Automated news reading: Stock price prediction based on financial news using context-capturing features

Hagenau, Michael; Liebmann, Michael; Neumann, Dirk

doi:10.1016/j.dss.2013.02.006

Cited by 279 publications

(136 citation statements)

References 15 publications

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“…In [20], Mittermayer suggested to divide the textual pre-processing into three major steps: extraction, selection and representation of features. This terminology was then employed in subsequent works [1].…”

Section: 3! Textual Pre-processingmentioning

confidence: 99%

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Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Shynkevich

McGinnity

Coleman

et al. 2016

Decision Support Systems

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-The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched.However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category.News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories.

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Section: 3! Textual Pre-processingmentioning

confidence: 99%

“…The documents are often classified into two (negative and positive), e.g. in [1] [21], or three (negative, neutral and positive) categories, e.g. in [20], classes depending on their impact on an asset price.…”

Section: " "mentioning

confidence: 99%

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Shynkevich

McGinnity

Coleman

et al. 2016

Decision Support Systems

View full text Add to dashboard Cite

show abstract

“…[7] investigates whether public sentiment can be used to predict the stock market, used tools like: OpinionFinder and GPOMS to measure variations in the public mood from tweets submitted on daily basis. [10] made an analysis on individual behavior rather than the micro-blogs created by a individual user, considered original words and retweet others' tweets for predicting future vitality. [4] had proposed Implicit Social Trust and Sentiment (ISTS) based Recommander System RS on Online Social Networks (OSNs) by utilizing the implicit trust between friends and the sentiment they hold in their posts.…”

Section: Related Workmentioning

confidence: 99%

“…Regression Algorithms [10] implemented Regression algorithms considered syntax and semantics for classification. Naive Bayes [6] implemented Naive Bayes, used Open-source natural language toolkit (NLTK) software for classification.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Weighted Fuzzy Rule Based Sentiment Prediction Analysis on Tweets

Basha¹,

Rajput²,

Iyengar³

et al. 2017

Advanced Science and Technology Letters

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Abstract.As E-Commerce is becoming more popular, the number of product reviews that a product received grows exponentially. In this context, others' opinions will play a vital role to make a decision to select among multiple options involves valuable resources like money and time, where people usually depend on their peers' past experiences in the form of reviews. Many companies use opinion mining and sentiment analysis as part of their research. Detecting sentiment in social media like Facebook, Twitter, online blogs and reviews have become an essential task as they have been influenced every business organization. In this paper, we would like to analyze the Fuzzy rulebased systems (FRBSs) with FRBS models, namely: Mamdani, and Takagi Sugeno Kang (TSK) using FRBS package in R, and a comparison with other common classification methods. Additionally, the performance of FRBSs method is measured in terms of precision, Recall and F-measure to find accuracy.

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“…We focus our study to the Nikkei 225 index and the US dollar/Yen exchange rate in order to develop an intelligent agent trader in the future. Past researches also showed a correlation between the time series data (stock and foreign exchange market) and the news data which can be used to improve the accuracy [3], [4], [5]). …”

Section: Introductionmentioning

confidence: 99%

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2017

IJBM

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Abstract:In this paper, we present the result of our experiment on analyzing a time series data such as the Nikkei 225 index or foreign currency exchange USD/JPY past data and text corpus related to Japanese economy and finance news or reports. There are several researches reported that forecasting of time series with additional features based on text data can be beneficial rather than relying on time series data history only. Experts on investment are usually making their decision based on those text data as they found patterns on them called the fundamental analysis. Time series analysis based on past history only are called technical analysis. The combination of them should make a better prediction system. The text data also provides an explanation or indication to the trend patterns (uptrend, downtrend, or trend reversal), important moves (spikes, releases) and/or volatility. We collected all Nikkei 225 index and USD/JPY past data on daily average closing prices, all Bank of Japan monthly reports and all related available news text data. We applied on the time series Nikkei 225 data the technical analysis such as simple moving average (SMA), regression, ARIMA models and seasonal return rates, GARCH model combined with text data features such as topic models. We aggregate topics yearly or a long period of 5 to 10 years. Topics are extracted with topic modeling techniques such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF). Through the observations of those technical and fundamental analyses, we show our results and conclude with some forecasts data.

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Automated news reading: Stock price prediction based on financial news using context-capturing features

Cited by 279 publications

References 15 publications

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Weighted Fuzzy Rule Based Sentiment Prediction Analysis on Tweets

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