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In this research, I show that aggregate information from financial statement analysis helps in predicting real economic development. Further, I show that using the top 100 U.S. public companies, ranked by market capitalisation, represents a convenient method to proxy for the entire portfolio of traded companies. I then show that aggregate accounting information of the same 100 biggest companies has predictive information for next quarter real Gross Domestic Product (GDP) growth, after controlling for the traditional stock market returns, and explains a portion of professional macro forecasters’ revisions and errors. Konchitchki and Patatoukas (2014a) provide an intuitive framework for these findings. Yet, I contribute by finding that aggregate accounting drivers from the Alternative Breakdown provide greater predictive power when compared to DuPont and that introducing financial and nonfinancial data split reduces heterogeneity. Another contribution of mine is introducing out-of-sample analysis. Although, I find that current methods used by professional macro forecasters exhibit slightly lower root-mean-square error (RMSE), I only use annual stock market returns and aggregate accounting profitability drivers in my model

This paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The paper is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focuses on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process. JEL Classification: C53, E37

In this study, we hypothesize and find that financial statement analysis of firm profitability drivers applied at the aggregate level yields timely insights that are relevant for forecasting real economic activity. We first show that focusing on the 100 largest firms offers a cost-effective way to extract information embedded in accounting profitability data of the entire stock market portfolio. We then show that accounting profitability data aggregated across the 100 largest firms have predictive content for subsequent real Gross Domestic Product (GDP) growth. We also show that stock market returns have predictive content for future real GDP growth, while their predictive power varies with the length of the measurement window with annual stock market returns being the most powerful. Importantly, we find that the predictive content of our indices of aggregate accounting profitability drivers is incremental to that of annual stock market returns. An in-depth investigation of consensus survey forecasts shows that professional macro forecasters revise their expectations of real economic activity in the direction of the predictive content of aggregate accounting profitability drivers and stock market returns. Although macro forecasters are fully attuned to stock market return data, their forecasts of real GDP growth can be improved in a statistically and economically significant way using our indices of aggregate accounting profitability drivers. Our findings suggest that professional macro forecasters and stock market investors do not fully impound the predictive content of aggregate accounting profitability drivers when forecasting real economic activity. In additional analysis, we examine the association between stock market returns and the portion of subsequent real GDP growth that is predictable based on our indices of aggregate accounting profitability drivers but that is not anticipated by stock market investors. We find that this portion is positively related to stock market returns, suggesting that the macro predictive content of aggregate accounting profitability drivers is relevant for stock valuation. Overall, our study brings financial statement analysis to the forefront as an incrementally useful tool for gauging the prospects of the real economy that should be of interest to academics and practitioners. JEL Classification: E01; E32; E60; M41. Data Availability: Data are available from public sources indicated in the text.

This paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The paper is the result of a collaborative effort between the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not focus exclusively on point forecast performance. It also examines density forecasts, as well as methodological contributions, including how financial market data could have been incorporated into the forecasting process.