Identifying the Best Fitting Model and Making Predictions

What is the best fitting model among the linear, quadratic, cosine, cyclical, or seasonal trend models?

Based on the provided data, how can we determine the best fitting model and make predictions for the next 15 trading days?

Model Selection and Prediction

To identify the best fitting model, we need to follow a model-building strategy. We will fit each trend model (linear, quadratic, cosine, cyclical, and seasonal) to the dataset and calculate the residuals for each model. The model with the smallest residuals, indicating the least amount of error, will be considered the best fitting model.

Understanding the Model Selection Process

When analyzing the dataset provided, we first need to implement the model-building strategy described in Module 1. This strategy involves fitting each trend model from Module 2 to the data and calculating the residuals. The residuals represent the error between the observed values and the predicted values produced by the model.

Deriving Predictions for the Next 15 Trading Days

After identifying the best fitting model based on the smallest residuals, we can proceed to make predictions for the next 15 trading days. By using the selected model, we can forecast the portfolio's return for each of the upcoming trading days.

Organizing Your Findings

It is essential to document your model selection process and the predictions made for the next 15 trading days in a well-structured report. The report should include sections such as Introduction, Conclusion, Appendix, and a chapter detailing the tasks performed. Ensure that the report is clear, organized, and presents your findings effectively.