Finansal Piyasalarda Hisse Fiyatlarının Derin Öğrenme ve Yapay Sinir Ağı Yöntemleri ile Tahmin Edilmesi; S&amp;P 500 Endeksi Örneği

Taş, Ali İsa; Gülüm, Pelin; Tulum, Gokalp

doi:10.29130/dubited.820620

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…As a result, a 95% accuracy rate was reported for the LSTM algorithm. Taş et al (2021) used the data from the S&P 500 index to predict the US dollar values. Estimations were made on the daily price data between 12.…”

Section: Literaturementioning

confidence: 99%

Finansal Zaman Serilerinin Derin Öğrenme Algoritmaları ile Tahminlenmesi

PAMUKÇU,

AYGÜL,

UĞURLU

2023

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Stock market index data, foreign currency, and gold have an important place in financial time series. Therefore, value or direction of movement estimation studies on this subject attracts the attention of both investors and researchers. This study aims to estimate the daily value of the US Dollar, Gold, and Borsa Istanbul (XU) 100 index using deep learning methods: Recurrent Neural Networks and Long-Short-Term Memory. A data set consisting of 2280 business days between 2013-2022, which includes the date, US Dollar, Gold, and XU 100 closing data, was used in the study. Mean absolute error, mean square error, root mean square error, and coefficient of determination were used to evaluate the performance of the developed prediction models. When the results were examined, it was seen that the Long-Short-Term Memory algorithm performs better than the Recurrent Neural Network algorithm and has an accuracy value of over 95% for the US Dollar, Gold, and XU 100 index. Moreover, the findings obtained in the study indicate that deep learning algorithms can show high prediction performance on financial time series without using extra independent variables.

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Section: Literaturementioning

confidence: 99%

Finansal Zaman Serilerinin Derin Öğrenme Algoritmaları ile Tahminlenmesi

PAMUKÇU,

AYGÜL,

UĞURLU

2023

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…Long Short-Term Memory (LSTM) is a type of repetitive neural network developed by creating solutions to problems in RNN [27]. While RNN can remember short-term information, LSTM can remember both short-term and long-term input information [28][29].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST

TOPRAK

Çağıl

Kökçam

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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This study predicts the stock price of Petkim Petrokimya Holding Corp. (PETKM), which is listed in Borsa Istanbul (BIST), using PETKM stock price, US dollar (USD/TRY) price and BIST Chemical, Petroleum & Plastic (XKMYA) index price. A time series data set with three inputs and one output is created using these data. Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. The success of these methods is compared using performance metrics such as MSE, RMSE, MAE, and R2. According to the calculated error metrics, LSTM and RFR algorithms gave better results than CNN with an MSE value less than 0.02. However, the fact that the R2 values of the most successful models created with all three algorithms were greater than 95% revealed that all the algorithms mentioned could be used to estimate this data set.

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“…Figure 3: General Structure of ANN(Atalay & Çelik, 2017).ANN is a data mining algorithm that is widely used in the literature in stock price determination, stock return determination, index return determination, credit scoring, and predictions about the economy, economics, finance and exchange rates(Aksoy, 2021;Altaş & Gülpınar, 2012;Çınaroğlu & Avcı, 2020;Ersöz, 2019b; Fılız, Karaboğa & Akogul, 2017;Karakul, 2020;Kavcıoğlu, 2019;Taş, Gülüm & Tulum, 2021; Yiğiter, Sari & Başakin, 2017).…”

mentioning

confidence: 99%

Data mining application with machine learning algorithms to manage interest rate risk

KOÇOĞLU

Ersöz²

2022

bmij

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In trade, the risks taken may increase the expected income; however, they may also cause large amounts of losses as well. Banks transfer the capital and the deposits they collect from their clients to the individuals or institutions in need of profit, taking certain risks into account. One of the important risks taken in this process of capital transfer is the market's change in interest or profit share rates. If the bank transfers the deposit collected with a certain commitment to the market at a lower rate, it will make a loss. Models for predicting future interest or profit share rates gain importance for preventing this situation. The aim of this study is to determine which variables will be taken into account for the loan interest rate that banks will offer to their customers during the lending process, and to create a machine learning model that can predict the loan interest rate that the bank will offer to its customers by using these variables. Multiple Linear Regression analysis was performed to demonstrate the relationship between the variables selected based on the literature review, expert opinions, and the interest rate. In order to facilitate decision-makers in practice, Random Forests, Decision Trees, K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) algorithms from machine learning algorithms were compared by using the prediction model. Accuracy Rate, Cohen's Kappa, Precision, Sensitivity, and F-Measure measurements were used to compare the algorithms used in the study. According to the analysis results, it was observed that the Random Forest algorithm was more successful on the first model consisting of weekly data. The Decision Tree algorithm succeeded more on the second model consisting of monthly data prediction performance. In the model consisting of weekly data, USD Selling Price, Stock Index (BIST100), and Central Bank Gold Reserve from the Multiple Linear Regression variables were found significant in affecting the interest rate.

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Finansal Piyasalarda Hisse Fiyatlarının Derin Öğrenme ve Yapay Sinir Ağı Yöntemleri ile Tahmin Edilmesi; S&P 500 Endeksi Örneği

Cited by 5 publications

References 13 publications

Finansal Zaman Serilerinin Derin Öğrenme Algoritmaları ile Tahminlenmesi

Finansal Zaman Serilerinin Derin Öğrenme Algoritmaları ile Tahminlenmesi

Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST

Data mining application with machine learning algorithms to manage interest rate risk

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