Feature engineering is a crucial step in preparing financial data for machine learning models, aiming to extract meaningful information that enhances the predictive power of these models. Raw financial data often requires transformation and manipulation to reveal underlying patterns and relationships that are not immediately obvious. This process can greatly improve the accuracy of AI models by providing them with more relevant and informative features. This description will focus on specific techniques that significantly impact the performance of AI in financial applications.
One of the most fundamental techniques is the creation of financial ratios. These ratios are calculated by combining different financial metrics to reveal relationships between different parts of the financial data. For example, the debt-to-equity ratio, calculated by dividing total debt by total equity, provides an insight into the financial leverage of a company. This ratio can be very important in predicting the financial stability of a company. Similarly, the current ratio, calculated by dividing current assets by current liabilities, indicates a company's ability to meet its short-term obligations. Analyzing these and other financial ratios such as profit margins, return on assets, or quick ratio, rather than relying solely on raw metrics, can make it much easier for AI models to understand the underlying financial health of a company, since these ratios are indicators that are frequently used by financial analysts. This allows the AI model to not only understand what the current values are, but also to understand the relationship between different features.
Another important technique is the creation of lagged features, which incorporate time-series data from previous time periods. Financial markets are highly dep....
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