Deep learning based forecasting in stock market with big data analytics

Şişmanoğlu, Gözde
Önde, Mehmet Ali
Koçer, Furkan
Sahingoz, Ozgur Koray
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In recent years, due to the technological improvements in computers' hardware and enhancements in the machine learning techniques, there are two increasing approaches for problem-solving as the use of "Big Data" and "Parallel Processing". Especially with the emergence of Deep Learning algorithms which can be executed parallelly on multi-core computing devices such as GPUs and CPUs, lots of real-world problems are resolved with these approaches. One of the most critical application areas in the Financial Market especially sits on Stock Markets. In this area, the aim is trying to predict the future value of a specific stock by looking at its previous financial data on the exchange process in the market. In this paper, we proposed a system that uses a Deep Learning based approach for training and constructing a knowledge base on a specific stock such as "IBM". We get time series values of the stock from the New York Stock Exchange which starts from 1968 up to 2018. Experimental results showed that this approach produces very good forecasting for specific stocks.
Deep Learning, Big Data, Forecasting, Tock Exchange, Derin Öğrenme, Büyük Veri, Tahmin, Borsa