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...
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...
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...
Environmental science and pollution research international
Feb 19, 2020
With the rapid development of social economy and the increasing environmental pollution in China, the government's environmental investment has been gradually increasing, and an effective evaluation system is required to study the benefits from envir...
Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies e...
International journal of environmental research and public health
Sep 25, 2019
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...
International journal of environmental research and public health
Sep 24, 2019
The purpose of this paper is to develop a fuzzy model of the risk assessment for environmental start-up projects in the air transport sector at the stage of business expansion. The model developed for the following software will be a useful tool for ...
Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper ...
In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's v...
Computational intelligence and neuroscience
Feb 20, 2017
The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today's markets experience intense activity i...
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