Stock Market Investment Risk Measurement Method Based on Data Mining and Decision Trees.

This study endeavors to introduce a method for measuring stock market investment risk, leveraging data mining techniques alongside decision trees (DTs). By harnessing extensive stock market data and integrating steps such as data cleaning, feature selection, and model construction within data mining technology, an effective risk measurement model is formulated. Specifically, DTs serve as the primary modeling tool, adept at capturing intricate relationships and nonlinear characteristics prevalent within the stock market, thereby facilitating precise measurement of investment risks. Through empirical analysis, the efficacy and viability of the proposed method in risk measurement are substantiated, furnishing investors with a pivotal decision-making reference. Overall, this study contributes to the ongoing discourse on stock market risk assessment by integrating advanced data mining methodologies, thereby enhancing the accuracy and reliability of risk evaluation in investment decision-making processes. [ABSTRACT FROM AUTHOR]