Daily prediction of Major Stock Indices from Textual WWW Data

Wüthrich, Beat; Permunetilleke, D.; Leung, Siu Fai; Lam, Wing-Shing; Cho, Vincent; Zhang, J

doi:10.1080/1023697x.1998.10667783

Cited by 51 publications

(46 citation statements)

References 4 publications

(4 reference statements)

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“…Their conclusions are somewhat contradictory to those of Wüthrich et al (1998) as for the causality of the correlation. According to them, messages were not predicting or influencing the market and, "if anything, the causality appears to run from the market to the financial forums."…”

Section: Stock Market Movementcontrasting

confidence: 51%

“…Probably reflecting its financial importance, research on predicting the stock market from Web data largely predates the existence of user-generated content. An early work by Wüthrich et al (1998) exploited contents from online financial newspapers to predict the closing values of stock markets in Asia, Europe and America. They find that a simple trading strategy informed by their prediction method -even when making a number of wrong predictions-would obtain a larger capital appreciation than that by the analyzed stock markets.…”

Section: Stock Market Movementmentioning

confidence: 99%

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The power of prediction with social media

et al. 2013

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Social media today provide an impressive amount of data about users and their societal interactions, thereby offering computer and social scientists, economists, and statisticiansamong others-many new opportunities for research exploration. Arguably, one of the most interesting lines of work is that of predicting future events and developments based on social media data, as we have recently seen in the areas of politics, finance, entertainment, market demands, health, etc. In fact, an average of one in seven research papers presented at the WWW, But what can be successfully predicted and why? Since the first algorithms and techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains.Better understanding the predictive power and limitations of social media is therefore of utmost importance, in order to be successful and avoid false expectations, misinformation or unintended consequences. Today, current methods and techniques are far from being well understood, and it is mostly unclear to what extent or under what conditions the different methods for prediction can be applied to social media. While there exists a respectable and growing amount of literature in this area, current work is fragmented, characterized by a lack of commonly accepted evaluation approaches. Yet, this research seems to have reached a sufficient level of interest and relevance to justify a dedicated section.This special section aims to shape a frame of important questions to be addressed in this field, and fill the gaps in current research with presentations of early research on algorithms, techniques, methods and empirical studies aimed at the prediction of future or current events based on user-generated content in social media.2

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Section: Stock Market Movementcontrasting

confidence: 51%

Section: Stock Market Movementmentioning

confidence: 99%

The power of prediction with social media

et al. 2013

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“…The previous attempts have mainly focused on the prediction of movements in stock prices or estimates of sales volume [3], [4], [19]. For example, Wuthrich et al [19] proposed a method of predicting stock indices using historical news about companies and past information on stock indices as training data. Choudhury et al [3] tried to predict changes in stock prices based on blog communication patterns.…”

Section: Related Workmentioning

confidence: 99%

Query Expansion and Text Mining for ChronoSeeker-Search Engine for Future/Past Events-

Kawai

Jatowt

Tanaka

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.

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“…Nowadays, news come almost exclusively via web sources in the form of text. This is the reason why many researchers have proposed methods that use text information for analysing the stock market leading to the establishment of an entirely new sub-field of data mining called text mining (Fawcett and Provost 1999;Permunetilleke and Wong 2002;Thomas and Sycara 2000;Wuthrich et al 1998).…”

Section: Introductionmentioning

confidence: 99%

UsingWords from Daily News Headlines to Predict the Movement of Stock Market Indices

Kavšek¹

2017

MGT

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Stock market analysis is one of the biggest areas of interest for text mining. Many researchers proposed different approaches that use text information for predicting the movement of stock market indices. Many of these approaches focus either on maximising the predictive accuracy of the model or on devising alternative methods for model evaluation. In this paper, we propose a more descriptive approach focusing on the models themselves, trying to identify the individual words in the text that most affect the movement of stock market indices. We use data from two sources (for the past eight years): the daily data for the Dow Jones Industrial Average index ('open' and 'close' values for each trading day) and the headlines of the most voted 25 news on the Reddit WorldNews Channel for the previous 'trading days. ' By applying machine learning algorithms on these data and analysing individual words that appear in the final predictive models, we find that the words gay, propaganda and massacre are typically associated with a daily increase of the stock index, while the word iran mostly coincide with its decrease. While this work presents a first step towards qualitative analysis of stock market models, there is still plenty of room for improvements.

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Daily prediction of Major Stock Indices from Textual WWW Data

Cited by 51 publications

References 4 publications

The power of prediction with social media

The power of prediction with social media

Query Expansion and Text Mining for ChronoSeeker-Search Engine for Future/Past Events-

UsingWords from Daily News Headlines to Predict the Movement of Stock Market Indices

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