Modelling for Forecasting of Pattern Recognition - Based on comparison and analysis be-tween U.S. stock Market and Chinese stock Markets
- DOI
- 10.2991/amsm-16.2016.1How to use a DOI?
- Keywords
- trend forecasting; pattern recognition; U.S. and Chinese stock markets
- Abstract
An essential aspect of stock trading is the accurate forecast of stock price. This enables buy and sell points to be determined, which facilities profitability whilst reducing potential loses. This paper proposes a "Two-stage pattern Strategy (TSPS)" as an effective and intuitive mechanism to identify buy and sell signals. Utilizing technical analysis methods and pattern recognition principles, the TSPS indicates that (1) the trend to be more important than the isolated price; (2) two continuous unidirectional trends could verify the uptrend or downtrend; (3) the high or low price have more prediction power than the closing price; (4) the low price is more effective in prediction in the uptrend case, while the high price is more valuable than the low price in the downtrend case. Accordingly, this paper establishes mechanism to recognize up or down patterns and the pinpoint for buying or selling. This empirical study was done to verify the prediction power and profitability of TSPS. This study compared the performance of the U.S. and Chinese markets. The results show that TSPS can be widely used in the market regardless of the economic environment.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hong Zhou AU - Yu He AU - Yuxiang Jin PY - 2016/05 DA - 2016/05 TI - Modelling for Forecasting of Pattern Recognition - Based on comparison and analysis be-tween U.S. stock Market and Chinese stock Markets BT - Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling PB - Atlantis Press SP - 1 EP - 5 SN - 2352-538X UR - https://doi.org/10.2991/amsm-16.2016.1 DO - 10.2991/amsm-16.2016.1 ID - Zhou2016/05 ER -