Study on Exchange Rate Volatility under Cross-border RMB Settlement Based on Multi-layer Neural Network Algorithm

2019 ◽  
Vol 28 (1) ◽  
pp. 58-64
Author(s):  
Enyang Zhu
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Exchange Rate Volatility ◽  
Cross Border ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Estimation of Direction of Exchange Rate, Gold Price and Stock Market Returns with High Order Markov Chain Models

2016 ◽  
Author(s):  
Süleyman Bilgin Kılıç ◽  
Salih Çam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Transition Probabilities ◽  
Stock Market Returns ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Returns Forecasting ◽  
Artificial Neural Network Algorithm

This study uses a hybrid high order Markov Chains Model to predict direction of exchange rate, gold price and stock market returns with the Artificial Neural Network Algorithm as an estimator of transition probability matrix. Many forecasting techniques are used to examine the direction of returns forecasting in the literature such as Markov Chains Model and Artificial Neural Network Algorithm. In this study, it is aimed to combine these two techniques and to utilize the predict values of the Artificial Neural Network Algorithm for calculate transition probabilities matrix. Calculations show that the hybrid model gives high correct classification probabilities besides of well approximated transition probabilities. Returns series of USD/TRY exchange rate, closing price of Borsa Istanbul Stock Exchange and gold prices cover the period of 01/01/2003 and 31/01/2016. All series are obtained from database of Central Bank of Turkey. As a result, although the transition probabilities almost equal to 0.5 and so estimation of these series are not easy, the transition probabilities and correct classification probabilities gained from the hybrid model provide substantial information related to direction of returns forecasting. Besides, estimated model provide valuable information to individual investors and companies, and could help them to take position against to risks.

The Feasibility of the Intelligent Development of Contrastive Translation between English and Chinese based on the Development of E-commerce

2021 ◽  
Vol 7 (5) ◽  
pp. 2000-2011
Author(s):  
Weijie Liu
Keyword(s):  
Neural Network ◽  
Rapid Development ◽  
Word Alignment ◽  
Translation Model ◽  
Cross Border ◽  
Network Algorithm ◽  
Text Information ◽  
Neural Network Algorithm ◽  
The Cross ◽  
Two Stages

Objectives: At present, with the rapid development and application of the Internet, the cross-border transaction of e-commerce presents a blowout development, and the demand for language is increasing. Methods: In this paper, starting from the perspective of machine intelligent translation of English and Chinese, and in view of the problem of traditional contrastive translation of machine, the algorithm of strengthening neural network was used to solve the problem of translation. In the study, the process of intelligent translation was divided into two stages: encoding and decoding. In view of the language type and word alignment, the input and output modules were formed and the algorithm was optimized, and a recurrent neural network algorithm was used to build an RNN-embed intelligent translation model of English and Chinese. Results: The model was input through the character level in English and Chinese, and then the network was trained, so as to solve the problem that it is difficult to deal with the advanced semantics in the process of strengthening the neural network calculation of text information in the cross-border transaction of e-commerce. Conclusion: It is proved by experiments that the RNN-embed translation model based on the enhanced neural network algorithm can improve the quality of the long sentence translation compared with the machine translation.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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