1. Chris Hughen and Jack Strauss Beijing Jiaotong University Jan. 2016

2.  Real Time Analysis over a 35 years (140 obs).  Uses Out-of-sample forecasts of ‘new’ sector fundamentals to choose sectors and past firm fundamentals to choose firms.  Examines Gross profits, Operating profit and EBITA as well as Cash-flow, EP, BM.  Assesses Portfolio of Sector, Firm and Sector/Firm over different periods to assess reliability/robustness of estimates  Results: Profitability measures above net income deliver consistent performance that substantially outperforms a random walk

3.  Long/short portfolio allocations using these fundamentals possessed alphas over 14% and increased Sharpe ratios by over 60%.  The portfolio strategies consistently beat a buy-and- hold benchmark two-thirds of the time over thirty- five years and over each of the last three decades.  A composite variable of profitability measures provided the highest payoff for firm allocations, while strategies using EBITDA were the most profitable for sector allocations  Why? Measures above net income predict future cash flow and represent high quality earnings.  Dichev, Graham, Harvey and Rajgopal (2013, 2015) show CFOs believe high quality earnings are:

4.  Dichev, Graham, Harvey and Rajgopal (2013, 2015) show CFOs believe high quality earnings are sustainable (persistent, recurring and repeatable) and possess predictive value with respect to future cash flows.  These accounting metrics are closer on the income statement to revenue (which is relatively stable), less likely to be manipulated, and less susceptible to non-reoccurring gains/losses.  Our study finds that profitability measures provide more sustainable measures of firm performance than net income or cash flow;  the persistence of these profitability measures implies they contain less transitory noise and easier to forecast than net income.  Results further document profitability metrics, such as gross profit and EBITDA, forecast cash flows better than bottom-line net income or even cash flows. Hence more likely to be related  to future equity returns.

5.  SP500 firms since 1975, OOS since 1980.  Always use one additional lag to allow for data release.  Novy-Marx (JFE, 2013) identify gross profits as an important factor in explaining the cross- section of returns.  Fama and French (JFE 2015) in their five factor model identify operating profits as an important factor.  Ball et al. (JFE, 2015) also show the importance of operating profits.  Loughran and Wellman (2011) show EBITDA/Enterprise Value – widely used by practitioners as significant

6.  None of these papers use a portfolio allocation approach in real time.  Data – SP500 since 1975 using the 10 GICS sectors. 57,000 observations  Select top quintile of sector forecasts based on AR model to long.  Select top quintile of firm fundamentals two quarters ago to long  Benchmark: A $100 investment 1980.1-2014.4 $7,017. Average quarterly return of 3.3% & Sharpe 0.59