| Category | Assignment | Subject | Accounting |
|---|---|---|---|
| University | University of Exeter | Module Title | BEAM079: Coding Analytics for Accounting and Finance |
This study uses both financial ratios and textual data to improve prediction of the chances of insolvency. This study investigates by means of logistic regression and neural networks on whether additional use of textual information obtained from annual reports enhances the forecasting ability of financial models.
Traditionally, the prediction of bankruptcy relied solely on numerical financial analysis, but in this study, dual approaches are adopted whereby both financial and textual data were analyzed. Alongside these, factors such as sentiment and readability scores drawn from corporate communications are also test predictors. This will give investors, financial analysts, and policymakers a well-rounded analytical tool for financial risk and bankruptcy forecasting.
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