Sosyal Medya ve Diğer Yatırım Aracı Verilerine Dayalı Hisse Senedi Değeri Tahmini

Year 2021, Volume: 5 Issue: 2, 267 - 285, 30.12.2021

Ömer Faruk Uyrun İbrahim Sabuncu

Abstract

Bu çalışmada, farklı makine öğrenmesi teknikleriyle yatırım aracı verileri ile birlikte sosyal medya verileri kullanılarak hisse senedi tahminlenmesi amaçlanmıştır. Çalışma kapsamında, beş farklı havayolu firmasına ilişkin Ekim 2019 – Şubat 2020 dönemine ait 236 764 adet tweet ve söz konusu şirketlerin hisse senedi değeri ve işlem gördüğü borsanın günlük verileri, dolar kuru ve altın fiyatları ele alınmış olup, tweet’lerin analizinde duygu analizi gerçekleştirilmiştir. Çalışmada, Gradyan Destekli Ağaçlar (Gradient Boosted Trees) algoritmasının hisse senedi tahminlemesinde en düşük hata payına sahip tahmin modeli olduğu tespit edilmiş olup, şirketler hakkındaki net pozitif (pozitif-negatif) tweet sayılarının hisse senedi değeri tahminindeki en etkili faktörlerden birisi olduğu görülmüştür. Çalışma sonucunda, Gradyan Destekli Ağaçlar algoritmasının çalışma kapsamında kullanılan diğer algoritmalara göre hisse senedi tahminlemesinde etkin olduğu ve Twitter verisinin diğer yatırım verileri ile birlikte hisse senedi değeri tahmininde faydalanılabilecek bir veri kaynağı olduğu düşünülmektedir.

Keywords

Sosyal Medya Analitiği, Veri Madenciliği, Tahmin Modeli, Hisse Değeri, Twitter

Supporting Institution

Yalova Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

2019/YL/0001

Forecasting Stock Value Based on Data from Social Media and Investment Instruments

Abstract

This study aimed to predict stocks using different machine learning techniques with social media data and investment instrument data. Within the scope of the study, 236,764 tweets related to five different airline companies during the period October 2019 - February 2020, the stock value of those companies, the daily data of the stock market, dollar rate and gold prices were discussed. Additionally, sentiment analysis was carried out in the analysis of the tweets. In the study, it was determined that the Gradient Boosted Trees algorithm was the prediction model with the lowest margin of error in stock prediction, and it was seen that the number of net positives (positive-negative) tweets about companies was one of the most influential factors in forecasting stock value. As a result of the study, it is thought that the Gradient Boosted Trees algorithm is effective in stock prediction compared to the other algorithms used in the study, and that Twitter data is a data source that can be used in forecasting stock value together with other investment data.

Keywords

Social Media Analytics, Data Mining, Prediction Model, Stock Value, Twitter

Project Number

2019/YL/0001

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