By Artur Lugmayr
This booklet constitutes the lawsuits of the seventh foreign Workshop on company purposes and prone within the Finance undefined, FinanceCom 2014, held in Sydney, Australia, on December 12, 2014.
The workshop spans a number of disciplines, together with technical, provider, fiscal, sociological, and behavioral sciences. It displays on technologically enabled possibilities, implications, and alterations as a result of the creation of recent company versions or rules relating to the monetary providers and the monetary markets.
The 9 papers provided have been rigorously reviewed and chosen from various submissions.
Read or Download Enterprise Applications and Services in the Finance Industry: 7th International Workshop, FinanceCom 2014, Sydney, Australia, December 2014, Revised Papers PDF
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Additional info for Enterprise Applications and Services in the Finance Industry: 7th International Workshop, FinanceCom 2014, Sydney, Australia, December 2014, Revised Papers
Example text
J. Finance 63(3), 1437–1467 (2008) 49. : Stock market prediction from WSJ: text mining via sparse matrix factorization. In: IEEE International Conference on Data Mining (ICDM), pp. 430–439. IEEE Computer Society, Shenzhen, China (2014) A Proposed Framework for Evaluating the Effectiveness of Financial News Sentiment Scoring Datasets Islam Qudah1(&), Fethi A. au Abstract. The impact of financial news on financial markets has been studied extensively. A number of news sentiment scoring techniques are being widely used in research and industry.
ACM, New York 43. : Irrational Exuberance. Princeton University Press, Princeton (2005) 44. : The noise trader approach to finance. J. Econ. Perspect. 4(2), 19–33 (1990) 45. : The limits of arbitrage. J. Finance 52(1), 35–55 (1997) 46. : Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks (2013) 47. : Giving content to investor sentiment: the role of media in the stock market. J. Finance 62(3), 1139–1168 (2007) 48. : More than words: quantifying language to measure firms’ fundamentals.
The column (No. of Distinct Event Days) contains the number of event days in dataset (Y). In addition, the table also lists the results for the end of day returns. The impact measure, IM, for this paper is (Mean Cumulative Abnormal Return (MCAR)) of Evaluating the Effectiveness of News Sentiment Scoring Datasets 41 Table 4. Experiments parameters Exp. No. AX, R=1, SRD={ 01/01/2011, 31/12/2011} 203 83 14 7 56 20 322 132 31 7 26 14 203 19 322 12 entity (E) in relative to benchmark (B). Lastly the column (Generalized Sign Z) highlights the statistical significance of the returns.