Klaudia Kaczmarczyk

TL;DR: In this paper, a comprehensive experimental comparison of different spiking neural networks in predicting ETF values is presented, where the main goal was to check if the spiking networks obtain better or worse results of forecasting than traditional neural networks.

TL;DR: In this article, the authors developed the machine learning models for predicting the stock market liquidity in the Vietnamese stock market, focusing on the recent years from 2011 to 2019, and the results of research can be used for developing the methods for decision support on stock markets.

TL;DR: The aim of this paper is to develop the Supervised Learning method based on the random forest algorithm for decision support on stock exchange by determining which of the most popular technical analysis indicators have the greatest predictive power for a successful transaction with the feature importance method.

TL;DR: The aim of this paper is the development of the Supervised Learning method based on machine learning algorithms, and the best results were achieved by the Logistic Regression.

TL;DR: In this article , the authors examined the effectiveness of selected machine learning algorithms and to find the best one for stock data by comparing several selected algorithms using a backtesting environment on the same data sets and general parameters.