We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Financial Trading System using Combination of Textual and Numerical Data

by Shital N. Dange, Rajesh V. Argiddi, S. S. Apte
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 1
Year of Publication: 2012
Authors: Shital N. Dange, Rajesh V. Argiddi, S. S. Apte
10.5120/8008-1372

Shital N. Dange, Rajesh V. Argiddi, S. S. Apte . Financial Trading System using Combination of Textual and Numerical Data. International Journal of Computer Applications. 51, 1 ( August 2012), 36-40. DOI=10.5120/8008-1372

@article{ 10.5120/8008-1372,
author = { Shital N. Dange, Rajesh V. Argiddi, S. S. Apte },
title = { Financial Trading System using Combination of Textual and Numerical Data },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 1 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number1/8008-1372/ },
doi = { 10.5120/8008-1372 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:49:18.627654+05:30
%A Shital N. Dange
%A Rajesh V. Argiddi
%A S. S. Apte
%T Financial Trading System using Combination of Textual and Numerical Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 1
%P 36-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There is large amount of financial data that are generated and evaluated at a high speed. These financial data is coming continuously, changing with time and may be unpredictable. Therefore there is a critical need for automated approaches to effective and efficient utilization of large amount of data to support companies and individuals for decision-making. Data mining techniques can be used to uncover hidden patterns, to discover the behavior of the stock market, to find out the trends in financial markets and so on. For predicting stock trends and making financial trading decisions, a new model is presented. It is based on combination of data and text mining techniques which takes the textual contents of time-stamped web documents along with numerical time series data and performs the future prediction. By using this model, we will show that the accuracy of result will be improved.

References
  1. Gil Rachlin, Mark Last, Dima Alberg and Abraham Kandel, 2007. ADMIRAL: A Data Mining Based Financial Trading System, Proceeding of the 2007 IEEE Symposium on Computational Intelligence and Data Mining(CIDM 2007).
  2. E. F. Fama, 1970. Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25 (May 1970): 383-417.
  3. E. F. Fama, 1991. Efficient Capital Markets: II, Journal of Finance, 46 (December 1991): 1575-1617.
  4. E. F. Fama, 1995 . Random Walks in Stock Market Prices, Financial Analysts Journal, September/ October 1965 (reprinted in January-February 1995).
  5. Quinlan, J. R. 1986. Induction of Decision Tree. Machine Leaning, Vol, pp. 81-106.
  6. J. R. Quinlan, 1993 C4. 5: Programs for Machine Learning, Morgan Kaufman Publishers Inc. , San Francisco.
  7. Luk Chi Wa, Analyzing Stock Quotes using Data Mining Technique, The University of Hong Kong
  8. B. Wuthrich, V. Cho, S. Leung, D. Permunetilleke, K. Sankaran, J. Zhang, W. Lam, 1998. Daily Stock Market Forecast from Textual Web Data, In IEEE International Conference on Systems, Man. and Cybernetics, Volume: 3, Page(s): 2720 -2725.
  9. R. Engle and T. Bollerslev, 1986 . Modelling the Persistence in Conditional Variances, Econometric Reviews, 5, 81 -87.
  10. T. Bollerslev, 1986. Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics, 31, 307-327.
  11. R. Engle, 1982 . Autoregressive Conditional Hetroscedasticity with Estimates of the Variance of United Kingdom Infation, Econometrica, 50(4), 987-1007.
  12. Agrawal, R. , Lin, K. -I. , Sawhney, H. S. , and Shim, K. (1995a). Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. Proc. 21st Int'l Conf. Very Large Data Bases (VLDB '95), Sept. 1995, pp. 490±501
  13. Agrawal, R. , Psaila, G. , Wimmers, E. L. , and Zait, M. (1995b). Querying Shapes of Histories, Proc. 21st Int'l Conf. Very Large Data Bases (VLDB '95), Sept. 1995, pp. 502±514.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining pre-processing feature extraction