable investment strategy is very low.
  2. True findings are often short-lived due to financial market evolution/adaptation.
  3. It is impossible to verify statistical findings through controlled experiments.

Based on statistical analysis principles and their experience in performing and reviewing financial markets research, they conclude that: Keep Reading

Performance of Statewide Pension Funds

When a public pension fund reports beating its benchmark, does that signify a job well done? In his July 2021 paper entitled “Cost, Performance, and Benchmark Bias of Public Pension Funds in the United States: An Unflattering Portrait”, Richard Ennis analyzes net returns of statewide pension funds in the U.S. He calculates both (1) net Sharpe ratio and (2) return versus a matched benchmark constructed via regression as the best-fit combination of the Russell 3000 stock index, the MSCI ACWI ex-U.S. stock index and the Bloomberg Barclays US Aggregate bond index. He compares performance relative to this benchmark and estimated costs for each fund. He then assesses performance of the funds versus the benchmarks they themselves construct and report against. Using self-reported data for a sample of 24 such funds self-reported via the Public Plans Data website during July 2010 through June 2020, he finds that: Keep Reading

Predicting Stock Market Crashes with Interpretable Machine Learning

Can machine learning-generated stock market crash predictions be amenable to human interpretation? In their June 2021 paper entitled “Explainable AI (XAI) Models Applied to Planning in Financial Markets”, Eric Benhamou, Jean-Jacques Ohana, David Saltiel and Beatrice Guez apply a gradient boosting decision tree (GBDT) to 150 technical, fundamental and macroeconomic inputs to generate daily predictions of short-term S&P 500 Index crashes. They define a crash as a 15-day S&P 500 Index return below its historical fifth percentile within the training dataset. The 150 model inputs encompass:

  1. Risk aversion metrics such as asset class implied volatilities and credit spreads.
  2. Price indicators such as returns, major stock index Sharpe ratios, distance from a long-term moving average and and equity-bond correlations.
  3. Financial metrics such as 12-month sales growth and price-to-earnings ratio forecasts.
  4. Macroeconomic indicators such Citigroup regional and global economic surprise indexes.
  5. Technical indicators such as market breath and index put-call ratio.
  6. Interest rates such as 10-year and 2-year U.S. Treasury yields and break-even inflation level.

They first rank and filter the 150 inputs based on GBDT to discard about two thirds of the variables. They then apply the Shapley value solution concept to identify the most important of the remaining variables and thereby support interpretation of methodology outputs. Using daily values of the 150 model inputs and daily S&P 500 Index roll-adjusted futures prices from the beginning of January 2003 through mid-January 2021 (with data up to January 2019 used for training, the next year for validation and the rest for testing), they find that:

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What Kind of Index Option Traders and Trades Are Profitable?

Overall, how do retail option traders perform compared to institutional counterparts, and what accounts for any performance difference? In their June 2021 paper entitled “Who Profits From Trading Options?”, Jianfeng Hu, Antonia Kirilova, Seongkyu Park and Doojin Ryu use account-level transaction data to examine trading styles and profitability by investor category for KOSPI 200 index options and futures. There are no restrictions in Korean derivatives markets on retail investor participation, and retail participation is high. Using anonymized account-level (153,835 domestic retail, 5,904 domestic institutional, 667 foreign institutional and 604 foreign retail) data for all KOSPI 200 index options and futures trades during January 2010 through June 2014, they find that:

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Fixing Institutional Investing?

Why have U.S. public pension, endowment and other non-profit funds (institutional investors) consistently underperformed simple, investible passive benchmarks since 2008? How should they remedy that underperformance? In his April 2021 paper entitled “How to Improve Institutional Fund Performance”, Richard Ennis summarizes prior papers quantifying post-2008 institutional investor returns and recommends how institutions can improve this performance. Extending performance estimates from prior analyses through June 2020, he finds that: Keep Reading

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