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https://doi.org/10.62836/iaet.v3i1.312
Kaixian Xu, Yunxiang Gan, & Alan Wilson. (2024). Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances. Innovations in Applied Engineering and Technology, 3(1), 1–12. https://doi.org/10.62836/iaet.v3i1.312
Volume 3, Issue 1

Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances

Predicting financial performance, particularly in the Trust and Private Equity sectors, is a critical challenge for investors and analysts, as it directly impacts decision-making and risk management. This study addresses this problem by proposing a novel solution using stacked generalization, an ensemble learning technique, to improve the accuracy and robustness of financial performance predictions. We utilized a dataset consisting of key financial metrics, including Net Income, EBITDA, Market Cap, Gross Profit, Current Ratio, and Debt/Equity Ratio, to predict Net Income as a proxy for Trust and Private Equity performance. The proposed solution employs Random Forest (RF), Linear Regression (LR), and Support Vector Machine (SVM) as the base model and the Neural network as meta-model to enhance prediction precision. The results indicate that stacked generalization significantly outperforms traditional single-model approaches, yielding higher accuracy and reliability in forecasting financial performance. This study not only offers an effective tool for financial prediction but also provides a foundation for future work, where additional features, more advanced models, and sector-specific applications can be explored to improve predictive capabilities.

stacked generalization; financial performance; trust; private equity

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Supporting Agencies

  1. Funding: Not applicable.