Can We Count on Accounting Fundamentals for Industry Portfolio Allocation?
研究了利用行业层面和总体的会计变量（如应计利润、账面市值比、盈利等）的组合预测来预测行业超额收益，发现这些预测能显著提升夏普比率和效用，并构建出超越买入持有基准两倍以上的投资组合。
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%. <b>TOPICS:</b>Portfolio theory, derivatives