Does the the utilities sector exhibit a useful lead-lag relationship with the broad stock market? In their January 2014 paper entitled “An Intermarket Approach to Beta Rotation: The Strategy, Signal and Power of Utilities”, Charles Bilello and Michael Gayed test a simple strategy that holds either the U.S. utilities sector or the broad U.S. stock market based on their past relative strength. Specifically, when utilities are relatively stronger (weaker) than the market based on total return over the last four weeks, hold utilities (the market) the following week. They call this strategy the Beta Rotation Strategy (BRS) because it seeks to rotate into utilities (the market) when the investing environment favors low-beta (high-beta) stocks. They perform both an ideal (frictionless) long-term test and a short-term net performance test using exchange-traded funds (ETF). Using weekly total returns for the Fama-French utilities sector and broad market since July 1926 and for the Utilities Select Sector SPDR (XLU) and Vanguard Total Stock Market (VTI) since July 2001, all through July 2013, they find that: Keep Reading
Investing Research Articles
Do active investment managers beat the market? In their January 2014 paper entitled “Active Manager Performance: Alpha and Persistence”, Frank Benham and Edmund Walsh assess the performance of active investment managers relative to appropriate benchmarks across asset classes over long periods. They consider six basic investment classes: core bonds; high-yield bonds; domestic large capitalization stocks; domestic small capitalization stocks; foreign large capitalization stocks; and, emerging markets stocks. They focus on whether investment managers beat benchmarks in the past and whether past outperformers become future outperformers. They take steps to avoid survivorship bias, selection bias and fund classification errors. Using a sample of 5,379 live and dead funds assembled from Morningstar Direct by filtering to avoid classification errors and to eliminate redundant funds run by the same manager from benchmark inceptions (ranging from January 1979 for domestic stocks to January 1988 for emerging markets stocks) through 2012, they find that: Keep Reading
April 15, 2014 - Economic Indicators
The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for March 2014. The actual total (core) inflation rate for March is slightly higher than (slightly higher than) forecasted.
The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.
April 15, 2014 - Investing Expertise
Do professional analysts systematically miss target prices for individual stocks? In the November 2013 draft of their paper entitled “Understanding and Predicting Target Price Valuation Errors”, Patricia Dechow and Haifeng You measure the errors in returns implied by professional stock analyst consensus price targets and examine the sources of these errors. They further investigate whether investors can anticipate and exploit consensus target price errors. They construct consensus target prices at the end of each month as the simple average of the most recent target price forecasts issued by following analysts within the last 90 days. Using analyst stock price targets, actual monthly returns and trading volumes, firm accounting data and institutional ownership data spanning April 1999 through December 2011 (227,127 firm-month observations), they find that: Keep Reading
April 14, 2014 - Calendar Effects
Does the seasonal change marked by the Easter holiday, with the U.S. stock market closed on the preceding Good Friday, tend to produce anomalous returns? To investigate, we analyze the historical behavior of the S&P 500 Index before and after the holiday. Using daily closing levels of the S&P 500 index for 1950-2013 (64 events), we find that: Keep Reading
April 11, 2014 - Weekly Summary
Below is a weekly summary of our research findings for 4/7/14 through 4/11/14. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.
April 11, 2014 - Investing Expertise
How do professional analysts value stocks? In their March 2014 paper entitled “Peering Inside the Analyst ‘Black Box’: How Do Equity Analysts Model Companies?”, Andreas Markou and Simon Taylor examine the private stock valuation models of a group of analysts working in research departments of large investment banks. They examine both modeling methods and inputs. Using 53 Excel-based valuation models from professional analysts covering the European healthcare and chemicals sectors acquired during the third quarter of 2009, they conclude that: Keep Reading
April 10, 2014 - Equity Premium
How big is the return premium associated with stock illiquidity? In his March 2014 paper entitled “The Pricing of the Illiquidity Factor’s Systematic Risk”, Yakov Amihud specifies and measures an illiquidity premium. He defines illiquidity as the average daily ratio of absolute return to dollar volume over the past three months. He specifies the illiquidity premium as the average four-factor (market, size, book-to-market, momentum) alpha on a set of hedge portfolios that are long (short) the stocks that are most (least) illiquid. Specifically, each month he:
- Sorts stocks on illiquidity and deletes the 1% with highest illiquidities as unreliable.
- Ranks surviving stocks on standard deviation of daily returns (volatility) over the last three months into three segments (terciles).
- To avoid confounding volatility and illiquidity, ranks stocks within each volatility tercile into illiquidity quintiles (creating 15 volatility-illiquidity portfolios). This step effectively controls for size, which relates negatively to volatility.
- Skips two months (avoiding reversal/momentum effects) and calculates value-weighted returns for the 15 portfolios during the third month after formation based on market capitalizations at the end of the prior month.
- Calculates the monthly illiquidity return as the average difference in returns between highest and lowest illiquidity portfolios across the three volatility groups.
- Calculates illiquidity alpha by controlling monthly illiquidity returns for market, size, book-to-market and momentum factors over the past 36 months.
Using daily and monthly data for all NYSE and AMEX common stocks and monthly factor returns during 1950 through 2012, he finds that: Keep Reading
What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:
- Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
- Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
- Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.
They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading
How well do long-short stock strategies work, after accounting for all costs? In their February 2014 paper entitled “Assessing the Cost of Accounting-Based Long-Short Trades: Should You Invest a Billion Dollars in an Academic Strategy?”, William Beaver, Maureen McNichols and Richard Price examine the net attractiveness of several long-short strategies as stand-alone investments (as for a hedge fund) and as diversifiers of the market portfolio. They also consider long-only versions of these strategies. Specifically, they consider five anomalies exposed by the extreme tenths (deciles) of stocks sorted by:
- Book-to-Market ratio (BM) measured annually.
- Operating Cash Flow (CF) measured annually as a percentage of average assets.
- Accruals (AC) measured annually as earnings minus cash flow as a percentage of average assets.
- Unexpected Earnings (UE) measured as year-over-year percentage change in quarterly earnings.
- Change in Net Operating Assets (ΔNOA) measured annually as a percentage of average assets.
For strategies other than UE, they reform strategy portfolios (long the “good” decile and short the “bad” decile) annually at the end of April using accounting data from the prior fiscal year. For UE, they reform the portfolio at the ends of March, June, September and December using prior-quarter data. They highlight cost of capital, financing costs and rebates received on short positions, downside risk and short-side contribution to performance. They assume that the same amount of capital supports either a long-only portfolio, or a portfolio with equal long and short sides (with the long side satisfying Federal Reserve Regulation T collateral requirements for the short side). They account for shorting costs as fees for initiating short positions plus an ongoing collateral rate set at least as high as the federal funds rate, offset by a rebate of 0.25% per year interest on short sale proceeds. They estimate stock trading costs as the stock-by-stock percentage bid-ask spread. They consider two samples (including delistings): (1) all U.S. listed stocks; and, (2) the 20% of stocks with the largest market capitalizations. Using accounting data as described above for all non-ADR firms listed on NYSE, AMEX and NASDAQ for fiscal years 1992 through 2011, and associated monthly stock returns during May 1993 through April 2013, they find that: Keep Reading