Predicting Stock Market Movement: Trend Deterministic Data

Year 2019, Volume: 22 Issue: 1, 246 - 256, 30.04.2019

Hakan Pabuçcu

https://doi.org/10.29249/selcuksbmyd.487862

Cited By: 6

Abstract

This study focuses on the estimation of negative and positive movements of BIST 100 stock index. The predictive performances of artificial neural network, support vector machine and naive Bayes algorithm are compared. The analyzes are carried out in two stages. In the first stage, nine technical indicators to be used as input in the estimation models are calculated by using the stock index, opening, closing, highest and lowest prices. In the second stage, the trend-setting dataset is used as input and the predictions are made by using three selected machine-learning algorithms. The BIST 100 data set includes the daily closing prices covering the range of 2009-2018. With the analysis, it is concluded that the support vector machines algorithm is the best classifier. In addition, comparisons with previous similar studies and the effects of both the data set used and the prediction models are discussed.

Keywords

BIST 100, Artificial Neural Network, Support Vector Machine, Naive Bayes Classifier, Machine Learning

Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri

Abstract

Bu çalışma BIST 100 borsa endeksinin negatif ve pozitif yönlü
hareketlerinin tahmin edilmesini konu edinmektedir. Yapay sinir ağı, destek
vektör makinesi ve naive Bayes algoritmasının tahmin performansları
karşılaştırılmaktadır. Analizler iki aşamalı olarak yapılmaktadır. Birinci
aşamada tahmin modellerinde girdi olarak kullanılacak dokuz adet teknik
gösterge, borsa endeksi açılış, kapanış, en yüksek ve en düşük fiyatlar,
kullanılarak hesaplanmakta ve sürekli olan bu teknik göstergeler
barındırdıkları trende göre kategorize edilerek yeni bir veri seti
oluşturulmaktadır. İkinci aşamada ise, trend belirleyici veri seti girdi olarak
kullanılmakta ve seçilen üç makine öğrenme algoritması kullanılarak tahminler
yapılmaktadır. BIST 100 veri seti 2009-2018 Aralığını kapsayan günlük kapanış
fiyatlarını içermektedir. Analizlerle, destek vektör makineleri algoritmasının
en iyi sınıflandırıcı olduğu sonucuna ulaşılmıştır. Ayrıca, daha önceki benzer
çalışmalarla karşılaştırmalar yapılarak gerek kullanılan veri seti gerekse
tahmin modellerinin etkileri tartışılmaktadır. 

Keywords

BIST100, Yapay Sinir Ağları, Destek Vektör Makineleri, Naive Bayes, Makine Öğrenme

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