  Financial performance management is at the core of business management and
has historically relied on financial ratio analysis using Balance Sheet and
Income Statement data to assess company performance as compared with
competitors. Little progress has been made in predicting how a company will
perform or in assessing the risks (probabilities) of financial
underperformance. In this study I introduce a new set of financial and
macroeconomic ratios that supplement standard ratios of Balance Sheet and
Income Statement. I also provide a set of supervised learning models (ML
Regressors and Neural Networks) and Bayesian models to predict company
performance. I conclude that the new proposed variables improve model accuracy
when used in tandem with standard industry ratios. I also conclude that
Feedforward Neural Networks (FNN) are simpler to implement and perform best
across 6 predictive tasks (ROA, ROE, Net Margin, Op Margin, Cash Ratio and Op
Cash Generation); although Bayesian Networks (BN) can outperform FNN under very
specific conditions. BNs have the additional benefit of providing a probability
density function in addition to the predicted (expected) value. The study
findings have significant potential helping CFOs and CEOs assess risks of
financial underperformance to steer companies in more profitable directions;
supporting lenders in better assessing the condition of a company and providing
investors with tools to dissect financial statements of public companies more
accurately.
