Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index

Lin, Yong; Wang, Renyu; Gong, Xingyue; Jia, Guozhu

doi:10.1016/j.physa.2022.127686

Cited by 11 publications

(4 citation statements)

References 50 publications

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“…We choose q such that {q : q ∈ [−10, 10], q ∈ Z} and set the time scale as 10 ≤ s ≤ N/2. The minimum value of time scale s is set to 10 to avoid incorrect results in polynomial fitting on local trends for s < 10 [64,65]. For the upper bound on s, we set the maximum value as N/2 to showcase very long-range cross-correlation in the series.…”

Section: Findings and Discussionmentioning

confidence: 99%

Multifractal Detrended Cross-Correlations between Green Bonds and Commodity Markets: An Exploration of the Complex Connections between Green Finance and Commodities from the Econophysics Perspective

Acikgoz,

Gokten,

Soylu

2024

Fractal Fract

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Green bonds represent a compelling financial innovation that presents a financial perspective solution to address climate change and promote sustainable development. On the other hand, the recent process of financialisation of commodities disrupts the dynamics of the commodity market, increasing its correlation with financial markets and raising the risks associated with commodities. In this context, understanding the dynamics of the interconnectivity between green bonds and commodity markets is crucial for risk management and portfolio diversification. This study aims to reveal the multifractal cross-correlations between green bonds and commodities by employing methods from statistical physics. We apply multifractal detrended cross-correlation analysis (MFDCCA) to both return and volatility series, demonstrating that green bonds and commodities exhibit multifractal characteristics. The analysis reveals long-range power-law cross-correlations between these two markets. Specifically, volatility cross-correlations persist across various fluctuations, while return series display persistence in small fluctuations and antipersistence in large fluctuations. These findings carry significant practical implications for hedging and risk diversification purposes.

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Section: Findings and Discussionmentioning

confidence: 99%

Multifractal Detrended Cross-Correlations between Green Bonds and Commodity Markets: An Exploration of the Complex Connections between Green Finance and Commodities from the Econophysics Perspective

Acikgoz,

Gokten,

Soylu

2024

Fractal Fract

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show abstract

“…Feng et al 14 found that the increase in Internet attention is conducive to the development of enterprises by matching China's A‐share data and Internet search index. Lin et al 15 found that online public attention can improve the prediction accuracy of USD/RMB exchange rate earnings. Zhang et al 16 conducted in‐depth mining through the social network attention factor and LDA topic model.…”

Section: Related Workmentioning

confidence: 99%

Relationship modeling and short‐term prediction analysis between public attention and teaching research

Zhao

Liu

et al. 2022

Engineering Reports

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Analyzing the relationship between Internet attention and teaching research can provide a reference for extracting and identifying research hot spots in the discipline. Currently, many kinds of literature obtain social hot‐spot information based on a single platform, such as the Baidu Index, China National Knowledge Infrastructure (CNKI), and Web of Science. Still, they ignore the common information reflected between the Baidu Index platform and the professional academic platform and the lack of relationship demonstration between them. Therefore, based on the CNKI database and Baidu Index platform, the most critical data statistical analysis service platform in the Internet era, this article proposes a relationship modeling and prediction framework (RMPF). First, RMPF analyzes the relationship between public attention and teaching research from the perspective of “high school information technology” by word frequency analysis and Pearson. Second, RMPF constructs the relationship model between them using leave‐one‐out cross‐validation. Third, RMPF predicts the development of subject teaching and research using the autoregressive integrated moving average model and multiple regressions. The results show that the correlation coefficient between public attention and teaching research is more than 0.65 from the perspective of “high school information technology,” showing a strong positive correlation. In recent years, the focus of high school information technology discipline tends to be integrating multimedia technology, cloud classroom, artificial intelligence, and other technologies and disciplines. In addition, discipline literature volume growth will slow in the next 2 years, with an average annual publication of 350 articles. The overall number of literature decreased by about 120 compared with 2020. Based on the proposed RMPF, this study clarifies the relationship between subject attention and teaching research through experimental demonstration and provides an implementation framework reference for researchers of relevant literature.

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“…The first to propose this study was Ginsberg et al [30], who used Google's massive user search data to accurately predict the trend of the proportion of influenza-like cases in the United States one week in advance in the "Google Flu Trends" software developed by Google in 2008. Since then, WSD has been widely used in major research fields such as economics and medicine [31][32][33]. In the house price prediction problem, a study by Wu and Brynjolfsson [34] found that the Google Home search index has good predictive power for real estate market sales and prices.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of House Price Index in First‐Tier Cities in China Based on Heterogeneous Integrated Learning Model

Mao

Duan

Guo

et al. 2022

Journal of Mathematics

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To address the difficulty of low prediction accuracy, insufficient model stability, and certain lag associated with a single machine learning model in the prediction of house price, this paper proposes a multimodel fusion house price prediction model based on stacking integrated learning. Firstly, web search data affecting house prices were collected by web crawler technology, and Spearman correlation analysis was performed on the attribute set to reduce its complexity and establish a prediction index system for four first-tier cities in China. Secondly, with the goal of improving accuracy, diversity, and generalization ability, the types of base learners as well as metalearners are determined, and the parameters of the base learners are optimized using the grey wolf optimization algorithm to produce the GWO-stacking model, and the experimental results from four datasets demonstrate that the model has high prediction accuracy. Finally, to solve the issue of unintelligible black boxes in machine learning models, we have used the state-of-the-art interpretation method SHAP combined with the LightGBM algorithm to interpret the model, and the result can be used as a basis for real estate policy planning and adjustment and even guide the demand of home buyers, thus improving the efficiency and effectiveness of government policy making.

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Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index

Cited by 11 publications

References 50 publications

Multifractal Detrended Cross-Correlations between Green Bonds and Commodity Markets: An Exploration of the Complex Connections between Green Finance and Commodities from the Econophysics Perspective

Multifractal Detrended Cross-Correlations between Green Bonds and Commodity Markets: An Exploration of the Complex Connections between Green Finance and Commodities from the Econophysics Perspective

Relationship modeling and short‐term prediction analysis between public attention and teaching research

A Study on the Prediction of House Price Index in First‐Tier Cities in China Based on Heterogeneous Integrated Learning Model

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