New Constructs: Disrupting Fundamental Analysis with Robo-Analysts

This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions, and aid investment analyses. Combining machine learning with human financial analysts, this technology automated the collection and classification of comprehensive quantitative data from annual (10-Ks) and quarterly reports (10-Qs). The technology parsed information from the face of the financials and from disclosures in the management discussion and analysis or the footnotes sections to the financial statements (e.g., gains and losses that are transitory in nature or off-balance-sheet liabilities like future operating lease payments). Using these data, New Constructs applied a set of transparent and clearly-defined adjustments to a firm’s income statement and balance sheet to create alternative measures of its sustainable operating profits, invested capital, and return on invested capital. This case highlights the complexity of financial disclosures in 10-Ks and 10-Qs and how machine learning and human analysts can work together to improve the quality of investment research. This case also highlights how analysts may adjust income statements and balance sheets to understand firm performance better or facilitate forecasting. However, could the CEO of New Constructs convince market participants of the technology’s (and the data’s) value? What else could the company do to establish a toehold in an increasingly competitive market for financial and alternative databases?