Discovering Intraday Market Risk Exposures in Unstructured Data Sources: The Case of Corporate Disclosures

Groth, Sven S.; Muntermann, Jan

doi:10.1109/hicss.2010.152

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Crone and Koeppel [26] explore efficacy of using sentiment indicators as a predictor for foreign exchange rates. (For applications of text mining to stock returns, see Siering [27], Day and Lee [28], Groth and Muntermann [29], Ming et al [30], Li et al [31] and references therein.) Although there are a large number of works on predictions of stock markets using financial news, there has not been literature on text mining applications to interest rate markets, particularly a study explaining factors in an interest rate model by texts in financial news, to the best of our knowledge.…”

Section: A Level Factor Representingmentioning

confidence: 99%

Interest Rate Model With Investor Attitude and Text Mining

et al. 2020

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This paper develops and estimates an interest rate model with investor attitude factors, which are extracted by a text mining method. First, we consider two contrastive attitudes (optimistic versus conservative) towards uncertainties about Brownian motions driving economy, develop an interest rate model, and obtain an empirical framework of the economy consisting of permanent and transitory factors. Second, we apply the framework to a bond market under extremely low interest rate environment in recent years, and show that our three-factor model with level, steepening and flattening factors based on different investor attitudes is capable of explaining the yield curve in the Japanese government bond (JGB) markets.Third, text mining of a large text base of daily financial news reports enables us to distinguish between steepening and flattening factors, and from these textual data we can identify events and economic conditions that are associated with the steepening and flattening factors. We then estimate the yield curve and three factors with frequencies of relevant word groups chosen from textual data in addition to observed interest rates. Finally, we show that the estimated three factors, extracted only from the bond market data, are able to explain the movement in stock markets, in particular Nikkei 225 index.INDEX TERMS Interest rate model, text mining, filtering.

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Section: A Level Factor Representingmentioning

confidence: 99%

Interest Rate Model With Investor Attitude and Text Mining

et al. 2020

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“…There is an ever growing demand for the consumption of high-dimensional data across different enterprises such as Biomedical Engineering, Telecommunications, Geospatial Data, Climate Data and the Earth's Ecosystems, and Capital Research and Risk Management (Groth and Muntermann, 2010). This has resulted into the employment of some or the combination of the various requirements described in Table I to deploy unstructured data mining tools.…”

Section: Unstructured Data Miningmentioning

confidence: 99%

Data Mining From Document-Append Nosql

Lomotey¹

2014

STSC

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Due to the unstructured nature of modern digital data, NoSQL storages have been adopted by some enterprises as the preferred storage facility. NoSQL storages can store schema-oriented, semi-structured, schema-less data. A type of NoSQL storage is the document-append storage (e.g., CouchDB and Mongo) which has received high adoption due to its flexibility to store JSON-based data and files as attachment. However, the ability to perform data mining tasks from such storages remains a challenge and the required tools are generally lacking. Even though there is growing interest in textual data mining, there is huge gap in the engineering solutions that can be applied to document-append storage sources. In this work, we propose a data mining tool for term association detection. The flexibility of our proposed tool is the ability to perform data mining tasks from the document-source directly via HTTP without any copying or formatting of the existing JSON data. We adapt the Kalman filter algorithm to accomplish macro tasks such as topic extraction, term organization, term classification and term clustering. The work is evaluated in comparison with existing textual mining tools such as Apache Mahout and R with promissory result on term extraction accuracy.

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“…Select one spot in the plane as origin to establish a Cartesian coordinate system. For x' i,j , mapped to point G i,j (G_X i,j , G_Y i,j ) by equation (2). In the same way, state variable X k at the moment K is mapped into M points, they are G 1,k ,G 2,k ,…,G M,k .…”

Section: Graphic Representation Of the State Variablesmentioning

confidence: 99%

“…Onoda Takashi [1] adopted support vector machine (SVM) to diagnose the hydropower station abnormalities. Sven S. Groth [2] used SVM for text-mining to find risk in capital market. Cao [3] introduced the chaos theory into the disaster forecasting, also combined the phase space reconstruction theory with neural network to identify the flood long-term signs.…”

Section: Introductionmentioning

confidence: 99%

A New Hazard Identification Method-State Transition Graph

et al. 2011

AMM

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It is important and difficult to identify the Hazard before a disaster happen because disaster often happens suddenly. This paper proposes a new method – State Transition Graph, which based on visual data space reconstruction, to identify hazard. The change process of the system state movement from one state to another in a certain period is described by some state transition graphs. The system state, which is safe or hazard, could be distinguished by its state transition graphs. This paper conducted experiments on single-dimension and multi-dimension benchmark data to prove the new method is effectiveness. Especially the result of stimulation experiments, based on the Yangtze River tunnel engineering data, showed that state transition graph identifies hazard easily and has better performances than other method. The State transition graph method is worth further researching.

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Discovering Intraday Market Risk Exposures in Unstructured Data Sources: The Case of Corporate Disclosures

Cited by 5 publications

References 28 publications

Interest Rate Model With Investor Attitude and Text Mining

Interest Rate Model With Investor Attitude and Text Mining

Data Mining From Document-Append Nosql

A New Hazard Identification Method-State Transition Graph

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