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The Sharpe Ratio: Blunted by Noise?

Many investors and analysts use the Sharpe ratio (mean excess return per unit of risk) as a field-leveling measure of investment performance. Does this variable reliably indicate the best portfolio? In his brief January 2007 summary paper entitled “Beware the Sharpe Ratio”, Steve Christie applies the Generalized Method of Moments to test the portfolio discrimination power of the Sharpe ratio. Using two monthly data sets spanning 24 years for a set of multi-asset class portfolios created from index series and 18 years for a large group of mutual funds, he concludes that: Keep Reading

Quantifying and Exploiting Long (Bull and Bear) Trends

Attempting to follow long stock market trends is a common investment approach, with much guru attention focused on calling long-term tops and bottoms. Is this approach meaningful for investors as an avenue to improve upon buy-and-hold performance? In the December 2006 version of his paper entitled “Analyzing Regime Switching in Stock Returns: An Investment Perspective”, Jun Tu investigates the potential importance to investors of exploiting differences between bull and bear markets within a Bayesian framework that accommodates considerable uncertainty. Using monthly value-weighted stock return and volatility data for July 1963 to February 2006 (512 observations), he finds that: Keep Reading

More Information is Better?

Is more investment information always better? Are there unintended consequences for individual investors/traders acquiring investment information? Specifically, do individual investors/traders systematically acquire information to support rational future decision-making, or do they focus on information that confirms (and builds overconfidence in) decisions already made? The following two recent studies examine these questions, with results as follows: Keep Reading

Screening for Fear When Portfolio Building

Implied idiosyncratic volatility is the “investor fear gauge” or perceived risk for an individual stock based on the pricing of its associated options, as contrasted with: (1) overall stock market volatility as measured by variables such as the CBOE Volatility Index (VIX); and, (2) realized idiosyncratic volatility based on variation of the stock’s historical price. Can investors use the return due this perceived risk in an individual stock as a building block in constructing outperforming portfolios? In their December 2006 paper entitled “Idiosyncratic Implied Volatility and the Cross-Section of Stock Returns”, Dean Diavatopoulos, James Doran and David Peterson examine the relationship between idiosyncratic implied volatility and 30-day, 60-day and 91-day future returns for different kinds of equities. Using daily data on 240 stocks with actively traded options for the period January 1996 to June 2005, they find that: Keep Reading

The Professor’s Forecast for the Indefinite Future…

…looks something like this: Keep Reading

Can Real Estate Experts Provide Reliable Advice for Commercial Property Investing?

While we normally do not stray far from the stock market, an article on the forecasting ability of commercial real estate gurus relates strongly to our ongoing investigation of investing expertise. Do these experts add value for investors in the relatively illiquid real estate market? In his 2005 paper entitled “A Random Walk Down Main Street: Can Experts Predict Returns on Commercial Real Estate?” David Ling examines the ability of institutional owners and managers to predict commercial real estate investment performance for various property types across major metropolitan markets. Using (1) predicted relative property returns (desirability rankings) for nine property types and 16 metropolitan markets from the Real Estate Research Corporation’s (RERC) quarterly Real Estate Investment Survey over a thirteen-year period and (2) corresponding National Council of Real Estate Investment Fiduciaries (NCREIF) Property Index returns, he concludes that: Keep Reading

The (Dynamic) Meanings of Buy, Hold and Sell

Do broker stock recommendations predict future returns? Are analysts at independent brokers more accurate than those associated with investment banking firms? Did the changes in research rules after the Internet bubble affect analyst behavior? In his November 2006 paper entitled “Do Affiliated Analysts Mean What They Say?”, Michael Cliff compares the performance of stock recommendations made by analysts employed by lead underwriters to that of recommendations made by analysts working at independent brokers. Using data for the period 1994-2005 (13,794 recommendations from lead underwriters and 10,216 from independent brokers), he finds that: Keep Reading

A Contrarian Play on Small Profitability Laggers?

Why do small capitalization stocks as a group tend to outperform the broad market? Do small firms represent relatively high risk of financial distress (with attendant reward), or are they victims of systematic investor overreaction to past poor performance? In the 2006 update of their paper entitled “Can Overreaction Explain Part of the Size Premium?” Ozgur Demirtas and Burak Güner investigate irregularities in the historical returns of small capitalization stocks to identify the source of the size effect. Using returns and financial data for NYSE/Amex/Nasdaq stocks over the period July 1971 through June 2001, they conclude that: Keep Reading

Bet Against Big Sympathy Moves?

Are investor actions well-calibrated when they punish or reward the stocks of all the companies in an industry based on the earliest earnings announcements among peer companies? In the December 2006 version of their paper entitled “Overreaction to Intra-Industry Information Transfers?”, Jacob Thomas and Frank Zhang test the efficiency of intra-industry information transfers by measuring whether the price responses of non-announcing firms to earlier peer group earnings announcements systematically relate to subsequent price responses when these same companies announce their own earnings a few days later. Using a sample of earnings announcement dates, stock returns and firm financial variables spanning 132 quarters over 1973-2005 (245,742 firm-quarter observations), they conclude that: Keep Reading

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals (Chapter-by-Chapter Review)

In his 2007 book Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals, David Aronson opens with two contentions: (1) “much of the wisdom comprising the popular version of TA does not qualify as legitimate knowledge;” and, (2) “TA must evolve into a rigorous observational science if it is to deliver on its claims and remain relevant.” Taken in parts, this book offers sound methods for analysis. Taken as an integrating whole, it offers insightful context for evaluating a broad range of financial analyses/claims presented by others. Here is a chapter-by-chapter review of some of the insights in this book: Keep Reading

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