Abstract
In today's business landscape, predicting the financial health of a company is essential for informed decision-making by investors, creditors, and other stakeholders. Using historical financial data and machine learning techniques, it is now possible to predict the likelihood of a company going bankrupt. This chapter provides a step-by-step guide on how to predict company bankruptcy using the “company bankruptcy prediction” dataset available on UCI machine learning repository. The chapter covers data analysis, pre-processing, applying various machine learning algorithms, evaluating model performance, and applying models to new datasets. The aim is to equip readers with the necessary skills to analyze and predict the financial health of companies, making it a valuable resource for investors, creditors, and financial analysts.
Top2. Predicting Company Bankruptcy: Practice Using Weka
2.1 Define Business Questions
To predict company bankruptcy, we first need to define the business questions we want to answer. As investors or financial analysts, we may be interested in understanding the factors that impact the likelihood of a company going bankrupt and whether we can use financial data to predict the bankruptcy risk of a specific company. Some potential business questions that we can explore include:
What financial ratios and metrics are most strongly associated with company bankruptcies?
Can we use historical financial data to predict the probability of a company going bankrupt within the next year?
How accurately can we predict the bankruptcy risk of a specific company using machine learning techniques?
Which machine learning algorithms perform best for predicting company bankruptcies?
By answering these business questions, we can gain insights into the financial health of companies and make informed decisions as investors or financial analysts. In the next section, we will explore the “Company Bankruptcy Prediction” dataset and analyze the data to gain a better understanding of the factors that impact bankruptcy risk.
2.2 Collect Data
To begin with, we use a real-world dataset from the Taiwan Economic Journal for the years 1999-20091. We need to collect the “Company Bankruptcy Prediction” dataset from the UCI Machine Learning Repository2. This dataset consists of financial ratios and other financial information for various companies. We will be using this dataset to predict the likelihood of a company going bankrupt. The “Company Bankruptcy Prediction” dataset contains 95 financial ratios (Figure 24 in the Appendix) and other financial information for 6,819 companies. Each company is labeled as either bankrupt or not bankrupt. The dataset includes both numerical and categorical data.
In the next section, we will review and pre-process the data to prepare it for machine learning modeling.
Key Terms in this Chapter
Investor Decision-Making: The process of using financial and other relevant data to make informed decisions about investing in a company or organization.
Misclassification Costs: The costs associated with incorrectly classifying data into the wrong class or category in a classification model.
Financial Ratios: Metrics that are used to assess a company's financial health and performance, such as liquidity ratios, profitability ratios, and debt ratios.
Financial Analysis: The process of analyzing financial data to assess the financial health and performance of a company or organization.
Financial Health: A term used to describe the overall financial well-being and stability of an individual, organization, or system. In the context of this chapter, it refers to the financial stability of companies and their ability to meet financial obligations.
Data Preprocessing: The process of cleaning, transforming, and preparing raw data for use in machine learning algorithms.
Machine Learning: A type of artificial intelligence that allows computers to learn from data without being explicitly programmed.
Evaluation Metrics: Measures used to assess the performance of a machine learning model, such as accuracy, precision, recall, F1-score, and AUC-ROC.
Classification Models: A type of machine learning algorithm that is used to categorize or classify data into distinct groups or classes based on features or attributes.
Bankruptcy: A legal process for a business or an individual who cannot pay their debts and seeks protection from their creditors.
Bankruptcy Prediction: The process of using financial and other relevant data to assess the likelihood of a company going bankrupt or experiencing financial distress.
Cost-Sensitive Learning: A machine learning technique that involves adjusting the classification model to account for the misclassification costs associated with different classes.