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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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Online, Real-time Test of AI Stock Picking?

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks to provide investment results that exceed broad U.S. Equity benchmark indices at equivalent levels of volatility.” More specifically, offeror EquBot: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model ranks each company based on the probability of the company benefiting from current economic conditions, trends, and world events and identifies approximately 30 to 70 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, while maintaining volatility…comparable to the broader U.S. equity market. The Fund may invest in the securities of companies of any market capitalization. The EquBot model recommends a weight for each company based on its potential for appreciation and correlation to the other companies in the Fund’s portfolio. The EquBot model limits the weight of any individual company to 10%.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through April 30, 2018, we find that: Keep Reading

AAII Stock Screens

A reader asked: “The American Association of Individual Investors (AAII) has a lot of strategies they have been paper-trading over many years at Stock Screens. It seems like every strategy builds upon a well-known investing book or otherwise publicized strategy from the last 40 years. Have you ever done an evaluation of those performance results?” According to AAII: “These approaches run the full spectrum, from those that are value-based to those that focus primarily on growth. Some approaches are geared toward large-company stocks, while others uncover micro-sized firms. Most fall somewhere in the middle.” AAII provides performance histories, risk-return statistics and characteristics for all screens. AAII cautions that: “The impact of factors such as commissions, bid-ask spreads, cash dividends, time-slippage (time between the initial decision to buy a stock and the actual purchase) and taxes is not considered. This overstates the reported performance…” Using monthly returns and turnovers for the equally weighted portfolios generated by the 60 screens presented during January 1998 through March 2018 (243 months), along with contemporaneous returns for SPDR S&P 500 (SPY), Vanguard Small Cap Index Fund (NAESX) and Vanguard Total Stock Market Index Fund (VTSMX), we find that: Keep Reading

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

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