Exchange rates change, asset-denominated currency difference and stock price fluctuation

Zheng, Luyuan; Jiang, Yuexiang; Long, Huaigang

doi:10.1080/00036846.2019.1624920

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…erefore, based on this network model, we can more fully and comprehensively analyze the contagion mechanism of stock price fluctuation risk under the influence of media sentiment and government supervision strategy. (3) In this paper, some interesting and practical conclusions are obtained: Firstly, market noise has a restraining effect on media sentiment and government supervision strategy. Secondly, the stock price fluctuation risk is inherent risk in the stock market, which cannot be eliminated by regulating media sentiment and government supervision strategy.…”

Section: Introductionmentioning

confidence: 89%

“…Chen et al [2] study how the stock information in the financial market can predict the stock price fluctuation and find that the effective use of market information can help to accurately predict the stock price fluctuation. Zheng et al [3] study the impact of exchange rate on stock price fluctuation and find that although the government intervention strategy of foreign exchange helps to stabilize the exchange rate, it often increases stock price fluctuation. ese studies show that not only the information in the financial market has an important predictive value for stock price fluctuation but also the media sentiment and government supervision strategy based on the information have an important impact on stock price fluctuation.…”

Section: Introductionmentioning

confidence: 99%

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Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

Ju-fang

2021

Discrete Dynamics in Nature and Society

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From the cross perspective of communication science and administration management, based on complex network theory, this paper constructs a model of stock price fluctuation risk contagion, which comprehensively considers media sentiment and government supervision strategy, and deeply analyzes the contagion mechanism of stock price fluctuation risk under the interaction of media sentiment and government supervision strategy. The main conclusions are as follows: The stock association network established by random way is more likely to cause contagion of stock price fluctuation risk. Media sentiment tendency, media sentiment intensity, and media attention persistence have positive “U” relationship, inverted “U” relationship, and positive correlation with contagion intensity of stock price fluctuation risk, respectively. There is a negative correlation between the strength, persistence, and timeliness of government supervision and the contagion intensity of stock price fluctuation risk. There is a positive correlation between market noise and contagion intensity of stock price fluctuation risk, and market noise has a restraining effect on media sentiment and government supervision strategy. In addition, the stock price fluctuation risk is inherent risk in the stock market, which cannot be eliminated by adjusting media sentiment and government supervision strategy, but its contagion intensity can be effectively controlled.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

Ju-fang

2021

Discrete Dynamics in Nature and Society

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show abstract

“…In addition, stock prices are also affected by many other factors. For instance, Zheng et al (Zheng, Jiang, & Long, 2019) studied the relationship between exchange rates and stock prices on the Hong Kong stock market. They built a model to analyze the correlation between Hong Kong company stocks, mainland company stocks and exchange rates.…”

Section: Related Workmentioning

confidence: 99%

A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System

Sun

Srivastava

et al. 2022

International Journal of Data Warehousing and Mining

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This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

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“…Besides, stock prices are also affected by many other factors. For instance, Zheng et al [38] studied the relationship between exchange rates and stock prices on the Hong Kong stock market. ey built a model to analyze the correlation between Hong Kong company stocks, mainland company stocks, and exchange rates.…”

Section: Related Workmentioning

confidence: 99%

A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

Sun

Srivastava

et al. 2021

Mobile Information Systems

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The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

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Exchange rates change, asset-denominated currency difference and stock price fluctuation

Cited by 10 publications

References 43 publications

Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System

A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

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