Informativeness of the market news sentiment in the Taiwan stock market

Wei, Yu‐Chen; Lu, Yang-Cheng; Chen, Jen-Nan; Hsu, Yen-Ju

doi:10.1016/j.najef.2016.10.004

Cited by 22 publications

(24 citation statements)

References 33 publications

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“…(a), Kraussl and Mirgorodskaya () and Wei et al . (). Xiong and Bharadwaj () used VAR in addition to dynamic panel generalised method of moments (GMM) estimate of the same equation.…”

Section: Econometric Techniquesmentioning

confidence: 97%

“…‘PB’, ‘TO’ and ‘MV’ represent price‐to‐book ratio, turnover and market value, respectively, as defined in Wei et al . (). Sorting quantiles and their associated ranking variables are listed in the order in which they were performed.…”

Section: Econometric Techniquesmentioning

confidence: 97%

“…() and Wei et al . (). Heston and Sinha () generate cross‐sectional portfolios but do not specify whether results reflect average calendar‐time or event‐time.…”

Section: Econometric Techniquesmentioning

confidence: 97%

“…These aspects allow researchers to study the impacts of news content aggregated over varying scales of time and entity. For example, researchers have studied the influence of news at the firm level (Tetlock et al ., ; Ferguson et al ., ), industry level (Li et al ., ; Smales, ), market level (Tetlock, ; Wei et al ., ), commodity level (Clements and Todorova, ; Maslyuk‐Escobedo et al ., ), between currencies (Nassirtoussi et al ., ), across countries (Griffin et al ., ) and at the global market level (Uhl et al ., ). Time horizons vary from intraday (Groß‐Klußmann and Hautsch, ; Ho et al ., ), daily (Tetlock, ; Garcia, ), weekly (Lu and Wei, ; Sinha, ), monthly (Ammann et al ., ; Cahan et al ., ) and over several years (Hillert et al ., ; Kraussl and Mirgorodskaya, ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 3 more Smart Citations

News media analytics in finance: a survey

Marty

Vanstone

Hahn³

2019

Accounting & Finance

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We survey the works applying text analytics to the study of news media in financial markets beyond intraday horizons, and expand into the fundamental economic theory and concepts relevant to the field. We compare and contrast the news sources, textual analysis methods and empirical modelling approaches adopted within the literature. We distil and categorise the key empirical insights, and summarise the bibliographic history of the literature so far. While this rapidly growing field has yielded many exciting discoveries, there are a number of promising avenues for future research which will only benefit from continued advances in computational technology.

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“…(a), Kraussl and Mirgorodskaya () and Wei et al . (). Xiong and Bharadwaj () used VAR in addition to dynamic panel generalised method of moments (GMM) estimate of the same equation.…”

Section: Econometric Techniquesmentioning

confidence: 97%

Section: Econometric Techniquesmentioning

confidence: 97%

“…() and Wei et al . (). Heston and Sinha () generate cross‐sectional portfolios but do not specify whether results reflect average calendar‐time or event‐time.…”

Section: Econometric Techniquesmentioning

confidence: 97%

Section: Theoretical Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

News media analytics in finance: a survey

Marty

Vanstone

Hahn³

2019

Accounting & Finance

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“…Many state of the art methods analyze sentiments in news and comments on social media to predict stock price [23,24]. Besides, stock quantitative data is also an important factor to the prediction [10,25].…”

Section: Introductionmentioning

confidence: 99%

Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

Liu¹,

Lu²,

Du³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can't be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks' news sentiment, the focal stock's news sentiment and the focal stock's quantitative features to predict the focal stock's price movement. Results show that our method has an improvement of 8.1% compared with the traditional method.

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Estimate of the Impact of Trade Sanctions on the Russian Financial Markets

Rychkov

2023

Science and Global Challenges of the 21st Century – Innovations and Technologies in Interdisciplinary Applications

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Informativeness of the market news sentiment in the Taiwan stock market

Cited by 22 publications

References 33 publications

News media analytics in finance: a survey

News media analytics in finance: a survey

Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

Estimate of the Impact of Trade Sanctions on the Russian Financial Markets

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