Week 7: Analyzing Results

This week was all about tuning and testing. With a functioning neural network model in place, my focus shifted to improving prediction accuracy and understanding why the model behaves the way it does. I ran multiple training sessions using different timeframes and adjusted parameters like batch size, learning rate, and the number of epochs. Through this process, I began to see stronger alignment between my model’s predictions and actual stock price movement, especially in stable market conditions.

I also spent time comparing my model’s performance to standard benchmarks, such as a simple buy-and-hold strategy with the S&P 500. Encouragingly, in some shorter time windows, my model showed better returns with lower volatility. However, it still struggled during rapid market shifts, which I’m hoping to address by experimenting with more responsive technical indicators and adding momentum-based features.

Another key part of this week was learning how to visualize model performance. I created graphs showing predicted vs. actual returns, which helped me spot patterns and identify when the model underperforms. These visuals will also be useful for my final presentation. With input from my external advisor, I started exploring ways to evaluate the model not just based on raw accuracy, but in terms of practical investment value, like profit/loss and risk-adjusted return.

Next week, I’ll dive deeper into testing this model on new stock tickers and refining its architecture to increase consistency. It’s great to see everything start to come together!