Forecasting the Tehran Stock market by Machine ‎Learning Methods using a New Loss Function

نویسندگان

  • Hassan Doosti Department of Mathematics and Statistics, Macquarie University, Sydney, Australia
  • Mahsa Tavakoli Department of mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده

Stock market forecasting has attracted so many researchers and investors that ‎many studies have been done in this field. These studies have led to the ‎development of many predictive methods, the most widely used of which are ‎machine learning-based methods. In machine learning-based methods, loss ‎function has a key role in determining the model weights. In this study a new loss ‎function is introduced, that has some special features, making the investing in the ‎stock market more accurate and profitable than other popular techniques. To ‎assess its accuracy, a two-stage experiment has been designed using data of ‎Tehran Stock market. In the first part of the experiment, we select the most ‎accurate algorithm among some of the well-known machine learning algorithms ‎based on artificial neural network, ANN, support vector machine, SVM. In the ‎second stage of the experiment, the various popular loss functions are compared ‎with the proposed one. As a result, we introduce a new neural network using a ‎new loss function, which is trained based on genetic algorithm. This network has ‎been shown to be more accurate than other well-known and common networks ‎such as long short-term memory (LSTM) for both train and test data.‎

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عنوان ژورنال:

دوره 6  شماره 2

صفحات  1- 24

تاریخ انتشار 2021-04-01

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