Detecting similar news tickers in the area of natural gas trading: improving decision support in uncertain situations
Pages 121 - 128
Abstract
The volatility of the natural gas market founded a need for the ability to analyze upcoming events in real time in order to manage profits and risks for participants. News ticker provide information being of utmost importance for the analysis. The research presented among this paper describes features of a software prototype supporting the analytical price prognosis tasks for gas traders. By knowing market development at the time of a certain past situation, the outcome of that situation can be used to predict the future market development of a current analyzed situation with similar content. For that, similar situations have to be detected in order to reduce uncertainty about future. Fitting into design science, we use task-technology-fit theory and technology-acceptance-model to identify information needs and to evaluate the artifact. This novel approach serves as a further step to gain a decision support with integrated structured and unstructured data.
References
[1]
Aamodt, A. and Plaza, E. 1994. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications. 7, 1 (March 1994), 39--59.
[2]
Agbon, I. S. and Araque, J. C. 2003. Predicting Oil and Gas Spot Prices Using Chaos Time Series Analysis and Fuzzy Neural Network Model. In SPE Hydrocarbon Economics and Evaluation Symposium (Dallas, USA, April 05-08, 2003). 1--8. DOI = http://dx.doi.org/10.2118/82014-MS.
[3]
Alić, I., Muntermann, J., Gregory, R. 2012. State of the Art of Financial Decision Support Systems based on Problem, Requirement, Component and Evaluation Categories. In 25th BLED eConference 2012 Proceedings, (Bled, Slovenia, June 17--20, 2012). 280--293.
[4]
BDEW Wettbewerb 2012 -- Wo steht der deutsche Energiemarkt?, http://www.bdew.de/internet.nsf/id/8CF41C4A9D744B5DC1257AAD005326D9/$file/121023-BDEW-Wettbewerb-Dt-Energiemarkt-longVersion-WEB.pdf (accessed: Dec 06, 2014).
[5]
Bloomberg Commodities, http://www.bloomberg.com/professional/markets/commodities/ (accessed: Dec 06, 2014).
[6]
Browne, G. J. and Ramesh, V. 2002. Improving information requirements determination: a cognitive perspective. Information & Management. 39, 8 (Sept. 2002), 625--645. DOI =
[7]
Buchananan, W. K., Hodges, P. and Theis, J. 2001. Which way the natural gas price: an attempt to predict the direction of natural gas spot price movements using trader positions. Energy Economics. 23, 3 (May 2001), 279--293. DOI =
[8]
Busse, S., Helmholz, P. and Weinmann, M. 2012. Forecasting day ahead spot price movements of natural gas -- An analysis of potential influence factors on basis of a NARX neural network. In Multikonferenz Wirtschaftsinformatik 2012, Tagungsband der MKWI 2012. Braunschweig, Germany.
[9]
Chang, H. H. 2003. Intelligent agent's technology characteristics applied to online auctions' task: A combined model of TTF and TAM. Technovation. 28, 9 (Sep. 2003), 564--577. DOI=
[10]
Cheung, Y. M. 2005. K*-means: A New Generalized k-means Clustering Algorithm. Pattern Recognition Letters. 24, 15 (Nov. 2003), 2883--2893. DOI =
[11]
Cooper, H. M. 1982. Scientific Guidelines for Conducting Integrative Research Reviews. 1982. Review of Educational Research. 52, 2 (Summer 1982), 291--302.
[12]
Dang, Y., Zhan, Y., Jen-Hwa Hu, P. and Brown, S. 2011. Knowledge mapping for rapidly evloving domains: A design science approach. Decision Support Systems. 50, 2 (Jan. 2011), 415--427.DOI = 10.1016/j.dss.2010.10.003.
[13]
Davis, F. D. 1989. Perceived Usefulness, perceived ease of use and users acceptance of information technology. MIS Quarterly. 13, 3 (Sep. 1989), 319--340.DOI = 10.2307/249008.
[14]
Dennis, A. R., Wixom, B. H. and Vandenberg, R. J. 2001. Understanding Fit and Appropriation Effects in Group Support Systems Via Meta-Analysis. MIS Quarterly. 25, 2, (June 2001), 167--193.DOI= 10.2307/3250928.
[15]
Felden, C. 2002. Konzept zum Aufbau eines Marktdateninformationssystems für den Energiehandel. Auf der Basis externer und interner Daten. Doctoral Thesis. Deutscher Universitäts-Verlag, Wiesbaden, Germany.
[16]
Flick, U. 2009. An Introduction to qualitative Research. SAGE Publications Ltd., London, UK.
[17]
Gregor, S. and Hevner, A. R. 2013. Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly. 37, 2 (June 2013), 337--355.
[18]
Goodhue, D. L. 1998. Toward theoretical and definition clarity. ACM SIGMIS Database.19, 3--4 (Fall/Winter 1998) 6--15. DOI= 10.1145/65766.65768.
