PREDICTING STOCK MARKET MOVEMENT BY USING MACHINELEARNING ALGORITHM

Year 2019, , 179 - 190, 09.04.2019

Hakan Pabuçcu

https://doi.org/10.18092/ulikidince.484138

Cited By: 7

Abstract

In addition to the uncertainty and chaotic movements of the financial
time series, the nonlinear dynamic structure makes the forecasts very
difficult. The fact that the stock market index are affected by the political
changes, the general outlook of the economy, the investors' expectations and
investment preferences, and the movements of other indexes, make the index
estimates quite difficult but attractive. It is known that the machine learning
algorithms are successful in estimating stock index movements and their future
values. In this study, the problem of forecasting the direction of BIST 100
index movements is discussed. Three different machine learning algorithms,
artificial neural networks, support vector machines and naïve Bayes classifier
were used and their performances were compared. Ten technical indicators were
used as inputs for the models. The data set consists of ten-year daily closing
price values covering the 2009-2018 period. Analysis results show that the
models can be used to capture stock market index movements, whereas artificial
neural network algorithm is a better classifier.

BORSA ENDEKSİ HAREKETLERİNİN MAKİNE ÖĞRENME ALGORİTMALARI İLE TAHMİNİ

Abstract

Finansal
zaman serilerinin barındırdığı belirsizlik, kaotik hareketler yanında doğrusal
olmayan dinamik yapı, tahminleri oldukça güçleştirmektedir. Borsa endekslerinin
politik değişimler, ekonominin genel görünümü, yatırımcıların beklenti ve
yatırım tercihleri ve diğer endekslerin hareketleri gibi birçok makroekonomik
faktörden etkilenmeleri, endeks tahminlerini oldukça zor ancak bir o kadar da
çekici kılmaktadır. Borsa endeksi hareketleri ve geleceğe dönük tahminler
üretmede makine öğrenme algoritmalarının başarılı oldukları bilinmektedir. Bu
çalışmada BIST 100 endeksi hareketlerinin yönünün tahmin edilmesi problemi ele
alınmıştır. Üç farklı makine öğrenme algoritması olan yapay sinir ağları,
destek vektör makineleri ve naive Bayes sınıflandırıcı algoritması kullanılmış
ve performansları karşılaştırılmıştır. Borsa endeksi tahminleri için kullanılan
on teknik gösterge modeller için girdi olarak kullanılmıştır. Veri seti
2009-2018 periyodunu kapsayan günlük kapanış değerlerini içermektedir. Analiz
sonuçları, her üç modelin de borsa endeks hareketlerini yakalamada
kullanılabilir olduğunu, yapay sinir ağı algoritmasının ise daha iyi bir
sınıflandırıcı olduğunu göstermiştir.

Keywords

BIST100, yapay sinir ağları, destek vektör makineleri, naive Bayes, makine öğrenme

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