Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

Ranco, Gabriele; Bordino, Ilaria; Bormetti, Giacomo; Caldarelli, Guido; Lillo, Fabrizio; Treccani, Michele

doi:10.1371/journal.pone.0146576

Cited by 28 publications

(16 citation statements)

References 52 publications

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“…We can observe that the obtained connected components by DNL keep a relatively stable structure although the coinvestment patterns are evolving annually and this indicates that our method captures the effective evolving co-investment patterns. What's more, we further find out that the results of DNL in Figure 5 cover 60% of the top stocks mentioned by the news reports found in the related study [22]. This shows that our result follows the financial rule about the positive correlation between the transaction volume of a stock and the number of times that it is mentioned in the news media [23].…”

Section: E Discovering the Evolving Co-investment Patternssupporting

confidence: 80%

Deep Co-Investment Network Learning for Financial Assets

Wang¹,

Zhang²,

Wang³

et al. 2018

2018 IEEE International Conference on Big Knowledge (ICBK)

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Most recent works model the market structure of the stock market as a correlation network of the stocks. They apply pre-defined patterns to extract correlation information from the time series of stocks. Without considering the influences of the evolving market structure to the market index trends, these methods hardly obtain the market structure models which are compatible with the market principles. Advancements in deep learning have shown their incredible modeling capacity on various finance-related tasks. However, the learned inner parameters, which capture the essence of the finance time series, are not further exploited about their representation in the financial fields. In this work, we model the financial market structure as a deep co-investment network and propose a Deep Co-investment Network Learning (DeepCNL) method. DeepCNL automatically learns deep co-investment patterns between any pairwise stocks, where the rise-fall trends of the market index are used for distance supervision. The learned inner parameters of the trained DeepCNL, which encodes the temporal dynamics of deep co-investment patterns, are used to build the co-investment network between the stocks as the investment structure of the corresponding market. We verify the effectiveness of DeepCNL on the real-world stock data and compare it with the existing methods on several financial tasks. The experimental results show that DeepCNL not only has the ability to better reflect the stock market structure that is consistent with widely-acknowledged financial principles but also is more capable to approximate the investment activities which lead to the stock performance reported in the real news or research reports than other alternatives.

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Section: E Discovering the Evolving Co-investment Patternssupporting

confidence: 80%

Deep Co-Investment Network Learning for Financial Assets

Wang¹,

Zhang²,

Wang³

et al. 2018

2018 IEEE International Conference on Big Knowledge (ICBK)

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“…Nardo, Petracco-Giudici, and Naltsidis (2016) review the literature and conclude that while there is merit in using online news to predict changes in financial markets, the gains from implementing such an approach are usually less than 5%. However, Ranco et al (2016) find substantial benefit in coupling news sentiment with web browsing data. Some studies (Dhar, 2014;Kao, Shyu, & Huang, 2015;Zheludev, Smith, & Aste, 2014) have also incorporated non-traditional online sources of information such as social media, blogs, and forums, and proposed many questions for future research.…”

Section: Stock Market Prediction and Quantitative Modellingmentioning

confidence: 99%

Big data techniques in auditing research and practice: Current trends and future opportunities

Gepp¹,

Linnenluecke²,

O'Neill³

et al. 2018

Journal of Accounting Literature

237

150

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This paper analyzes the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

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“…Finally, The combination of public news with the browsing activity of the users of Yahoo! Finance to forecast intra-day and daily price changes of a set of 100 highly capitalised US stocks was explored in [43]. The work showed that, when taken alone, sentiment analysis or browsing activity have very small or no predictive power and uncovered a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news.…”

Section: Related Workmentioning

confidence: 99%

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Vilas

Redondo

Crockett

et al. 2018

Multimed Tools Appl

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There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange

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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

Cited by 28 publications

References 52 publications

Deep Co-Investment Network Learning for Financial Assets

Deep Co-Investment Network Learning for Financial Assets

Big data techniques in auditing research and practice: Current trends and future opportunities

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

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