ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Forecasting Shanghai stock index using FTS model based on SVM-modify

  • School of Management, Nanjing University, Nanjing 210093, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.03.009
  • Received Date: 12 September 2015
  • Accepted Date: 29 December 2015
  • Rev Recd Date: 29 December 2015
  • Publish Date: 30 March 2016
    Abstract

  • Abstract

    Traditional methods for stock index research are still at the stage of judging by experience or relying on simple data analysis, among which fundamental analysis and trading indicator analysis frequently used. These methods have noticeable disadvantages: Inefficient utilization of existing information or requirement for highly experienced users. A modified fuzzy time series (FTS) model was proposed based on the following three aspects. Firstly, a new method of interval division was developed. Secondly, a new weight formula for fuzzy set was devised. Thirdly, a modified FTS model was built with the application of SVM classification model. Predictions for stock index were made by using the proposed model. Experiment results from Shanghai index data ranging from 1996 to 2003 indicate that compared with other important FTS models; the proposed model provides better performance.

    Abstract

    Traditional methods for stock index research are still at the stage of judging by experience or relying on simple data analysis, among which fundamental analysis and trading indicator analysis frequently used. These methods have noticeable disadvantages: Inefficient utilization of existing information or requirement for highly experienced users. A modified fuzzy time series (FTS) model was proposed based on the following three aspects. Firstly, a new method of interval division was developed. Secondly, a new weight formula for fuzzy set was devised. Thirdly, a modified FTS model was built with the application of SVM classification model. Predictions for stock index were made by using the proposed model. Experiment results from Shanghai index data ranging from 1996 to 2003 indicate that compared with other important FTS models; the proposed model provides better performance.
    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    陈迪红, 杨湘豫, 李华中. 中国证券市场指数波动的周期分析[J]. 湖南大学学报, 2003, 30(5): 88-91.
    CHEN Dihong, YANG Xiangyu, LI Huazhong. Analysis of cycle about fluctuation of stock index in Chinese securities market[J]. Journal of Hunan University, 2003, 30(5): 88-91.
    [2]
    周佰成, 周建文, 方炬. 中、美证券市场的波动周期比较[J]. 经济纵横, 2006, (5): 72-73.
    [3]
    田俊刚, 梁红漫. 中国股票市场周期性研究[J]. 武汉金融, 2008, (7): 14-16, 36.
    [4]
    黄继平, 黄良文. 中国股市波动的周期性研究[J]. 统计研究, 2003, (11): 9-14.
    [5]
    董直庆, 夏小迪. 我国通货膨胀和股市周期波动共变性和非一致性再检验[J]. 经济学家, 2010, (3): 73-80.
    [6]
    CHENG C H, CHEN Y S, WU Y L. Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model[J]. Expert Systems with Applications, 2009, 36(2): 1826-1832.
    [7]
    张韬, 冯子健, 杨维中, 等. 模糊时间序列分析在肾综合征出血热发病率预测的应用初探[J]. 中国卫生统计, 2011, 28(2): 146-150.
    [8]
    钱冰冰. Type-2模糊系统在黄金价格预测中的应用[J]. 佳木斯大学学报, 2007, 25(3): 397-399.
    [9]
    SONG Q, CHISSOM B S. Forecasting Enrollments With Fuzzy Time Series[J]. Fuzzy Sets and Systems, 1993, 54(93): 1-9.
    [10]
    LEE M H, EFENDI R, ISMAIL Z. Modified weighted for enrollment forecasting based on fuzzy time series[J]. MATEMATIKA, 2009, 25(1): 67-78.
    [11]
    CHEN S M. Forecasting enrollments based on fuzzy time series[J]. Fuzzy Sets and Systems, 1996, 81(3): 311-319.
    [12]
    HUARNG K, YU H K. A type 2 fuzzy time series model for stock index forecasting[J]. Physica A: Statistical Mechanics and its Applications, 2005, 353(1-4): 445-462.
    [13]
