An intraday market risk management approach based on textual analysis

Groth, Sven S.; Muntermann, Jan

doi:10.1016/j.dss.2010.08.019

Cited by 139 publications

(75 citation statements)

References 27 publications

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“…Various studies have shown that the linguistic content of a document is useful in explaining stock market returns. In this context, dictionary-based methods for sentiment analysis are used to explain stock returns, stock volatility and firm earnings by the tone of newspapers (e. g. Tetlock, 2007;Tetlock, Saar-Tsechansky, and Macskassy, 2008), company press releases (Demers and Vega, 2010;Engelberg, 2008;Henry, 2008), regulated ad hoc announcements (Feuerriegel, Ratku, and Neumann, 2015;Groth and Muntermann, 2011; Twenty-Third European Conference on Information Systems, Münster, Germany, 2015 Muntermann and Guettler, 2007) and 10-K reports (Feldman, Govindaraj, Livnat, and Segal, 2008;Hanley and Hoberg, 2008;Li, 2008).…”

Section: Dictionary-based News Sentimentmentioning

confidence: 99%

“…In related research, ad hoc announcements are a frequent choice (Mittermayer and Knolmayer, 2006) when it comes to evaluating and comparing methods for sentiment analysis. Additionally, this type of news corpus shows several advantages: ad hoc announcements must be authorized by company executives, the content is quality-checked by the Federal Financial Supervisory Authority 2 and several publications analyze their relevance to stock market reactions -finding a direct relationship (e. g. Feuerriegel, Ratku, and Neumann, 2015;Groth and Muntermann, 2011;Muntermann and Guettler, 2007). As a requirement, each announcement must have at least 50 words and be written in the English language.…”

Section: Datasetmentioning

confidence: 99%

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Generating Domain-Specific Dictionaries Using Bayesian Learning

2014

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Section: Dictionary-based News Sentimentmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Generating Domain-Specific Dictionaries Using Bayesian Learning

2014

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“…Benchmark. To evaluate the results produced by the developed predictive system and to get a better understanding of its performance, a standard benchmark [37], [38] following the conditions of the stock market is utilised. In the long-term, stock market prices tend to increase, and it is essential to assure that the trading system based on predictions outperforms a simple benchmark and actually generates value.…”

Section: Methodsmentioning

confidence: 99%

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

et al. 2017

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-The creation of a predictive system that correctly forecasts future changes of a stock price is crucial for investment management and algorithmic trading. The use of technical analysis for financial forecasting has been successfully employed by many researchers. Input window length is a time frame parameter required to be set when calculating many technical indicators. This study explores how the performance of the predictive system depends on a combination of a forecast horizon and an input window length for forecasting variable horizons. Technical indicators are used as input features for machine learning algorithms to forecast future directions of stock price movements. The dataset consists of ten years daily price time series for fifty stocks. The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon. This novel pattern is studied using multiple performance metrics: prediction accuracy, winning rate, return per trade and Sharpe ratio.

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“…Regarding the machine learning algorithms and the incorporation of quantitative indicators, the approaches of Groth and Muntermann (2011), Kogan et al (2009), andHajek andOlej (2013) are a good basis for the experiments of this study. All of them define the document labels based on suitable quantitative indicators.…”

Section: Related Workmentioning

confidence: 99%

Detecting Risks in the Banking System by Sentiment Analysis

Nopp¹,

Hanbury²

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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In November 2014, the European Central Bank (ECB) started to directly supervise the largest banks in the Eurozone via the Single Supervisory Mechanism (SSM). While supervisory risk assessments are usually based on quantitative data and surveys, this work explores whether sentiment analysis is capable of measuring a bank's attitude and opinions towards risk by analyzing text data. For realizing this study, a collection consisting of more than 500 CEO letters and outlook sections extracted from bank annual reports is built up. Based on these data, two distinct experiments are conducted. The evaluations find promising opportunities, but also limitations for risk sentiment analysis in banking supervision. At the level of individual banks, predictions are relatively inaccurate. In contrast, the analysis of aggregated figures revealed strong and significant correlations between uncertainty or negativity in textual disclosures and the quantitative risk indicator's future evolution. Risk sentiment analysis should therefore rather be used for macroprudential analyses than for assessments of individual banks.

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An intraday market risk management approach based on textual analysis

Cited by 139 publications

References 27 publications

Generating Domain-Specific Dictionaries Using Bayesian Learning

Generating Domain-Specific Dictionaries Using Bayesian Learning

Forecasting price movements using technical indicators: Investigating the impact of varying input window length

Detecting Risks in the Banking System by Sentiment Analysis

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