Predictive AI for SME and Large Enterprise Financial Performance Management

ArXiv ID: 2311.05840 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Financial Ratio Analysis, Bayesian Networks, Feedforward Neural Networks (FNN), Financial Performance Management, Equities

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced supervised learning models (FNNs) and Bayesian Networks with probability density functions, indicating significant mathematical depth. It provides a structured methodology for model training and evaluation with specific performance metrics (e.g., 11% improvement), showing solid empirical grounding though without public datasets or code.
  flowchart TD
    A["<b>Research Goal</b><br/>Improve predictive accuracy for financial performance<br/>(ROA, ROE, Net Margin, etc.)"] --> B["<b>Data & Inputs</b><br/>Balance Sheet & Income Statement<br/>New Macro/Financial Ratios"]
    B --> C["<b>Methodology</b><br/>Supervised Learning<br/>(ML Regressors)"]
    C --> D["<b>Model Training</b><br/>Feedforward Neural Networks (FNN)<br/>Bayesian Networks (BN)"]
    D --> E["<b>Computational Process</b><br/>6 Predictive Tasks<br/>(Regression & Probability Density)"]
    E --> F["<b>Key Findings / Outcomes</b><br/>• FNN performs best overall (simpler)<br/>• New ratios improve accuracy<br/>• BN offers risk probability (PDF)<br/>• Actionable insights for CFOs/CEOs/Lenders"]