1 |
Ito, A. (1999). Profits on technical trading rules and time-varying expected returns: Evidence from Pacific- Basin equity markets. Pacific-Basin Finance Journal, 7, 283-330.
DOI
ScienceOn
|
2 |
Izumi, K., Toriumi, F. and Matsui, H. (2009). Evaluation of automated-trading strategies using an artificial market. Neurocomputing, 72, 3469-3476.
DOI
ScienceOn
|
3 |
Kho, B. C. (1996). Time-varying risk premia, volatility, and technical trading rule profits: Evidence from foreign currency futures markets. Journal of Financial Economics, 41, 249-290.
DOI
ScienceOn
|
4 |
Kim, M. S. and Oh, K. J. (2011). Using rough set to support arbitrage box spread strategies in KOSPI 200 option markets. Journal of the Korean Data & Information Science Society, 22, 37-47.
|
5 |
Lee, J. H. and Jo, G. S. (1999). Exper system for predicting stock market timing using a candlestick chart. Expert Systems with Applications, 16, 357-364.
DOI
ScienceOn
|
6 |
Lee, S. J. and Oh, K. J. (2011). Finding the optimal frequency for trade and development of system trading strategies in futures market using dynamic time warping. Journal of the Korean Data & Information Science Society, 22, 255-267.
|
7 |
Marc, B. (2004). Khiops: A statistical discretization method of continuous attributes. Machine Learning, 55, 53-69.
DOI
|
8 |
Marshall, B. R., Young, M. R. and Rose, L. C. (2006). Candlestick technical trading strategies: Can they create value for investors? Journal of Banking & Finance, 30, 2303-2323.
DOI
ScienceOn
|
9 |
Murphy, J. J. (1999). Technical analysis of the financial markets, New York Institute of Finance, Paramus, NJ.
|
10 |
Neely, C. J. (2002). The temporal pattern of trading rule returns and exchange rate intervention: intervention does not generate technical trading profits. Journal of International Economics, 58, 211-232.
DOI
ScienceOn
|
11 |
Arie, B. D. (2008). Rule effectiveness in rule-based systems: A credit scoring case study. Expert Systems with Applications, 34, 2783-2788.
DOI
ScienceOn
|
12 |
Byun, H. W., Song, C. W., Han, S. K., Lee, T. K. and Oh, K. J. (2009). Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization. Journal of the Korean Data & Information Science Society, 20, 467-478.
|
13 |
Chavarnakul, T. and Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34, 1004-1017.
DOI
ScienceOn
|
14 |
Chavarnakul, T. and Enke, D. (2009). A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing, 72, 3517-3528.
DOI
ScienceOn
|
15 |
Chris, F., Andros, G. and Alexandros, K. (2011). Trading frequency and asset pricing on the London Stock Exchange: Evidence from a new price impact ratio. Journal of Banking & Finance, 35, 3335-3350.
DOI
ScienceOn
|
16 |
Dymova, L., Sevastianov, P. and Bartosiewicz, P. (2010). A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications, 37, 5564-5576.
DOI
ScienceOn
|
17 |
Gencay, R. (1999). Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of International Economics, 47, 91-107.
DOI
ScienceOn
|
18 |
Song, C. W. and Oh, K. J., (2009). Study of validation process according to various option strategies in a KOSPI 200 options market. Journal of the Korean Data & Information Science Society, 20, 1061-1073.
|
19 |
Hong, J. S. and Kwon, T. W. (2010). Distribution fitting for the rate of return and value at risk. Journal of the Korean Data & Information Science Society, 21, 219-229.
|
20 |
Horton, M. J. (2009). Stars, crows, and doji : The use of candlesticks in stock selection. The Quarterly Review of Economics and Finance, 49, 283-294.
DOI
ScienceOn
|
21 |
Tan, Z., Quek, C. and Cheng, P. Y. K. (2011). Stock trading with cycles: A financial application of ANFIS and reinforcement learning. Expert Systems with Applications, 38, 4741-4755.
DOI
ScienceOn
|
22 |
Park, B. J. (2011). Herd behavior and volatility in financial markets. Journal of the Korean Data & Information Science Society, 22, 1199-1215.
|
23 |
Taylor, M. P. (1992). The use of technical analysis in the foreign exchange market. Journal of International Money and Finance, 11, 304-314.
DOI
ScienceOn
|
24 |
Wei, J. M., Wang, S. Q., Wang, M. Y., You, J. P. and Liu, D. Y. (2007). Rough set based approach for inducing decision trees. Knowledge-Based Systems, 20, 695-702.
DOI
ScienceOn
|
25 |
Oh, K. J., Kim, T. Y., Jung, K. W. and Kim, C. H. (2011). Stock market stability index via linear and neural network autoregressive model. Journal of the Korean Data & Information Science Society, 22, 355-351.
|
26 |
Pawlak, Z. and Munakata, T. (1996). Rough control, application of rough set theory to control. Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing, Germany, 209-218.
|
27 |
Park, I. S., Han, J. T., Kang, S. B. and Ji, J. H. (2010). Developing the predictive model for stomach cancer using data mining. Journal of the Korean Data & Information Science Society, 21, 1253-1261.
|
28 |
Pawlak, Z. (1991). Rough sets - Theoretical aspects of reasoning about data, Kluwer Academic Publishers, Dordrecht, Boston, London.
|
29 |
Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99, 48-57.
DOI
ScienceOn
|
30 |
Quinlan, J. R. (1986). Induction of decision tree. Machine Learning, 1, 81-106.
|
31 |
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21, 49-58.
DOI
ScienceOn
|
32 |
Shen, L. and Loh, H. T. (2004). Applying rough sets to market timing decisions. Decision Support Systems, 37, 583-597.
DOI
ScienceOn
|
33 |
Shin, Y. K. (2006). An empirical study on stock trading value of each investor type in the Korean stock market. Journal of the Korean Data & Information Science Society, 17, 1099-1106.
|
34 |
Slowinski, R. and Stefanowski, J. (1994). Rough classification with valued closeness relation, Springer- Verlag, Berlin, 482-488.
|