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Multilayer stock forecasting model using fuzzy time series.
Hossein Javedani Sadaei1, Muhammad Hisyam Lee1
1Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
Thescientificworldjournal
|March 8, 2014
Summary
This study introduces a novel multilayer model for financial time series (FTS) stock market forecasting. The proposed framework addresses existing literature gaps and shows potential for developing robust FTS forecasting systems.
Area of Science:
- Quantitative Finance
- Computational Finance
- Financial Econometrics
Background:
- Existing literature on financial time series (FTS) forecasting reveals deficiencies in hybridized findings.
- A significant gap exists in systematic frameworks guiding the development of FTS forecasting systems.
Purpose of the Study:
- To propose a novel multilayer model for stock market forecasting.
- To provide a structured framework addressing specific challenges in FTS forecasting.
Main Methods:
- Development of a five-layer multilayer model for stock market prediction.
- Empirical validation using extensive datasets including Taiwan Stock Index (TAIEX), NASDAQ, DJI, and S&P 500.
Main Results:
- The proposed multilayer model demonstrates effectiveness in stock market forecasting.
- The methodology provides a potential framework for enhancing FTS forecasting systems.
Conclusions:
- The novel multilayer model offers a promising approach to stock market forecasting.
- This framework can guide future research and development in FTS forecasting systems.
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