Investing Research Articles

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Composite Stock Market Valuation Model

Is there some better predictor of long-term stock market return than the widely cited cyclically adjusted price-earnings ratio (P/E10 or CAPE)? In the July 2014 version of his paper entitled “Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model”, Stephen Jones compares how well several fundamental and economic factors predict real long-term (10-year) equity market total return, with focus on Market Value/Gross Domestic Product (MV/GDP). He compares the predictive power of MV/GDP to those of P/E10 and Tobin’s q. He then constructs a multi-variable forecasting model that includes MV/GDP, a demographic metric and personal income-related variables. Using U.S. data since 1954 for different input variables, he finds that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for June 2014. The actual total (core) inflation rate for June is a little higher than (slightly lower than) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

Cyclical Behaviors of Size, Value and Momentum in UK

Do the behaviors of the most widely accepted stock market factors (size, book-to-market or value, and momentum) vary with the economic trend? In the June 2014 version of their paper entitled “Macroeconomic Determinants of Cyclical Variations in Value, Size and Momentum premium in the UK”, Golam Sarwar, Cesario Mateus and Natasa Todorovic examine differences in the sensitivities of UK equity market size, value and momentum factor returns (premiums) to changes in broad and specific economic variables. They define the broad economic state each month as upturn (downturn) when the OECD Composite Leading Indicator for the UK increases (decreases) that month. They also consider contributions of six specific variables to economic trend: GDP growth; unexpected inflation (change in CPI); interest rate (3-month UK Treasury bill yield); term spread (10-year UK Treasury bond yield minus 3-month UK Treasury bill yield); credit spread (Moody’s U.S. BBA yield minus 10-year UK government bond yield); and, money supply growth. They lag economic variables by one or two months to align their releases with stock market premium measurements. Using monthly UK size, value and momentum factors and economic data during July 1982 through December 2012, they find that: Keep Reading

Value-Momentum Switching Based on Value Premium Persistence

Can investors exploit monthly persistence in the value premium for U.S. stocks? In his February 2014 paper entitled “Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns”, Kevin Oversby investigates whether investors can exploit the fact that the Fama-French model high-minus-low (HML) value factor exhibits positive monthly autocorrelation (persistence). The HML factor derives from the difference in performance between portfolios of stocks with high and low book-to-market ratios. Prior published research indicates that the value premium concentrates in small firms, so he focuses on stocks with market capitalizations below the NYSE median. His test strategies each month invest in capitalization-weighted small value (small growth or small momentum) Fama-French portfolios when the prior-month sign of the HML factor is positive (negative). The strategies additionally retreat to a risk-free asset (such as U.S. Treasury bills) if the prior-month return for the test strategy is negative. Using HML factor values and monthly portfolio returns for small value, small growth and small momentum Fama-French portfolios, he finds that: Keep Reading

Weekly Summary of Research Findings: 7/14/14 – 7/18/14

Below is a weekly summary of our research findings for 7/14/14 through 7/18/14. 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.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Ultimate Stock-Pickers vs. Luck

Are Morningstar’s Ultimate Stock-Pickers good stock pickers? In his June 2014 paper entitled “Using Random Portfolios to Evaluate the Performance of the Ultimate Stock-Pickers Index”, Stefaan Pauwels compares the quarterly volatility-adjusted performances of the Morningstar Ultimate Stock-Pickers (USP) top buys, top holdings and top sells to those of many randomly generated (zero-skill) portfolios. Morningstar specifies USP members as fund managers across a range of equity styles with: (1) tenure longer than average within style category; and, (2) 1-year, 3-year, 5-year and 10-year returns exceeding that of the broad equity market. Each quarter, Morningstar generates lists of top ten USP buys, holdings and sells. The study compares the volatility-adjusted returns of these equally weighted lists to those of 1,000 equally weighted portfolios of ten stocks randomly selected each quarter from the S&P 500 Index. He performs volatility adjustment by dividing quarterly return by the standard deviation of daily returns during the quarter. Using quarterly USP lists from the end of November 2010 through early September 2013 and contemporaneous quarterly total returns and daily returns for associated stocks and the stocks in the S&P 500 Index, he finds that:

Keep Reading

Exploitation of Technical Analysis by Hedge Funds?

