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The Journal of Portfolio Management

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Can We Count on Accounting Fundamentals for Industry Portfolio Allocation?

Justin Lallemand and Jack Strauss
The Journal of Portfolio Management Summer 2016, 42 (4) 70-87; DOI: https://doi.org/10.3905/jpm.2016.42.4.070
Justin Lallemand
is an assistant professor of finance at the University of Denver in Denver, CO.
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  • For correspondence: justin.lallemand@du.edu
Jack Strauss
is the Miller Endowed Chair of Applied Economics at the University of Denver in Denver, CO.
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  • For correspondence: jack.strauss@du.edu
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Abstract

The authors examine out-of-sample industry excess return predictability and portfolio allocation using forecasting combination methods of industry-level and aggregate accruals, book-to-market, earnings, investments, and gross profits. Out-of-sample combination forecasts generate significant industry return predictability. Substantial increases in Sharpe ratios and utility gains demonstrate that predictability is not driven primarily by higher risk. Real-time portfolio allocation strategies rotate into long positions in industries with high expected returns and short industries with low expected returns. Over the past thirty years, outof-sample combination forecasts of accounting variables have generated value-weighted industry portfolio payoffs five times greater than a buy-and-hold benchmark. The constructed portfolios consistently beat a buy-and-hold benchmark portfolio two-to-one while generating alphas that exceed 10%.

TOPICS: Portfolio theory, derivatives

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The Journal of Portfolio Management: 42 (4)
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Can We Count on Accounting Fundamentals for Industry Portfolio Allocation?
Justin Lallemand, Jack Strauss
The Journal of Portfolio Management May 2016, 42 (4) 70-87; DOI: 10.3905/jpm.2016.42.4.070

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Can We Count on Accounting Fundamentals for Industry Portfolio Allocation?
Justin Lallemand, Jack Strauss
The Journal of Portfolio Management May 2016, 42 (4) 70-87; DOI: 10.3905/jpm.2016.42.4.070
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