Application Research of Neural Networks in the Evaluation of Investment Risk for Entrepreneurial Ventures

This research investigates the practical application of neural networks in evaluating investment risks within entrepreneurial ventures. The study specifically contrasts the performance of two neural network models, namely the Multi-Layer Perceptron (MLP) and the Long Short-Term Memory (LSTM) network. The findings underscore the superiority of the LSTM model in comprehensively capturing intricate temporal relationships inherent in entrepreneurial investment datasets. The results reveal a notable enhancement in crucial metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²) when employing the LSTM model, indicating its proficiency in providing more accurate risk assessments compared to the MLP model. Despite these positive outcomes, the study acknowledges certain limitations, including the potential influence of training data quality and the impact of architectural choices on model outcomes. Looking ahead, future research avenues involve exploring advanced neural network structures, integrating additional feature variables, and expanding the scope of datasets to address these limitations comprehensively. This study contributes valuable insights to the burgeoning field of neural network applications in entrepreneurial investment, offering a foundation for further exploration and refinement of models.