[19]
Goodhue, D. L. and Thompson, R. L. 1995. Task-technology fit and individual performance. MIS Quarterly. 19, 2 (June 1995), 213--236.
[20]
Heuterkes, M. and Janssen, M. 2005. Die Regulierung von Gas- und Strommärkten in Deutschland. Beiträge aus der angewandten Wirtschaftsforschung. Westfälische Wilhelms-Universität Münster.
[21]
Hevner, A. and March, S. 2003. The Information System Research Cycle. IT Systems Perspectives. 36, 11 (Nov. 2003), 111--113.DOI = 10.1109/MC.2003.1244541.
[22]
Hevner, A., March, S, Park, J. and Ram, S.2004. Design Science in Information System Research. MIS Quarterly. 28, 1 (March 2004), 75--105.
[23]
Jardine, N. and van Rijsbergen, C. J. 1971. The use of hierarchical clustering in information retrieval. Information Storage and Retrieval. 7, 5 (Dec, 1971), 217--240. DOI =
[24]
Leifer, R., Lee, S. and Durgee, J. 1994. Deep structures: Real information requirements determination. Information & Management. 27, 5 (Nov. 1994), 275--285. DOI =
[25]
Lessmann, S., Listiani, M. and Voß, S. 2010. Decision Support in Car Leasing: A Forecasting Model For Residual Value Estimiation. In ICIS 2010 Proceedings of 31. International Conference on Information Systems (St. Louis, USA, Dec. 12--15 2010). ICIS2010.
[26]
Lin, B. and Wesseh, P. K. 2013. What causes price volatility and regime shifts in the natural gas market. Energy. 55, 15 (June 2013), 553--563. DOI =
[27]
Linn, S. C. and Zhu, Z. 2004. Natural gas prices and the gas storage report: Public news and volatility in energy futures markets. Journal of Futures Markets. 24, 3 (Jan. 2004), 283--313. DOI = 10.1002/fut.10115.
[28]
Lv, X. and Shan, X. 2013. Modeling natural gas market volatility using GARCH with different distributions. Physica A. 392, 22 (Nov. 2013), 5685--5699. DOI =
[29]
Malliaris, M. E. and Malliaris, S. G. 2005. Forecasting Energy Product Prices. In Proceedings of International Joint Conference on Neural Networks (Montreal, Canada, July 31 -- August 04, 2005). IJCNN '05. IEEE.
[30]
v. Mettenheim, H.-J. and Breitner, M. 2010. Robust Decision support systems with matrix forecasts and shared layer perceptrons for finance and other applications. In ICIS 2010 Proceedings of 31. International Conference on Information Systems (St. Louis, USA, Dec. 12--15 2010). ICIS2010.
[31]
Mishra, P. 2012. Forecasting Natural Gas Price - Time Series and Nonparametric Approach. In Proceedings of the World Congress on Engineering (London, UK, July 04-06, 2012). WCE, London.
[32]
Mishra, P. 2013. Natural Gas Price Forecasting: A Novel Approach. Lecture Notes in Electrical Engineering. 229, 627--639. DOI =
[33]
Movassagh, N. and Modjtahedi, B. 2005. Bias and Backwardation in natural gas futures prices. The Journal of Futures Markets. 25, 3 (March 2005), 281--308. DOI = 10.1002/fut.20151.
[34]
Muntermann, J. 2009. Towards ubiquitous information supply for indidual investors: a decision support system design. Decision Support Systems. 47, 2 (May 2009), 82--92. DOI =
[35]
Naumann, J. and Jenkins, M. 1982. Prototyping: The New Paradigm for System Development. MIS Quarterly. 6, 3 (Sep. 1982), 29--44. DOI = 10.2307/248654.
[36]
Nguyen, H. T. and Nabney, I. T. 2010. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models. Energy. 35, 9 (Sep. 2010), 3674--3685. DOI =
[37]
Odukaya, O. H., Aderounmu, G. A. and Adagunodo, E. R. 2010. An Improved Data Clustering Algorithm for Mining Web Documents. Obafemi Awolowo University, Osun State, Ile-Ife, Nigeria.
[38]
Pakhiraa, M. K., Bandyopadhyayb, S. and Maulik, U. 2004. Validity index for crisp and fuzzy clusters. Pattern Recognition Letters. 37, 3 (March 2004), 487--501.DOI =
[39]
Panella, M., Barcellona, F. and D'Ecclesia, R. L. 2012. Subband Prediction of Energy Commodity Prices. In IEEE 13th International Workshop on Signal Processing Advances in Wireless Communication (Cesme, Turkey, June 17--20 2012).
[40]
Peffers, K., Tuunanen, T., Rothenberger, M. A. and Chatterjee, S. 2007. A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24, 3 (Winter 2007--2008), 45--78.
