Grupo focal

week.

Decision analysis

When evaluating decisions in table5.2it is revealed that, contrary to the fiat cur- rency output analysis5.2.1, this dataset has significantly more ”sell” than ”buy” signals overall. In particular, for the year 2017 there is a much greater amount of ”sell” than ”buy” signals, and ”sell” signals were double the number of ”buy” sig- nals for the period in 2015. Lastly, there is an equal amount of ”buy” and ”sell” signals in the year 2016 which was not observed in the fiat currency analysis5.2.1.

Chapter 6

Results

In this chapter we analyse the test results obtained in the implementation of each prediction model. We analyse the results within the fiat currency as well as the cryptocurrency context. Furthermore, each model is discussed in terms of its Up- trend, Downtrend and Out-of-Sample prediction accuracies respectively. As stated in previous chapters, the Uptrend and Downtrend prediction accuracies refers to the particular model’s ability to correctly predict change of direction with respect to the signals ”Buy” and ”Sell” inferred on the quoted currency USD. Out-of-Sample prediction Accuracy refers to overall predictive ability on the unseen set.

In both the fiat currency and cryptocurrency sections a sigmoid activation function is used for the implemented ANN optimised through a genetic training. It pos- sesses many desirable qualities, one being that the function and its derivatives are continuous everywhere (Shazly and Lou,2016), it also gives the proposed ANN the ability to estimate nonlinear functions. The function is also symmetric and in line with the suggestions ofRefenes et al.(1993) who showed that symmetric functions yield faster convergence and a better generalised performance. Firstly, we state some experimental insights which give some justification for the chosen approach.

Experimental insights During the process of trial and error in the experimen- tal stages, an original split of 80% training and 20% unseen was attempted. The findings showed that this split put the ANN optimised with genetic training at a significant disadvantage to the other implemented models. In particular, the larger unseen set brought about a greater prediction horizon. This intern means that an ANN model, trained and optimised to a specific fitness on the training set, would then make predictions on all the weeks in the unseen set using only knowledge obtained from a static training set. However, this is not the true for other predic- tion methods implemented such as the SMA where it uses all previously available needed information to make a prediction for the next time step. Then at the next time step, it will use all available needed information at that time step and so on. This gives the SMA the ability to take into account the latest and older market data when making its forecasts. As such, the SMA is in a process of continuous learning whereas the ANN optimised with a GA no longer learns after training and relies only on the knowledge obtained in the training set. Therefore, in an attempt to put models on a more equal footing the following options were identified:

6.1 Fiat Signal detection Results 24

for models that are not continuously learning.

2. Implement a genetically optimised ANN using a moving training set ap- proach. In this approach, after each weekly prediction on the unseen data, the observed values form part of the training set. Thereafter, using all the data available till that week, retraining through the genetic algorithm takes place to a specified fitness level before the next prediction takes place and so on.

In this study, a moving training set approach is implemented since this would en- able the genetically optimised ANN to continuously learn from new market infor- mation thus increasing its forecasting ability. This is also believed to be the best approach since it would mirror an asset manager’s approach.

6.1

Fiat Signal detection Results

The data set of weekly observations was for the complete period of 20 September 2015 - 16 September 2018 and was divided into two subsets: training set period 20 September 2015 - 21 January 2018 and unseen set 28 January 2018 - 16 September 2018. The training and unseen sets therefore consisted of 123 and 34 weekly observations respectively, which is sufficiently close to the 80/20 split suggested in earlier chap- ters. Note also that since this is timeseries data data-points are indexed in order of time, therefore the unseen set’s weekly observations are strictly after those of the training set and all implemented prediction models should take this into account.

Random simulation on Fiat

Here the results obtained from the Random simulation are presented. Since the method inherently random, it is important for a general illustration of its predic- tive capabilities to be presented. This is what figure6.1illustrates through the use of histograms showing the Downtrend, Uptrend and Out-of-Sample Prediction Ac- curacies for 1000 repetitions of the method on the unseen set. Table6.1shows the Average Downtrend, Uptrend and Out-of-Sample Prediction Accuracy.

Avg. Uptrend Prediction Accuracy 0.5018 Avg. Out-of-Sample Prediction Accuracy 0.5014 Avg. Downtrend Prediction Accuracy 0.5008