Stock price prediction using financial news articles

Kaya, M. I. Yasef; Karslıgil, M. Elif

doi:10.1109/icife.2010.5609404

Cited by 40 publications

(21 citation statements)

References 4 publications

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“…Exploration of existing strategies gives better perceiveability in the current scenario; the related work gives a basic outline of various methodologies explored for problem arrangement. Kaya et al, [1] proposed a system to predict the stock price movement using rich online textual information extracted from the financial news articles. They classified financial news articles using support vector machines method and achieved an accuracy of 61%.…”

Section: Related Workmentioning

confidence: 99%

Behavior Prophecy of Stock Trader using Machine Learning Techniques

Gowda¹,

Lakshmikantha²

2019

IJITEE

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 Abstract: The web utilization by users is expanding very rapidly. Users are getting to data and administrations effectively through different media like social correspondence, sight and sound substance, web based trading, banking administrations and so forth. It winds up provoking undertaking to precisely recognize and separate typical and suspicious human behavior conduct. Every unique application need to predict user behavior to forecast and upgrade their administration quality. This work gives the examination of stock trader conduct recognition and expectation. Many Machine Learning (ML) methods and recognizable proof strategies are looked at and examined for stock trader behavior analysis. Their parameters are considered and enhancements are recommended. The proposed procedure portrays stock trader conduct discovery framework. The vital segment examination is the classification and prediction technique used to recognize and understand the typical and irregular behavior of the stock trader.

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Section: Related Workmentioning

confidence: 99%

Behavior Prophecy of Stock Trader using Machine Learning Techniques

Gowda¹,

Lakshmikantha²

2019

IJITEE

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“…Research on stock prediction using financial news content is also relevant to our work [13,23,28]. One of its goals is to find the most predictive words and label the news according to their effect on the stock prices for that specific day.…”

Section: Related Workmentioning

confidence: 99%

Information Retrieval with Time Series Query

Kim

Nikitin

Zhai

et al. 2013

Proceedings of the 2013 Conference on the Theory of Information Retrieval

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We study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant documents in a text collection of the same time period, which contain topics that are correlated with the query time series. This retrieval problem arises in many text mining applications where there is a need to analyze text data in order to discover potentially causal topics. To solve this problem, we propose and study multiple retrieval algorithms that use the general idea of ranking text documents based on how well their terms are correlated with the query time series. Experiment results show that the proposed retrieval algorithm can effectively help users find documents that are relevant to the time series queries, which can help users analyze the variation patterns of the time series.

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“…Time frames vary between next 20 minutes to up to next month. Works such as , (Hagenau et al,06), (Kaya and Karsligil, 2010), (Lauren and Harlili, 2014), (Mao et al, 2012) , (Patel, 2015), (Xu and Keelj, 2014), and (Vu et al, 2012) predict stock price direction for the next day. predict stock price direction for both the next day and next week.…”

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confidence: 99%

“…Related systems collected their input data from various sources and exchanges: (Schumaker and Chen, 2006), (Mao et al, 2012), and (Mao et al, 2013) collected stock news, Tweets and price charts related to S&P 500 companies. (Vu et al, 2012) collected Tweets and stock price data related to Nasdaq stocks, (Bollen et al, 2011) collected Tweets and stock price charts related to Dow Jones Industrial Average (DJIA), (Kaya and Karsligil, 2010) collected one year of data related to Microsoft company. (Xu and Keelj, 2014) collected stock price charts and tweets from a social media platform used (Nassirtoussi et al, 2015) collected currency price and news data related to foreign exchange market (Forex).…”

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confidence: 99%

“…(Patel, 2015) collected stock price data from CNX Nifty, S&P BSE Sensex exchanges and finally (Roy et al, 2015) collected thirteen years of stock price charts data related to Goldman Sachs Group Inc. (Gong and Sun, 2009), (Patel, 2015), and (Roy et al, 2015) used only stock price as input to predict stock price or direction with accuracies varying between 83% and 90%. (Hagenau et al,06), (Kaya and Karsligil, 2010), (Lauren and Harlili, 2014), and (Nassirtoussi et al, 2015) are examples of papers which utilize news as well as stock prices to predict price direction with varying accuracies between 51% and 83%. (Deng et al, 2011) used historical price and volume for three major technology companies AMZN, MSFT, and GOOG, with some features related to news, and comments like volume of news, and volume comments from Endaget microbloging site to predict next day price using multiple kernel learning (MKL), the performance of the proposed model in this paper outperforms the other presented methods under MAE, MSPE, and RMSE performance measures.…”

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confidence: 99%

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Directional prediction of stock prices using breaking news on Twitter

Alostad

Davulcu

2017

WEB

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Stock market news and investing tips are popular topics in Twitter. In this dissertation, first I utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website matching the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content.Next, I proceed to show that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the DJI stock prices mentioned in these articles. Secondly, I show that using document-level sentiment extraction does not yield a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features. Thirdly I test the performance of the system on several time-frames and identify the 4 hour time-frame for both the price charts and for Tweet breakout detection as the best time-frame combination. Finally, I develop a set of price momentum based trade exit rules to cut losing trades early and to allow the winning trades run longer. I show that the Tweet volume breakout based trading system with the price momentum based exit rules not only improves the winning accuracy and the return on investment, but it also lowers the maximum drawdown and achieves the highest overall return over maximum drawdown.

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Stock price prediction using financial news articles

Cited by 40 publications

References 4 publications

Behavior Prophecy of Stock Trader using Machine Learning Techniques

Behavior Prophecy of Stock Trader using Machine Learning Techniques

Information Retrieval with Time Series Query

Directional prediction of stock prices using breaking news on Twitter

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