Hisse senetlerindeki trendlerindeki dalgalanmaları öngörmekderin öğrenme
2022
0 views
0 downloads
Advisor: Dr. Öğr. Üyesi Ayça Kurnaz Türkben
Abstract (EN)
Recent studies indicate that variations in the value of the stock market are difficult to anticipate due to the large number of unknowns and variables that affect its value on any given day. This includes the current market conditions, investor opinion toward a certain firm, and political developments. Hurriedly and arbitrarily, the pricing marketplaces are selected when setting the stock price. Due to the fact that it is not rare for the stock market to be dynamic and disorderly (due to several reasons), the stock market's direction is categorized as a random process, with more shifting possibilities in short time frames Therefore, an attempt to carry out a price forecast in the stock market can bring great benefits to investors, by increasing the level of information about the financial market, minimizing exposure to financial risk. In this sense, a computational technique called Artificial Neural Networks (ANNs) can be applied. The Artificial Neural Network (ANN) simulates on computers the functioning of the human brain in a simplified way.
Author
Dr. Iman Mohammed
Institution
How to Cite
Iman Mohammed (Master Thesis). Hisse senetlerindeki trendlerindeki dalgalanmaları öngörmekderin öğrenme, 2022, Altınbaş University.
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from Altınbaş University
- Mahmutbey, İstanbul'da sosyal dayanıklılık ve toplumsal uyumun güçlendirilmesi(2025)
- Evaluation of the factors affecting the choice of child oral care products and the attitudes of parents to these products(2023)
- Poliüre kaplamanın alüminyum köpük ve katkılı üretilen numunelerin mekanik özelliklerine etkisi(2021)
- Internationalism and a socialist workers' organization in Ottoman Empire: The socialist workers' federation of thessaloniki (1908 - 1914)(2019)
- Symmetry-based multi-objective AI/ML driven optimization framework for sustainable building performance(2026)
- The effect of music and aromatherapy on dental anxiety and fear in children(2024)
