This is the final part of my series of articles on my dissertation from the University of York. The first article introduced the basic concepts for this study while the second article described the experimentation process and approaches taken. This final article explains the results and evaluation.

# Results and Evaluation

## The Base Cases

The first base case consisted of just predicting the most common direction, while the second base consisted of a feedforward network which took the closing prices of the FTSE for the last five days.

The data used for training and testing these experiments spanned from 1st January 2013 to 1st January 2018. This translates to 1256 days of closing prices, thus splitting the data 80 / 20 will result in 1004 daily closing prices for training and 252 for testing. The direction of the FTSE index in the testing dataset rose 51.98% of the time, therefore 48.02%. Therefore, the **predictive accuracy of the first base case is 51.98%**.

As for all feedforward networks, an exercise for finding the most suitable learning rate and number of hidden nodes had to be performed. This consisted of running models with different combinations of parameters. For each combination, the model was run 20 times and the average accuracy was reported. The graph below shows the results from this exercise for the second base case.

**The optimal parameters for the second base case were 9 hidden nodes with a learning rate of 0.0016. These parameters achieved an average of 55.36%.** Continue reading “The Impact of Macroeconomic Parameters and Global Markets on Forecasting the FTSE Index (Part 3)”