Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for November 2023 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for November 2023 (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.

The Most Intriguing Gurus?

Which stock market experts intrigue investors and traders the most? For insight, we examine CXOadvisory.com log files for visits derived from web search engines based on search phrases associated with specific experts. We consider the top 50 search phrases for each of the last three years and consolidate similar searches (e.g., “jim jubak” and “jubak” or “ken fisher” and “fisher investments”). We also normalize results for each year by expressing relative interest in experts by dividing the number of searches for each by the total number of searches for all experts. Using the top 50 search phases arriving at CXOadvisory.com for each of 2007, 2008 and 2009 (to date), we find that: Keep Reading

Performance Trend for Value Line’s Timeliness Ranking

A reader observed and suggested:

“When I first started paying attention to markets in the 1980s and 1990s, one frequently cited argument against market efficiency was the Value Line anomaly – the fact that stocks with their best timeliness ranking had extraordinary returns over a long period. You can still find charts showing how well Group 1 has done versus Group 5 over a multi-decade period, but it seems that there has not been much cumulative performance separation among groups in recent years. Some raw data on their site shows that the predictive power of the ranking system seems to be missing from about 2000 onward. It might be interesting to look at what was once a widely discussed method of potential market outperformance.”

The Value Line Timeliness Ranking System sorts stocks into five groups, with Group 1 (5) expected to exhibit the strongest (weakest) future performance. Value Line summarizes annual performance data for Groups 1 through 5 based on assumptions of both weekly and annual group re-sorting. Because the trading frictions of weekly re-sorting are likely high and difficult to estimate, we focus on performance by group for annual re-sorting. Specifically, we measure the Group 1 annual returns minus the Group 5 annual returns and the Group 2 annual returns minus the Group 4 annual returns. If the ranking system is persistently reliable, both sets of differences should be persistently positive, with the differences for the first set generally larger than those for the second set. Using annual return data stated by Value Line for 1965 (partial year) through 2008 (nearly 44 years), we find that: Keep Reading

Guru Stock Market Forecasting Accuracy Over Time

A reader inquired whether the average accuracy rate for U.S. stock market forecasts at Guru Grades has been stable over time. The average accuracy rate is a cumulative (inception-to-date) calculation. To test its stability, we calculate the inception-to-date, equally-weighted average guru accuracy rates as of October 1 for each of the past four years (with 2008 not yet fully graded). Over this time, the database has expanded, with some gurus lapsing to inactivity and others being added, so the mix of active forecasters changes over time. Using all currently collected and graded forecasts, we find that: Keep Reading

Best Ideas of Mutual Fund Managers

How many stocks within an equity fund manager’s portfolio represent truly “passionate” (high-conviction) picks? Do passionate picks outperform the diversifying “fillers” in the portfolio, and the market in general? In the March 2009 version of their paper entitled “Best Ideas”, Randy Cohen, Christopher Polk, and Bernhard Silli attempt to identify which holdings in equity mutual fund portfolios represent the high-conviction “Best Ideas” of the fund managers and then measure the performance those stocks after the conviction becomes apparent. They identify high-conviction holdings via several measures that indicate unusually high commitment (tilt) of funds to specific stocks, with the “Best Idea” in a portfolio being the stock with the highest tilt. Using monthly stock returns and quarterly fund holdings data for U.S. equity mutual funds over the period 1991-2005, they conclude that: Keep Reading

Critically Delegating, or Fearfully Abrogating?

Do individuals tend to think critically about financial advisor recommendations, or blindly follow them? In the March 2009 article entitled “Expert Financial Advice Neurobiologically ‘Offloads’ Financial Decision-Making under Risk”, Jan Engelmann, Monica Capra, Charles Noussair and Gregory Berns investigate the neurobiological basis of the influence of expert advice on financial decisions via functional Magnetic Resonance Imaging monitoring of individuals choosing between a certain payment and a lottery, with and without expert advice. Using test results for 24 individuals (mostly female and mostly undergraduate students), they conclude that: Keep Reading

Mutual Fund Stock Selection vs. Market Timing

Can investors assess the performance of an active fund manager without access to the fund’s detailed trading records (especially trades not evident from quarterly holdings reports)? In the February 2009 update of his paper entitled “Active Alpha and Active Beta – Detecting the Unobserved Actions of Portfolio Managers”, Anders Ekholm presents a new methodology for indirectly measuring the effects of a fund manager’s trading that relies exclusively on portfolio returns. His approach decomposes fund tracking error into two aspects of active management: stock selection (idiosyncratic risk, or active alpha) and general market timing (systemic risk, or active beta). Applying this methodology to daily returns for a sample of actively managed U.S. equity mutual funds over the period 12/31/99-3/31/08, he finds that: Keep Reading

Morningstar Ratings and Future Returns

Does the Morningstar mutual fund rating system work? If so, how? In their March 2009 paper entitled “Selectivity, Market Timing and the Morningstar Star-Rating System”, Antonios Antypas, Guglielmo Caporale, Nikolaos Kourogenis and Nikitas Pittis investigate whether Morningstar mutual fund ratings enable investors to select funds that are likely to outperform in the future. Using data for 1,511 rated equity mutual funds since January 1998, they conclude that: Keep Reading

Converging Guru Accuracies

Do stock market gurus tend to anchor on bullish or bearish outlooks, regardless of market trends? If so, the distribution of their stock market forecasting accuracies should diverge when the market persists in one mode over a long period and converge when the market changes modes. The results at Guru Grades offer a limited way to test these hypotheses. Forecasts from the 2003-2007 bullish period still dominate the samples. If the gurus are mostly anchored on their outlooks, the 2008-2009 bearish period should be compressing the previously spreading distribution of accuracies by raising the grades of stuck bears and lowering the grades of stuck bulls. Based on trends in the Guru Grades accuracy rates over the past five bearish months, we find that: Keep Reading

The Advised, the Non-advised and Frequent Traders

How do financial advisors affect the investing practices of individual investors? Does their advice decisively improve client performance, or are other factors more explanatory? In their February 2009 paper entitled “The Influence of Financial Advisors on Household Portfolios: A Study on Private Investors Switching to Financial Advice”, Ralf Gerhardt and Andreas Hackethal compare the portfolios and transactions of advised and non-advised German investors to determine the effects of advice. They further decompose the sample of investors to explore whether differences between advised and non-advised arise from the advice per se or from investor socio-demographics or trading frequency. Using portfolio compositions and transactions for over 65,000 German investors during February 2006 through July 2007, including 597 who initiated a relationship with a financial advisor during that period, they conclude that: Keep Reading

Usefulness of Non-U.S. Analyst Stock Recommendations and Earnings Forecasts

Are stock recommendations and earnings forecasts from analysts in markets outside the U.S. useful to investors? In their February 2009 paper entitled “International Evidence on Analyst Stock Recommendations, Valuations, and Returns”, Ran Barniv, Ole-Kristian Hope, Mark Myring and Wayne Thomas examine the usefulness of non-U.S. analyst outputs by testing relationships between: (1) valuation estimates and stock recommendations; (2) valuation estimates and future excess returns; and, (3) stock recommendations and future stock returns. They segment results according to level of investor legal protection within the analyst’s country, as indicated by assessments of rule of law, judicial system efficiency and corruption. Using earnings forecasts, stock recommendations and monthly stock return data for 30 countries over the period January 1993 to May 2007, they conclude that: Keep Reading

Daily Email Updates
Filter Research
  • Research Categories (select one or more)