… reduce the risk of trend prediction with machine learning and deep learning algorithms. Four
… [17] examined a deep neural network model with public market data and the transaction …

… becomes possible to predict the future trend of stock market with … trees used for prediction
of continuous variables are called … In this study, RNNs are used from deep neural network …

… to predict the stock market prices of BIST 100 through all … where 𝐶 is a constant value that
assigns some penalty values … has the worst forecasting performance among other models …

… machine learning techniques, such as deep neural networks … via principal component analysis
(PCA), to predict the daily … gap of more than five continuous trading days during the study …

… [10] applied deep neural networks for stock market direction prediction, showcasing the … for
stock market prediction, encompassing machine learning, deep learning, time series analysis…

… machine learning algorithms were utilized for prediction of … a deep neural network model
with public market data and the … , all parameters but epochs are constant; for RNN and LSTM …

… Due to the continuous innovation of machine learning … to the use of machine learning
techniques to analyze stock data … a deep neural network-based stock trend prediction system and …

… For this, (2D) 2 PCA + Deep Neural Network (DNN) method … based on artificial intelligence
and machine learning [28]. In … choice of learning rate η and since the denominator is continual …

… at a more constant level (Mukherjee et al. 2021). In several cases… neural networks and
deep neural networks have shown superior forecasting performance compared to other machine …

… analysis method, which concentrates on previous stock prices and values. … analysis uses
historical charts and patterns to predict future prices. Stock markets were normally predicted by …