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Value Investing Strategy (Strategy Overview)

Allocations for August 2022 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for August 2022 (Final)
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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.

Mutual Fund Performance Persistence

Do top-performing mutual funds reliably continue to be top performers. In their June 2012 semiannual report entitled “Does Past Performance Matter? S&P Persistence Scorecard”, Standard and Poor’s summarizes performance persistence statistics for U.S. mutual funds overall and for funds grouped by capitalization focus of holdings. They measure persistence of the top 25% (quartile) and top half of funds across multiple subsequent years and frequency of migration of all performance quartiles from one multi-year interval to the next. Using annual performance data for a broad sample of U.S. mutual funds during March 2002 through March 2012, they find that: Keep Reading

Dueling Consensus Forecasts of Economic Indicators

Which consensus forecast of U.S. economic indicators is best? How does the U.S. equity market react to consensus forecast errors? In their April 2012 paper entitled “Market Reaction to Information Shocks: Does the Bloomberg and Briefing.com Survey Matter?”, Linda Chen, George Jiang and Qin Wang investigate the accuracy of, and equity futures market reactions to, competing Bloomberg and Briefing.com survey-based forecasts for the values of scheduled weekly, biweekly, monthly and quarterly economic announcements. They focus on 14 announcements commonly treated as important: Building Permits, Capacity Utilization, Case-Shiller 20-city Index, Consumer Confidence, Consumer Price Index, Durable Goods Orders, Existing Home Sales, GDP Advance, Leading Indicators, Non-farm Payrolls, Personal Spending, Producer Price Index, Retail Sales and Unemployment Rate. They introduce standardization to compare errors across different indicator scales. Using consensus forecasts and announced values of 59 economic indicators, along with contemporaneous high-frequency price and volume data for the nearest S&P 500 futures contract (as available), over the period January 1998 through August 2010, they find that: Keep Reading

Best Stock Market Forecasters?

Where can investors find the best stock market forecasters: academia, banks, government? In the March 2012 draft of his paper entitled “On the Forecasting Quality of Professionals”, Aron Veress compares the stock market forecasting accuracies of different professional groups (academics, commercial bankers, investment bankers, government employees and non-financial professionals) who participate in the semi-annual Livingston Survey, both to each other and to quantitative predictors. He focuses on forecasts of the S&P Composite Index return at horizons of roughly one month and six months after publication of survey reports (early June/December of each year). He considers also a naive forecasting approach that invests in stocks or cash according to which has the higher preceding return. Using results from 120 semi-annual surveys and contemporaneous data for the S&P Composite Index and commonly used financial/economic U.S. stock market predictors, he finds that: Keep Reading

Verdict on Financial Markets Efficiency?

What do three prominent academic experts conclude when they review the body of evidence for and against the Efficient Markets Hypothesis (EMH), and therefore the potential benefit of speculation? In the April 2011 version of their paper entitled “Review of the Efficient Market Theory and Evidence”, Andrew Ang, William Goetzmann and Stephen Schaefer review the theoretical and empirical literature on EMH, with focus on implications for active investment management. They consider a range of markets and tests of both prices and investment managers, noting that EMH has evolved to consider the costs of collecting, analyzing and exploiting market information (trading frictions, financing costs, manager fees). Based on this literature review, they conclude that: Keep Reading

Pension Fund Real Estate Allocation, Cost and Performance

How do pension funds, arguably representative of sophisticated and conservative investors, use real estate as an alternative investment? In their January 2012 paper entitled “Value Added From Money Managers in Private Markets? An Examination of Pension Fund Investments in Real Estate”, Aleksandar Andonov, Piet Eichholtz and Nils Kok investigate the allocation, costs and performance of pension funds with respect to real estate investments. Using self-reported investment data for 884 U.S., Canadian, European and Australian/New Zealand pension funds during 1990 through 2009, they find that: Keep Reading

All-Americans: The Best Picks?

