Uzun-Kısa Süreli Bellek Ağı Kullanarak Hisse Senedi Fiyatı Tahmini

TOKMAK>, Mahmut

doi:10.31200/makuubd.1164099

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…The focus of this study revolves around the forecasting of stock prices for four specific stocks within the Technology Index, utilizing deep learning methodologies like as LSTM . The results of this investigation illustrate the reliability of these predictions [3].…”

Section: Literature Reviewsupporting

confidence: 64%

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Stock Price Forecasting Using Machine Learning and Deep Learning Algorithms: A Case Study for the Aviation Industry

Gür

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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With technological advances, humans are constantly generating data through various electronic devices and sensors, and this data is stored in digital environments. A vast amount of data has served as a valuable asset that has facilitated the rise and progression of novel fields, including data science, artificial intelligence (AI), deep learning (DL), and the internet of things (IoT). Effectively managing and analyzing data provides a competitive advantage for modern businesses. The objective of this study is to forecast the stock price of Turkish Airlines (THY), a publicly traded corporation listed on Borsa Istanbul. In order to achieve the intended objective, the utilization of machine learning approaches like SVM and XGBoost, as well as the deep learning algorithm Long Short-Term Memory (LSTM), are used. The models are trained over a time period including daily data from January 4, 2010 to September 5, 2023. The forecast performance of the models is evaluated by comparing the actual and predicted stock prices and the model with the lowest error is identified. The proposed models' performances are assessed using the RMSE, MSE, MAE, and R2 error statistics. According to the results obtained, it is determined that the LSTM model has lower error coefficients than SVM and XGBoost models and gives the best performance.

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Section: Literature Reviewsupporting

confidence: 64%

“…Besides macroeconomic indicators, certain variables at the micro-level within sectors can also impact stock values. During periods of economic uncertainty, precise forecasting of these values becomes crucial for investors in order to mitigate financial risk [2,3].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Forecasting Using Machine Learning and Deep Learning Algorithms: A Case Study for the Aviation Industry

Gür

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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“…These insights hold paramount importance for investors and analysts looking to understand and predict THY's stock price movements. In a volatile market and evolving economic conditions, accurate stock price predictions are essential for effective investment risk management (Tokmak, 2022) and portfolio management (Ekawarti, 2018). Additionally, these findings provide actionable guidance for market participants, enabling them to make informed decisions in the dynamic aviation sector.…”

Section: Discussionmentioning

confidence: 99%

“…This understanding aids investors in making informed decisions (Tse, 2001) and enables companies to enhance their market value and attractiveness to investors (Branch, 1983). Moreover, accurate predictions of stock prices are essential for managing investment risks (Tokmak, 2022) and effective portfolio management (Ekawarti, 2018). Additionally, insights into the factors affecting stock prices offer a deeper understanding of market behavior and momentum (Safdar, 2020).…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Long-Term Stock Performance of Airlines: The Case of Turkish Airlines

Akusta

2024

Politik Ekonomik Kuram

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This study presents a time series analysis of the long-term stock performance of airlines, with a focus on Turkish Airlines (THY). Stock-related variables were obtained from the Istanbul Stock Exchange, and other variables were obtained from the Turkish Statistical Institute. The research investigates various economic factors that may influence THY's stock prices over an extended period. Specifically, the study examines the relationship between global energy prices, inflation, the BIST100 index, gold prices, exchange rates, and THY stock prices. The analysis employs the Autoregressive Distributed Lag (ARDL) approach, a versatile technique capable of estimating both short-term and long-term relationships between variables. The findings of this research reveal significant and statistically meaningful connections between these economic variables and THY's stock prices. Notably, the study highlights the impact of global energy prices and other economic factors on the long-term stock performance of Turkish Airlines. These results provide valuable insights for investors and analysts seeking to understand and forecast THY's stock price movements.

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Inception Sh: A New CNN Model Based on Inception Module for Classifying Scene Images

Metlek,

Çetiner

2024

Mühendislik Bilimleri Ve Tasarım Dergisi

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In this study, a light-weight model with an optimum block structure that can be used in autonomous unmanned aerial vehicles (UAVs) was designed. The Inception SH model, which was developed based on the Inception V3 model, was compared on "Intel Image Dataset", a publicly available dataset in the literature. As a result of the comparison, values of 0.882, 0.883, 0.882 and 0.882 were obtained for the accuracy, precision, recall, and F1 score metrics for the Inception V3 model, respectively. In the Inception SH model, values of 0.958, 0.957, 0.974 and 0.967 were obtained for accuracy, precision, recall and F1 score metrics, respectively. As can be seen from these values, the proposed Inception SH model offers higher performance values than the underlying Inception V3 model. The Inception SH model was compared with different models in the literature using the same data set and was superior in accuracy, precision, recall and F1 score metrics compared to the compared models. According to the results obtained, it is predicted that the Inception SH model can be used as a lightweight model in various IoT devices, considering the popularity of autonomous UAVs.

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Uzun-Kısa Süreli Bellek Ağı Kullanarak Hisse Senedi Fiyatı Tahmini

Cited by 4 publications

References 40 publications

Stock Price Forecasting Using Machine Learning and Deep Learning Algorithms: A Case Study for the Aviation Industry

Stock Price Forecasting Using Machine Learning and Deep Learning Algorithms: A Case Study for the Aviation Industry

Time Series Analysis of Long-Term Stock Performance of Airlines: The Case of Turkish Airlines

Inception Sh: A New CNN Model Based on Inception Module for Classifying Scene Images

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