I AM YUEYIN JI
Forecast IPOV of Funds Based on Neural Network
Introduction
My objective is to demonstrate the efficacy of neural networks in predicting IOPV, and in doing so, contribute to the broader field of financial analytics. The study meticulously documents each phase of the modeling process—from data preparation to parameter optimization, model training, and prediction—culminating in a comprehensive analysis of the model's performance. Through this research, I aim to shed light on the potential of machine learning technologies in transforming financial forecasting and decision-making processes.
Data Preparation
In this part, I selected relevant financial datasets and performed necessary preprocessing steps like normalization to ensure the data is suitable for modeling.
Neural Network Model
The core of the study is the development of a neural network model to predict IOPV. The model used a Multi-Layer Perceptron (MLP) Regressor, a type of artificial neural network known for its effectiveness in regression tasks.
Parameter Tuning and Optimization
I employed techniques like Grid Search Cross-Validation to find the optimal parameters for the neural network model, ensuring the best possible performance.
Model Training and Prediction
With the optimal parameters identified, the model is trained on the prepared data. The study then used the trained model to make predictions about IOPV.
Result Analysis and Visualization
I presented an analysis of the model's performance, including metrics like Mean Squared Error (MSE) on both training and test datasets. I also included visualizations comparing the predicted values of IOPV with the actual values, demonstrating the model's accuracy.
Conclusion
​​This study concludes with insights gained from the model's performance, discussing the effectiveness of neural networks in forecasting IOPV and potential improvements or future research directions.