Do stock analysts elected to All-American (AA) status by institutional voters (via Institutional Investor magazine) reliably out-pick other analysts? In the December 2011 update of their paper entitled “Are Stars’ Opinions Worth More? The Relation Between Analyst Reputation and Recommendation Values”, Lily Fang and Ayako Yasuda examine the average performance of stock recommendations of AA analysts and other analysts as distinct groups. They further differentiate top-rank AAs (first and second place winners) from bottom-rank AAs (third-place and runners-up). They define “strong buy” and “buy” stock ratings as buy recommendations and “hold,” “sell” and “strong sell” ratings as sell recommendations. They focus on distinguishing skill from luck and information value from pure influence. They consider adjustments for five risk factors: market, size, book-to-market, momentum and technology sector. Competing portfolios hold recommended stocks for fixed intervals relative to public release dates with equal recommendation weighting and daily rebalancing. Using analyst recommendation data and associated stock returns for 1994 through 2009 (roughly 3,000 analysts and 20,000 stock recommendations per year), they find that: Keep Reading

University Endowment Performance: Strategic versus Tactical Allocation

Is strategic asset class allocation or active management paramount for U.S. university endowment investment performance? In the October 2011 draft of their paper entitled “Do (Some) University Endowments Earn Alpha?”, Brad Barber and Guojun Wang explore the investment performance of U.S. university endowments with regard to overall alpha, performance persistence and sources of superior performance. They assess three groups of universities: Ivy League; other elite universities based on high average math SAT entrance scores; and, the balance of universities. They measure alpha as the residual return (from specific asset selection and tactical asset class allocation) after accounting for the combined returns of best-fit constant (strategic) asset class allocations to five indexes representing U.S. stocks (S&P 500 Index), non-U.S. stocks (MSCI non-U.S.), U.S. bonds (Barclays Capital Aggregate Bond Index), hedge funds (Hedge Fund Research Fund‐Weighted Composite Index) and private equity (Cambridge Associates U.S. Private Equity Index). Using annual voluntarily reported university endowment investment returns, benchmark index returns and math SAT score statistics for incoming freshmen during 1991 through 2010 (279 endowments report in all 20 years), they find that: Keep Reading

SumZero Participant Trading Acumen

Do analysts who work for hedge funds make good calls? In their November 2011 paper entitled “Do Buy-side Recommendations Have Investment Value?”, Steven Crawford, Wesley Gray, Bryan Johnson and Richard Price III profile analysts employed by mutual funds, hedge funds and other investment firms and examine whether these experts make good trading recommendations. Using personal data and 2,135 long and short U.S. common stock investment propositions from over 1,100 participants in the SumZero community of buy-side investment professionals (mostly associated with hedge funds) during March 2008 through December 2010, and contemporaneous institutional holdings from SEC Form 13F filings, they find that: Keep Reading

Impact of Free, Unbiased Investing Advice

How do individual investors respond to an offer of free, unbiased investment advice? In their August 2010 paper entitled “Is Unbiased Financial Advice To Retail Investors Sufficient? Answers from a Large Field Study”, Utpal Bhattacharya, Andreas Hackethal, Simon Kaesler, Benjamin Loos and Steffen Meyer evaluate the responses of 8,195 randomly selected active and likely self-directed individual clients of a large European broker to an offer of free advice. This advice, unbiased in that it is free of monetary incentives for the broker, consists of personalized written and verbal guidance on mean-variance optimization of the client’s existing portfolio based on the client’s risk tolerance, wealth and investment horizon. The broker initiated the offer via email, with telephone follow-ups by an advisor to non-respondents. Using portfolio holder characteristics and daily portfolio holdings/price data from September 2005-May 2009 pre-offer, May 2009-October 2009 offer and post-offer measurement intervals (through March 2010), along with advised portfolio adjustments, they find that: Keep Reading

Does Accurate Forecasting Get Attention?

Do individual experts whose U.S. stock market forecasting records are good (bad) gain (lose) attention? The “pro” argument is that investors (and online intermediaries) eventually flock to good forecasters and ignore bad ones in search of a market timing edge. The “con” arguments are that loud noise (for example, marketing-related or entertainment-driven) swamps information, and/or investors do not or cannot measure forecaster accuracy, and/or investors are more interested in ideas than forecasts. As a simple test these arguments, we compare two data series: (1) the stock market forecasting accuracies of gurus in the Guru Grades summary table; and, (2) the attention paid to these same individuals as measured by the number of search results found by a Google query on (“[guru name]” “stock market”), with the “stock market” qualifier intended to filter out potential namesakes and connect each name to the forecasted variable. Using results from searches for 60 individually graded gurus on 7/20/11, we find that: Keep Reading

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