AIMC Topic: Models, Economic

Clear Filters Showing 41 to 50 of 78 articles

Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization.

IEEE transactions on neural networks and learning systems
Portfolio selection is one of the important issues in financial investments. This article is concerned with portfolio selection based on collaborative neurodynamic optimization. The classic Markowitz mean-variance (MV) framework and its variant mean ...

Impact of chart image characteristics on stock price prediction with a convolutional neural network.

PloS one
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...

Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.

PloS one
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...

A machine learning approach to predict healthcare cost of breast cancer patients.

Scientific reports
This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients ...

Innovative deep matching algorithm for stock portfolio selection using deep stock profiles.

PloS one
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...

Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning.

PloS one
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...

Recurrent convolutional neural kernel model for stock price movement prediction.

PloS one
Stock price movement prediction plays important roles in decision making for investors. It was usually regarded as a binary classification task. In this paper, a recurrent convolutional neural kernel (RCNK) model was proposed, which learned complemen...

Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data.

PloS one
We propose a novel method for training neural networks to predict the future prices of stock indexes. Unlike previous works, we do not use target stock index data for training neural networks for index prediction. Instead, we use only the data of ind...

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

PloS one
To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benef...

Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport.

International journal of environmental research and public health
The purpose of this paper is to develop an applied fuzzy model of information technology to obtain quantitative estimates of environmental start-up projects in air transport. The developed model will become a useful tool for venture funds, business a...