The direction of media influence: Real-estate news and the stock market

Walker, Clive

doi:10.1016/j.jbef.2016.02.001

Cited by 42 publications

(24 citation statements)

References 26 publications

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“…Li (2006) and Tetlock (2007) indicated that media information contains sentimental content that affects investment behavior. Other researchers (Bollen et al , 2011; Yu et al , 2013; Smailović et al , 2014; Walker, 2016) also showed that social media affect stock market volatility. The literature also shows that news articles have significant effects on the stock market (Cutler et al , 1989; Veronesi, 1999; Chan, 2003; Schumaker et al , 2012; Medovikov, 2016).…”

Section: Introductionmentioning

confidence: 91%

The association between stock price volatility and financial news – a sentiment analysis approach

Seng

Yang

2017

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Purpose The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility. Design/methodology/approach An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility. Findings The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation. Research limitations/implications Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis. Practical implications Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets. Originality/value The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

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Section: Introductionmentioning

confidence: 91%

The association between stock price volatility and financial news – a sentiment analysis approach

Seng

Yang

2017

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“…According to Walker (2016), the media can drive market behavior. In addition, companies' activity on Twitter can reduce the expected negative reactions in the market (Mazboudi and Khalil 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Guijarro

Clemente

Saleemi

2021

JRFM

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Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool, is priced along with market liquidity dimensions. The analysis is based on the Australian Securities Exchange (ASX), and from the results, we conclude that microblogging content in pessimistic periods has a higher impact on liquidity and its dimensions. On a daily basis, pessimistic investor sentiments lead to higher trading costs, illiquidity, a larger price dispersion and a lower trading volume.

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“…The application of natural language-based techniques to the real estate field is rather new. Besides the works of Soo (2014) and Walker (2014Walker ( , 2016, it is our understanding that no other studies have analysed this topic using a similar set of tools. However, the application of text analysis with word lists has been employed in various fields such as marketing (see He, 2012;Chen et al, 2016) and political science (Greene and Haber, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring sentiment in real estate – a comparison study

Heinig

Nanda

2018

JPIF

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Purpose -In mainstream economics and finance literature, market sentiment is considered "irrational". This leads to significant challenges in capturing the effect of sentiment on economic relationships. Real estate is even more complex due to the fact that the sector exhibits several market inefficiencies. The purpose of this paper is to explore the literature and present a simple test for the potential of using three different sentiment indicators to improve a basic cap rate model. The authors establish the case using commercial real estate (CRE) data for London West End. Design/methodology/approach -The three indicators differ in their underlying source and method. The authors used orthogonalisation and principal component analysis for a macroeconomic sentiment indicator. Furthermore, online search volume data have been used to mirror the market sentiment for the London West End market. Finally, textual analysis based on word lists has been applied to corpus of market reports. Findings -The results indicate considerable improvement in the authors' ability to capture the effect of sentiment. Furthermore, the consideration of a human factor leads to improvement in the basic yield model. Practical implications -The methods suggest that sentiment extracted from more forward-looking sources, such as online searches, could be a significant information gain for investors, lenders or other market participants. The additional information could be used to adjust their behaviour within the market. Originality/value -To the authors' knowledge, this is the first study that applies textual analysis to market reports for the CRE market in the UK.

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The direction of media influence: Real-estate news and the stock market

Cited by 42 publications

References 26 publications

The association between stock price volatility and financial news – a sentiment analysis approach

The association between stock price volatility and financial news – a sentiment analysis approach

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Measuring sentiment in real estate – a comparison study

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