Computational intelligence and neuroscience
Sep 22, 2021
Today, the global exchange market has been the world's largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this ...
Computational intelligence and neuroscience
Aug 19, 2021
Forward-looking forecasting of the inflation rate could help the central bank and other government departments to better use monetary policy to stabilize prices and prevent the impact of inflation on market entities, especially for low- and middle-in...
PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of dail...
Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as AN...
The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, a...
Neural networks : the official journal of the International Neural Network Society
Mar 17, 2021
Deep Reinforcement Learning (RL) is increasingly used for developing financial trading agents for a wide range of tasks. However, optimizing deep RL agents is notoriously difficult and unstable, especially in noisy financial environments, significant...
INTRODUCTION: The fusion of fractional order differential equations and fuzzy numbers has been widely used in modelling different engineering and applied sciences problems. In addition to these, the Allee effect, which is of high importance in field ...
Construction of a reliable stock portfolio remains an open issue in quantitative investment. Multiple machine learning models have been trained for stock portfolio selection, but their practical applicability remains limited due to the challenges pos...
In five experiments (N = 3,828), we investigate whether people prefer investment decisions to be made by human investment managers rather than by algorithms ("robos"). In all of the studies we investigate morally controversial companies, as it is pla...
Despite active research on trading systems based on reinforcement learning, the development and performance of research methods require improvements. This study proposes a new action-specialized expert ensemble method consisting of action-specialized...
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