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Generating Buy/Sell Signals for an Equity Share Using Machine Learning
Authors: Bugra ERKARTAL, Linet OZDAMAR
Number of views: 440
This study proposes a novel model for predicting 5 days’ ahead share price direction
of GARAN (Garanti Bankasi A.Ş.), an equity share that is the top traded stock in
BIST100, Istanbul Stock Exchange -Turkey. The first model includes global
macroeconomic indicators as well as local inputs whereas the second model is
focused more on local inputs. The performances of the two models are tested using
Support Vector Machines (SVM), Neural Network with Back-Propagation (BPN), and
Decision Tree (DT) algorithms. Though BPN and SVM have previously been used to
predict BIST100 Index movement, DT has not been utilized before with this purpose.
Forecasting is carried out tested for a time span of about 6 months on a rolling
horizon basis, that is, algorithms are re-run weekly with updated data to generate
daily buy/sell signals for the next week. A simple trading strategy is implemented
based on buy/sell signals to calculate the rate of return on investment during the
testing period. The results illustrate that DT having 80% prediction accuracy
outperforms BPN and SVM that achieve 60% accuracy. Consequently, DT achieves a
higher rate of return
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