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)”
This is part two of my series of articles on my dissertation from the University of York. While the first article introduced the basic concepts required for this study, the second article describes the experimentation process and approaches taken. Check out my GitHub if you want to skip to looking at the code.
This study seeks to measure the impact of certain external factors when predicting the direction of the FTSE index. These external factors are macroeconomic parameters and closing prices from other stock market indices. It is important to highlight that this study does not predict the actual closing price, but only whether the index will rise or fall on a daily basis. The aim of this study was not to find a state-of-the-art technique, or to build a trading tool, but rather to only study the impact of data. Therefore, predicting the direction was deemed enough for measuring this impact. Additionally, I only had four months, so I also had to limit the scope of my study somehow!
The study consisted of the following experiments:
- Predict the FTSE index by incorporating FTSE data with closing prices from other stock market indices. (Hypothesis 1)
- Predict the FTSE index by incorporating FTSE data with macroeconomic parameters. (Hypothesis 2)
- Predict the FTSE index by incorporating FTSE data with both closing prices from other stock market indices and macroeconomic parameters. (Hypothesis 3)
To determine whether an improvement in prediction accuracy was achieved, the above experiments were compared with two base cases:
- Always predict the most common direction. (Base Case 1)
- In the chosen dataset period (1st January 2013 to 1st January 2018), the most frequent direction was up.
- Predict the FTSE index by using the FTSE data only. (Base Case 2)
For all three hypotheses, the null hypothesis was that the method in question makes no significant difference in prediction accuracy when compared to underlying base cases.
Continue reading “The Impact of Macroeconomic Parameters and Global Markets on Forecasting the FTSE Index (Part 2)”
The following series of articles is about my masters dissertation from the University of York, supervised by Dimitar Kazakov. The project spanned over four months, therefore the project had a very limited scope. Nonetheless, the aim of this study was to measure the impact of macroeconomic parameters and closing prices from other global markets when predicting the direction of the FTSE index. This study made use of feed-forward networks and an ensemble approach with the TensorFlow library.
This article will serve as a basic introduction by defining some basic concepts, particularly stock markets, stock market indices, market contagion, macroeconomics and microeconomics. It will also mention what sort of data was used in this study. Feel free to skip this article if you are already well versed in these topics.
Background and Data
A stock is basically a share of a company. These stocks can be traded on what is called a stock exchange. Companies that are traded on a stock exchange are referred to as a listed company. As of June 2017, the stock exchange with the highest market capitalisation was the New York Stock Exchange (NYSE) at $21 trillion. These stock exchanges do not operate 24 hours a day and are closed throughout the weekend and bank holidays. The following table shows the opening and closing times for different stock exchanges from around the world. The price of a share at the end of the day is referred to as the closing price.
Continue reading “The Impact of Macroeconomic Parameters and Global Markets on Forecasting the FTSE Index (Part 1)”