Do hedge fund managers who use technical analysis beat those who do not? In their May 2014 paper entitled “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry”, David Smith, Na Wang, Ying Wang and Edward Zychowicz examine the relative performance of users and non-users of technical analysis among hedge fund managers in different sentiment environments. They hypothesize that short-selling constraints prevent market correction of mispricings when sentiment is high (overly optimistic), but not when sentiment is low (overly pessimistic). Discovery of mispricings via technical analysis may therefore be more effective when sentiment is high. To test their hypothesis, they compare the performance of hedge funds that report using technical analysis to that of hedge funds that do not, with focus on the state of market sentiment. They define the market sentiment state as high or low depending on whether the monthly Baker-Wurgler market sentiment measure is above or below its full-sample median. Using end-of-period status on use/non-use of technical analysis and monthly returns for 3,290 live and 1,845 dead funds from the Lipper TASS hedge fund database and monthly market sentiment data during January 1994 through December 2010, they find that: Keep Reading

Accuracy of Robert Taylor’s Xyber9 Trend Forecasts

In February 2008, a reader requested evaluation of the market timing value of Xyber9 trend forecasts for the U.S. stock market, as developed and presented by Robert Taylor, CEO of Trend Corporation, Inc. Our conclusion then was that the claimed accuracy rate probably derives not from forecasting skill but from defining targets that are hard to miss. In June 2014 via email, Robert Taylor reported: “I was nominated for the Nobel Memorial Prize in Economics in March of 2000 for proving the financial markets are not random, but rather predictable. During the past 8 and a half years my U.S. Market forecasts…averaged better than 80% accuracy, including several years with an accuracy of over 90%, while the worse year produced over 70% accuracy.” He claims an aggregate forecast accuracy “greater than 83%”. He bases his forecasts on “Taylor’s Law”: “The financial market’s expansion and contraction is qualitatively in direct correlation to the increases and decreases in gravitational fluctuations experienced at the human level. The increases in market price are in direct response to decreases in gravitational forces; the decreases in market price are in direct response to the increases in gravitational forces.” He measures forecast accuracy as follows:

“The lowest price on the last day of a downtrend will be below the highest price recorded on the last day of the previous uptrend. The highest price on the last day of the uptrend will be above the lowest price recorded on the last day of the previous down trend.”

Robert Taylor observed that the accuracies reported in Guru Grades are rather low and inquired about a review of his forecasts, which for the U.S. market typically project short-term (a few days) trends in the S&P 500 Index and/or SPDR S&P 500 (SPY). Using daily highs and lows for the S&P 500 Index during March 2006 (when the Xyber9 historical forecasts commence) through June 2014, we find that: Keep Reading

High Growth in Operating Costs Bad for Stocks?

Does growth in a firm’s operating costs signal trouble for its stock? In their June 2014 preliminary paper entitled “Cost Growth and Stock Returns”, Dashan Huang, Fuwei Jiang, Jun Tu and Guofu Zhou examine the relationship between growth in operating costs and future stock returns. They measure operating cost growth as the annual percentage change in costs of goods sold plus selling, general and administrative expenses. They speculate that high cost growth warns of deteriorating profitability. Since analysts and investors focus on earnings and cash flows, they may not fully appreciate the import of cost growth. To ensure that cost growth data is available for public signaling, they relate stock return for July through June of year t+1 to accounting data as of the end of firm fiscal year t-1. Using accounting data from 1963 through 2012 and associated stock returns during July 1968 through 2013 for a broad sample of U.S. common stocks, they find that: Keep Reading

Stock Returns During and Between Earnings Seasons

Does intensity of firm quarterly earnings releases affect stock market behaviors? A reader proposed the following stock market timing strategy based on a strictly calendar-based definition of earnings season: go short (long) the market at the close at the end of the first full week (sixth full week) of each calendar quarter, representing the beginning (end) of earnings season. The hypothesis is that the broad stock market performs poorly during earnings season and well outside of earnings season. Using weekly closes for the S&P 500 Index since January 1950 and for the S&P 500 Implied Volatility Index (VIX) since January 1990, both through June 2014, we find that: Keep Reading

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