[41]
Pospiech, M. and Felden, C. 2014. Towards a Price Forecast Model for the German Electricity Market Based on Structured and Unstructured Data. In Multikonferenz Wirtschaftsinformatik MKWI (Paderborn, Germany, February 26--28, 2014).
[42]
Pospiech, M and Felden, C. 2012. Big Data -- A State-of-the-Art. In Americas Conference on Information Systems AMCIS (Seattle, USA, August 09-12, 2012).
[43]
Premalatha, K. and Natarajan, A. 2010. A Literature Review on Document Clustering. Information Technology Journal. 9, 993--1002. DOI = 10.3923/itj.2010.993.1002.
[44]
Ramos, J. 2003. Using TF-IDF to Determine Word Relevance in Document Queries. Department of Computer Science, Rutgers University.
[45]
Reiter, D. F. and Economides, M. J. 1999. Prediction of Short-term Natural Gas Prices Using Econometric and Neural Network Models. In SPE Hydrocarbon Economics and Evaluation Symposium (Dallas, USA, March 21-23, 1999). 52960-MS SPE Conference Paper.
[46]
Root, T. H. and Lien, D. 2003. Can modeling the natural gas futures market as a threshold cointegrated system improve hedging and forecasting performance? International Review of Financial Analysis. 12, 2 (2nd Quarter 2003), 117--133. DOI =
[47]
Salehnia, N., Falahi, M. A. and Seifi, A. 2013. Forecasting natural gas spot prices with nonlinear modeling using Gamma test analysis. Journal of Natural Gas Science and Engineering. 14 (Sep. 2013), 238--249. DOI =
[48]
Salton, G., Wong, A. and Yang, C. S. 1975. A Vector Space Model for Automatic Indexing. Information Retrieval and Language Processing. 8, 11 (Nov. 1975), 613--620. DOI = 10.1145/361219.361220.
[49]
Schiffer, H. 2010. Energiemarkt Deutschland. TÜV Media GmbH, Köln, Germany.
[50]
Schröder, U. 2008. Prototyping in der Softwareentwicklung. Softwareentwicklung Aufbaukurs. AKAD Die Privathochschule. Pinneberg, Germany.
[51]
Simon, H. 1996. The Science of the Artifical. The MIT Press, Cambridge, MA, USA.
[52]
Sitarama, S., Mahadevan, U., Abrol, M. 2004. Efficient cluster representation in similar document search. In Proceedings of 12. WWW conference (Budapest, Hungary, May 20-24, 2003) WWW2003.
[53]
Specht, P. 1986. Job Characteristics as indicants of CBIS Data Requirements. MIS Quarterly. 10, 3 (Sep. 1986), 271--287. DOI = 10.2307/249261.
[54]
Stavrianou, A., Andritsos, P. and Nicoloyannis, N. 2007. Overview and Semantic Issues of Text Mining. SIGMOD Record. 36, 3 (Sep. 2007), 23--34.
[55]
Stirndt, D., Nuss, C., Bensch, S. and Dirr, T. 2014. An environmental Management Information System for Closing Knowledge Gaps in corporate Sustainable Decision Making. In Proceedings of 35. International Conference on Information Systems (Auckland, New Zealand, Dec. 12-14 2014).
[56]
Totok, A. 2000. Modellierung von OLAP- und Data-Warehouse-Systemen. Betriebswirtschaftlicher Verlag Dr. Th. Gabler, Wiesbaden, Germany.
[57]
Trück, S. and Weron, R. 2004. Vorsicht Hochspannung! Risikomanagement in Energiemärkten (Teil I) Stromhandel in Deutschland und Besonderheiten des Energiemarktes. RISKNEWS. 1, 3 (June 2004), 64--69.
[58]
Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 3 (Sep. 2003) 425--478.
[59]
Wan, X. 2007. A novel document similarity measure based on earthmover's distance. Information Sciences. 177, 18 (Sep. 2007), 3718--3730. DOI =
[60]
Webster, J. and Watson, R. 2002. Analyzing the past to prepare for the future: writing a literature review. MIS Quarterly. 26, 2 (June 2002), 13--23. DOI =
[61]
Woo, C. K., Olsona, A. and Horowitz, I. 2006. Market efficiency, cross hedging and price forecasts: California's natural gas markets. Energy. 31, 8-9 (July 2006), 1290--1304.
[62]
Zhang, W., Yoshida, T. and Tang, X. 2011. A comparative study of TFIDF, LSI and multi-words for text classification. Expert Systems with Applications. 38, 3 (March 2011), 2758--2765. DOI = 10.1016/j.eswa.2010.08.066.
[63]
Zigurs, I., Buckland, B., Connolly, J. and Wilson, E. V. 1999. A test of task-technology fit theory for group support systems. Data Base for Advances in Information Systems. 30, 3--4 (Summer/Fall 1999), 34--50. DOI= 10.1145/344241.344244.
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- Detecting similar news tickers in the area of natural gas trading: improving decision support in uncertain situations
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