Textual analysis for China’s financial markets: a review and discussion

Huang, Alan Guoming; Wu, Wen Jian; Yu, Tong

doi:10.1108/cfri-08-2019-0134

Cited by 28 publications

(15 citation statements)

References 31 publications

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“…From the insurance sector's perspective, the sales of insurance were severely impacted, and the sales of traditional insurance products fell sharply. The securities sector was affected by market sentiment, and herein, trading volume, investment, and financing activities fluctuated greatly ( Agarwal and Chua, 2020 ; Huang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Does COVID-19 matter for systemic financial risks? Evidence from China's financial and real estate sectors

Huang

Cheng²,

Xu³

et al. 2022

Pacific-Basin Finance Journal

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Section: Introductionmentioning

confidence: 99%

Does COVID-19 matter for systemic financial risks? Evidence from China's financial and real estate sectors

Huang

Cheng²,

Xu³

et al. 2022

Pacific-Basin Finance Journal

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“…(6) More new analytic approaches appropriate for big data such as data collected from Internet and other sources at large scales can be used. For example, studies using textual analyses for consumer finance research topics can be encouraged (Huang et al , 2020).…”

Section: Discussionmentioning

confidence: 99%

Consumer finance/household finance: the definition and scope

Xiao

Tao

2020

CFRI

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PurposeThe purpose of this literature review paper is to define consumer finance, describe the scope of consumer finance and discuss its future research directions.Design/methodology/approachIn this paper, consumer finance is used as a synonym of household finance. Consumers refer to individuals and families. After defining the term “consumer finance,” we conducted a critical review of consumer finance as an interdisciplinary research field in terms of money managing, insuring, borrowing and saving/investing. Future research directions are also discussed.FindingsThis paper discusses similarities and differences among several terms such as consumer finance, household finance, personal finance, family finance and behavioral finance. The paper also reviewed key studies on consumer financial behavior around four key financial functions, namely, money management, insurance, loan and saving/investment and several nontraditional topics such as fintech and financial capability/literacy. The paper also introduced several datasets of consumer finance commonly used in the United States and China.Originality/valueThis paper clarified several similar terms related to consumer finance and sorted out the diverse literature of consumer finance in multiple disciplines such as economics, finance and consumer science, which provide a foundation for generating more fruitful research in consumer finance in the future.

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“…Further, the impact of certain non-financial news like the spread of "Coronavirus" across various continents as lead articles have also recently shown significant effects on the relationship between sentiment and attraction to headlines. In Huang et al (2019), a literature survey focusing on the latest developments in textual analysis on China's financial markets is highlighted, showing its distinction from US-based similar studies. The study concludes that there is "limited evidence on the association between sentiment and other contemporaneous or future returns."…”

Section: Importance Of Social Media In Investor Sentiment Analysismentioning

confidence: 99%

Prediction of the Stock Market From Linguistic Phrases

Eachempati¹,

Srivastava

2023

Journal of Database Management

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Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future markets' prediction factoring in the sentiment of investors and algorithmic traders is an exciting area for research with deep learning techniques emerging to understand the market and its future direction. The paper develops two FINBERT deep neural network models pre-trained on the financial phrase dataset, the first one to extract sentiment from the NSE market news. The second model is adopted to predict the stock market movement of NSE with the above sentiment, historical stock prices, return on investment, and risk as predictors. The accuracy is compared with RNN and LSTM and baseline machine learning classifiers like naïve bayes and support vector machine (SVM). The accuracy of the FINBERT model is found to out-perform the deep learning algorithms and above baseline machine learning classifiers thus justifying the importance of the FINBERT model in stock market prediction.

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Textual analysis for China’s financial markets: a review and discussion

Cited by 28 publications

References 31 publications

Does COVID-19 matter for systemic financial risks? Evidence from China's financial and real estate sectors

Does COVID-19 matter for systemic financial risks? Evidence from China's financial and real estate sectors

Consumer finance/household finance: the definition and scope

Prediction of the Stock Market From Linguistic Phrases

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