Objective
This project aims to construct a novel, more accurate measure of corporate financial constraints. Building on the latest academic research, it moves beyond traditional accounting-based indices to leverage the rich, unstructured information in corporate financial filings. The goal is to create a more dynamic and nuanced signal to improve systematic investment strategies by better identifying firms whose investment and financing decisions are materially affected by financing frictions.
The Evolving Academic Consensus: From Accounting Ratios to Textual Analysis
A multi-decade academic debate has shown the limitations of traditional, accounting-based constraint indices (e.g., KZ, WW), with seminal work by Farre-Mensa & Ljungqvist (2016) demonstrating their failure to predict actual firm behavior. A new consensus has emerged, pioneered by researchers like Hoberg & Maksimovic (2015) and Bodnaruk, Loughran & McDonald (2015), that the most informative and robust signals are found in the text of 10-K filings.
The Contribution: From Keywords to Context
While early textual methods relied on keyword counts and dictionaries, this research takes the next logical step by applying modern Natural Language Processing (NLP) and machine learning to understand context and semantic meaning. The methodology follows a hybrid, two-stage process inspired by recent work in the field (e.g., Lin & Weagley, 2023):
Generate a "Ground Truth" Signal with LLMs: The first step is to create a high-quality, nuanced measure of constraints directly from the text. I am developing a proof-of-concept using a zero-shot classification Large Language Model (LLM). By engineering a precise prompt with categories like "Mentions covenant violation risk" or "Discusses difficulty raising equity," the LLM can perform a targeted, thematic analysis of the MD&A section of 10-K filings. This approach aims to capture the contextual richness of managerial disclosures in a scalable way, improving upon simpler "bag-of-words" models.
Build a Scalable, Broad-Coverage Model: The direct textual analysis, while informative, has limited historical and cross-sectional coverage. To create a universally applicable factor, the second stage uses the high-quality textual scores from Step 1 as a "training label" for a powerful, non-linear classifier (XGBoost). This model learns the complex, non-linear relationships between a firm's standard financial statement data and its "true" constraint status as revealed by the text. The final output is a robust, quantitative "FC_Score" that can be calculated for a broad universe of stocks over a long history.
Potential Investment Applications
This superior measure of financial constraints is a practical tool designed for direct integration into a systematic investment process.
Enhancing Existing Factors (e.g., Value & Growth):
A key application is to refine traditional factors. The Value premium, for example, is likely conditional on a firm's ability to act on its apparent cheapness. By using the FC_Score, a portfolio manager can isolate high book-to-market firms that are also financially unconstrained, creating a much sharper and more consistent Value signal. Conversely, it can be used to screen out high-growth firms that lack the capital to fund their own expansion.
A Superior, High-Frequency Signal:
Since textual data from corporate filings and news is more timely than quarterly financial statements, the FC_Score can serve as a higher-frequency indicator of a company's changing financial health, providing an edge in alpha generation.
Granular Risk Management:
By disaggregating the signal into Debt Constraint and Equity Constraint scores, the model provides a more nuanced view of firm-level risk. This allows for more precise risk management in a portfolio, differentiating between firms facing covenant risk and those facing informational asymmetry challenges.