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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.

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Ask for Advisor’s Personal Investing Performance?

Are financial advisors expert guides for their client investors? In their December 2017 paper entitled “The Misguided Beliefs of Financial Advisors”, Juhani Linnainmaa, Brian Melzer and Alessandro Previtero compare investing practices/results of Canadian financial advisors to those of their clients, including trading patterns, fees and returns. They estimate account alphas via multi-factor models. Using detailed data from two large Canadian mutual fund dealers (accounting for about 5% of their sector) for 3,276 Canadian financial advisors and their 488,263 clients, and returns and fees for 3,023 associated mutual funds, during January 1999 through December 2013, they find that:

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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 economic experts the 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 survey report for the fourth quarter of 2017 is November 13, 2017, with forecasts for the first quarter of 2018 through the fourth quarter of 2018. The “Anxious Index” is the probability of recession during the next quarter. When professional forecasters are relatively optimistic (pessimistic) about the economy, does the stock market go up (down) over the coming quarters? 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 the forecasts are accurate, a relatively high (low) forecasted probability of good times should arguably indicate a relatively strong (weak) stock market. Using survey results and quarterly S&P 500 Index levels ( measured from survey release date to survey release date as available, and from mid-quarter to mid-quarter before availability of release dates) from the fourth quarter of 1968 through the fourth quarter of 2017 (197 surveys), we find that:

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Chess, Jeopardy, Poker, Go and… Investing?

How can machine investors beat humans? In the introductory chapter of his January 2018 book entitled “Financial Machine Learning as a Distinct Subject”, Marcos Lopez de Prado prescribes success factors for machine learning as applied to finance. He intends that the book: (1) bridge the divide between academia and industry by sharing experience-based knowledge in a rigorous manner; (2) promote a role for finance that suppresses guessing and gambling; and, (3) unravel the complexities of using machine learning in finance. He intends that investment professionals with a strong machine learning background apply the knowledge to modernize finance and deliver actual value to investors. Based on 20 years of experience, including management of several multi-billion dollar funds for institutional investors using machine learning algorithms, he concludes that: Keep Reading

10 Steps to Becoming a Better Quant

Want your machine to excel in investing? In his January 2018 paper entitled “The 10 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado examines common errors made by machine learning experts when tackling financial data and proposes correctives. Based on more than two decades of experience, he concludes that: Keep Reading

Seven Habits of Highly Ineffective Quants

Why don’t machines rule the financial world? In his September 2017 presentation entitled “The 7 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado explores causes of the high failure rate of quantitative finance firms, particularly those employing machine learning. He then outlines fixes for those failure modes. Based on more than two decades of experience, he concludes that: Keep Reading

Financial Analysts 25% Optimistic?

How accurate are consensus firm earnings forecasts worldwide at a 12-month horizon? In his May 2016 paper entitled “An Empirical Study of Financial Analysts Earnings Forecast Accuracy”, Andrew Stotz measures accuracy of consensus 12-month earnings forecasts by financial analysts for the companies they cover around the world. He defines consensus as the average for analysts coverings a specific stock. He prepares data by starting with all stocks listed in all equity markets and sequentially discarding:

  1. Stocks with market capitalizations less than $50 million (U.S. dollars) as of December 2014 or the last day traded before delisting during the sample period.
  2. Stocks with no analyst coverage.
  3. Stocks without at least one target price and recommendation.
  4. The 2.1% of stocks with extremely small earnings, which may results in extremely large percentage errors.
  5. All observations of errors outside ±500% as outliers.
  6. Stocks without at least three analysts, one target price and one recommendation.

He focuses on scaled forecast error (SFE), 12-month consensus forecasted earnings minus actual earnings, divided by absolute value of actual earnings, as the key accuracy metric. Using monthly analyst earnings forecasts and subsequent actual earnings for all listed firms around the world during January 2003 through December 2014, he finds that: Keep Reading

Guru Re-grades

What happens to the rankings of Guru Grades after weighting each forecast by forecast horizon and specificity? In their March 2017 paper entitled “Evaluation and Ranking of Market Forecasters”, David Bailey, Jonathan Borwein, Amir Salehipour and Marcos Lopez de Prado re-evaluate and re-rank market forecasters covered in Guru Grades after weighting each forecast by these two parameters. They employ original Guru Grades forecast data as the sample of forecasts, including assessments of the accuracy of each forecast. However, rather than weighting each forecast equally, they:

  • Apply to each forecast a weight of 0.25, 0.50, 0.75 or 1.00 according to whether the forecast horizon is less than a month/indeterminate, 1-3 months, 3-9 months or greater than 9 months, respectively.
  • Apply to each forecast a weight of either 0.5 for less specificity or 1.0 for more specificity.

Using a sample of 6,627 U.S. stock market forecasts by 68 forecasters from CXO Advisory Group LLC, they find that: Keep Reading

How Large University Endowments Allocate Investments

How are the asset allocations of the largest university endowments, conventionally accepted as among the best investors, evolving? In their December 2016 paper entitled “The Evolution of Asset Classes: Lessons from University Endowments”, John Mulvey and Margaret Holen summarize recent public reports from large U.S. university endowments, focusing on asset category definitions and allocations. Using public disclosures of 50 large university endowments for 2015, they find that: Keep Reading

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, currently Wednesdays. Responses can vary widely [200% Leveraged Short; 100% Fully Short; 0% (100% Cash or Hedged to Market Neutral); 100% Fully Invested; 200% Leveraged Long]. Responses are tallied and averaged to provide the average long (or short) position or all NAAIM managers, as a group [NAAIM Exposure Index].” Using historical weekly survey data and weekly Wednesday-to-Wednesday dividend-adjusted returns for SPDR S&P 500 (SPY) over the period July 2006 through mid-March 2017 (550 surveys), we find that: Keep Reading

The Value of Fund Manager Discretion?

Are there material average performance differences between hedge funds that emphasize systematic rules/algorithms for portfolio construction versus those that do not? In their December 2016 paper entitled “Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance”, Campbell Harvey, Sandy Rattray, Andrew Sinclair and Otto Van Hemert compare average performances of systematic and discretionary hedge funds for the two largest fund styles covered by Hedge Fund Research: Equity Hedge (6,955 funds) and Macro (2,182 funds). They designate a fund as systematic if its description contains “algorithm”, “approx”, “computer”, “model”, “statistical” and/or “system”. They designate a fund as discretionary if its description contains none of these terms. They focus on net fund alphas, meaning after-fee returns in excess of the risk-free rate, adjusted for exposures to three kinds of risk factors well known at the start of the sample period: (1) traditional equity market, bond market and credit factors; (2) dynamic stock size, stock value,  stock momentum and currency carry factors; and, (3) a volatility factor specified as monthly returns from buying one-month, at‐the‐money S&P 500 Index calls and puts and holding to expiration. Using monthly after-fee returns for the specified hedge funds (excluding backfilled returns but including dead fund returns) during June 1996 through December 2014, they find that: Keep Reading

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