    CHENG C H, CHEN T L, CHIANG C H. Trend-weighted fuzzy time-series model for TAIEX forecasting[C]// Proceedings of the 13th International Conference on Neural Information Processing. Hong Kong, China: ACM Press, 2006, 4234: 469-477.
    [14]
    CHU H H, CHEN T L, CHENG C H, et al. Fuzzy dual-factor time-series for stock index forecasting[J]. Expert Systems with Applications, 2009, 36(1): 165-171.
    [15]
    TING J L. Causalities of the Taiwan stock market[J]. Physica A: Statistical Mechanics and its Applications, 2003, 324(1-2): 285-295.
    [16]
    KARPOFF J M. The relation between price changes and trading volume: A survey[J]. The Journal of Financial and Quantitative Analysis, 1987, 22(1): 109-126.
    [17]
    金春雨, 郭沛. 我国股票市场量价关系的实证研究--基于上证指数的VAR模型分析[J]. 价格理论与实践, 2010, (9): 60-61.
    [18]
    王重, 张文转. 股票指数与股票市场技术要素的实证分析[J]. 现代商贸工业, 2008, 20(2): 159-160.
    [19]
    ALADAG C H, BASARAN M A, EGRIOGLU E, et al. Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations[J]. Expert Systems with Applications, 2009, 36(3): 4228-4231.
    [20]
    EGRIOGLU E, ALADAG C H, YOLCU U, et al. Finding an optimal interval length in high order fuzzy time series[J]. Expert Systems with Applications, 2010, 37(7): 5052-5055.
    [21]
    CHEN S M, WANG N Y, PAN J S. Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships[J]. Expert Systems with Applications, 2009, 36(8): 11070-11076.
    [22]
    QIU W, LIU X, WANG L. Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees[J]. Expert Systems with Applications, 2012, 39(9): 7680-7689.
    [23]
    OSBORNE M F M. Brownian motion in the stock market[J]. Operations Research, 1959, 7(2): 145-173.
    [24]
    CLARK P K. A subordinated stochastic process model with finite variance for speculative prices[J]. General Information, 1973, 41(1): 135-155.
    [25]
    TAUCHEN G E, Pitts M. The price variability-volume relationship on speculative markets[J]. Econometrica, 1983, 51(2): 485-505.
    [26]
    GERVAIS S, Kaniel R, Mingelgrin D H. The high-volume return premium[J]. Journal Akuntansi Dan Keuangan, 2008, 56(3): 877-919.
    [27]
    田利辉, 王冠英. 我国股票定价五因素模型:交易量如何影响股票收益率?[J]. 南开经济研究, 2014, (2): 54-75.
    TIAN L H, WANG G Y. Asset pricing model of the Chinese stock market: How trading volumes influence the returns[J]. Nankai Economic Studies, 2014, (2): 54-75.
    [28]
    QIU W R, LIU X D, WANG L D. Forecasting Shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees[J]. Expert Systems with Applications, 2012, 39(9): 7680-7689.
    [29]
    ZHANG X, FUEHRES H, GLOOR P A. Predicting Stock market indicators through twitter “I hope it is not as bad as I fear”[J]. Procedia - Social and Behavioral Sciences, 2011, 26: 55-62.
    [30]
    BOLLEN J, MAO H N, ZENG X J. Twitter mood predicts the stock market[J]. Journal of Computational Science, 2011, 2(1): 1-8.)
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML
    [1]
    陈迪红, 杨湘豫, 李华中. 中国证券市场指数波动的周期分析[J]. 湖南大学学报, 2003, 30(5): 88-91.
    CHEN Dihong, YANG Xiangyu, LI Huazhong. Analysis of cycle about fluctuation of stock index in Chinese securities market[J]. Journal of Hunan University, 2003, 30(5): 88-91.
    [2]
    周佰成, 周建文, 方炬. 中、美证券市场的波动周期比较[J]. 经济纵横, 2006, (5): 72-73.
    [3]
    田俊刚, 梁红漫. 中国股票市场周期性研究[J]. 武汉金融, 2008, (7): 14-16, 36.
    [4]
    黄继平, 黄良文. 中国股市波动的周期性研究[J]. 统计研究, 2003, (11): 9-14.
    [5]
    董直庆, 夏小迪. 我国通货膨胀和股市周期波动共变性和非一致性再检验[J]. 经济学家, 2010, (3): 73-80.
    [6]
    CHENG C H, CHEN Y S, WU Y L. Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model[J]. Expert Systems with Applications, 2009, 36(2): 1826-1832.
    [7]
    张韬, 冯子健, 杨维中, 等. 模糊时间序列分析在肾综合征出血热发病率预测的应用初探[J]. 中国卫生统计, 2011, 28(2): 146-150.
    [8]
    钱冰冰. Type-2模糊系统在黄金价格预测中的应用[J]. 佳木斯大学学报, 2007, 25(3): 397-399.
    [9]
    SONG Q, CHISSOM B S. Forecasting Enrollments With Fuzzy Time Series[J]. Fuzzy Sets and Systems, 1993, 54(93): 1-9.
    [10]
    LEE M H, EFENDI R, ISMAIL Z. Modified weighted for enrollment forecasting based on fuzzy time series[J]. MATEMATIKA, 2009, 25(1): 67-78.
    [11]
    CHEN S M. Forecasting enrollments based on fuzzy time series[J]. Fuzzy Sets and Systems, 1996, 81(3): 311-319.
    [12]
    HUARNG K, YU H K. A type 2 fuzzy time series model for stock index forecasting[J]. Physica A: Statistical Mechanics and its Applications, 2005, 353(1-4): 445-462.
    [13]
    CHENG C H, CHEN T L, CHIANG C H. Trend-weighted fuzzy time-series model for TAIEX forecasting[C]// Proceedings of the 13th International Conference on Neural Information Processing. Hong Kong, China: ACM Press, 2006, 4234: 469-477.
    [14]
    CHU H H, CHEN T L, CHENG C H, et al. Fuzzy dual-factor time-series for stock index forecasting[J]. Expert Systems with Applications, 2009, 36(1): 165-171.
    [15]
    TING J L. Causalities of the Taiwan stock market[J]. Physica A: Statistical Mechanics and its Applications, 2003, 324(1-2): 285-295.
    [16]
    KARPOFF J M. The relation between price changes and trading volume: A survey[J]. The Journal of Financial and Quantitative Analysis, 1987, 22(1): 109-126.
    [17]
    金春雨, 郭沛. 我国股票市场量价关系的实证研究--基于上证指数的VAR模型分析[J]. 价格理论与实践, 2010, (9): 60-61.
    [18]
    王重, 张文转. 股票指数与股票市场技术要素的实证分析[J]. 现代商贸工业, 2008, 20(2): 159-160.
    [19]
    ALADAG C H, BASARAN M A, EGRIOGLU E, et al. Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations[J]. Expert Systems with Applications, 2009, 36(3): 4228-4231.
    [20]
    EGRIOGLU E, ALADAG C H, YOLCU U, et al. Finding an optimal interval length in high order fuzzy time series[J]. Expert Systems with Applications, 2010, 37(7): 5052-5055.
    [21]
    CHEN S M, WANG N Y, PAN J S. Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships[J]. Expert Systems with Applications, 2009, 36(8): 11070-11076.
    [22]
    QIU W, LIU X, WANG L. Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees[J]. Expert Systems with Applications, 2012, 39(9): 7680-7689.
    [23]
    OSBORNE M F M. Brownian motion in the stock market[J]. Operations Research, 1959, 7(2): 145-173.
    [24]
    CLARK P K. A subordinated stochastic process model with finite variance for speculative prices[J]. General Information, 1973, 41(1): 135-155.
    [25]
    TAUCHEN G E, Pitts M. The price variability-volume relationship on speculative markets[J]. Econometrica, 1983, 51(2): 485-505.
    [26]
    GERVAIS S, Kaniel R, Mingelgrin D H. The high-volume return premium[J]. Journal Akuntansi Dan Keuangan, 2008, 56(3): 877-919.
    [27]
    田利辉, 王冠英. 我国股票定价五因素模型:交易量如何影响股票收益率?[J]. 南开经济研究, 2014, (2): 54-75.
    TIAN L H, WANG G Y. Asset pricing model of the Chinese stock market: How trading volumes influence the returns[J]. Nankai Economic Studies, 2014, (2): 54-75.
    [28]
    QIU W R, LIU X D, WANG L D. Forecasting Shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees[J]. Expert Systems with Applications, 2012, 39(9): 7680-7689.
    [29]
    ZHANG X, FUEHRES H, GLOOR P A. Predicting Stock market indicators through twitter “I hope it is not as bad as I fear”[J]. Procedia - Social and Behavioral Sciences, 2011, 26: 55-62.
    [30]
    BOLLEN J, MAO H N, ZENG X J. Twitter mood predicts the stock market[J]. Journal of Computational Science, 2011, 2(1): 1-8.)
    Recommended articles
    TrendMD
    Volume 46 Issue 3 page: 238-246
    Cover

    Article Metrics

    Article views (36) PDF downloads(67)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return