The Experts below are selected from a list of 88533 Experts worldwide ranked by ideXlab platform
Chenchi Huang - One of the best experts on this subject based on the ideXlab platform.
-
fuzzy dual factor time series for Stock Index forecasting
Expert Systems With Applications, 2009Co-Authors: Hsinghui Chu, Tailiang Chen, Chinghsue Cheng, Chenchi HuangAbstract:There is an old Wall Street adage goes, ''It takes volume to make price move''. The contemporaneous relation between trading volume and Stock returns has been studied since Stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China's Stock market. Pacific-Basin Finace Journal, 12, 541-564], Hodgson et al. [Hodgson, A., Masih, A. M. M., & Masih, R. (2006). Futures trading volume as a determinant of prices in different momentum phases. International Review of Financial Analysis, 15, 68-85], and Ting [Ting, J. J. L. (2003). Causalities of the Taiwan Stock market. Physica A, 324, 285-295] have found the correlation between Stock volume and price in Stock markets. To verify this saying, in this paper, we propose a dual-factor modified fuzzy time-series model, which take Stock Index and trading volume as forecasting factors to predict Stock Index. In empirical analysis, we employ the TAIEX (Taiwan Stock exchange capitalization weighted Stock Index) and NASDAQ (National Association of Securities Dealers Automated Quotations) as experimental datasets and two multiple-factor models, Chen's [Chen, S. M. (2000). Temperature prediction using fuzzy time-series. IEEE Transactions on Cybernetics, 30 (2), 263-275] and Huarng and Yu's [Huarng, K. H., & Yu, H. K. (2005). A type 2 fuzzy time-series model for Stock Index forecasting. Physica A, 353, 445-462], as comparison models. The experimental results indicate that the proposed model outperforms the listing models and the employed factors, Stock Index and the volume technical indicator, VR(t), are effective in Stock Index forecasting.
Bin Gao - One of the best experts on this subject based on the ideXlab platform.
-
Forecasting Stock Index futures returns with mixed-frequency sentiment
International Review of Economics & Finance, 2017Co-Authors: Bin Gao, Chunpeng YangAbstract:Using the data in Chinese financial market, mixed-frequency Stock Index futures sentiment and mixed-frequency Stock Index sentiment are constructed according to MIDAS model. We test whether mixed-frequency Stock Index futures sentiment and mixed-frequency Stock Index sentiment have predictive power on Stock Index futures returns. The empirical results show that mixed-frequency Stock Index futures sentiment factors have more predictive power than mixed-frequency Stock Index sentiment factors and Fama-French three factors. In out-sample forecast, we show that sentiment trading strategy provides a more positive returns than time series momentum trading strategy and passive long positions.
-
The term structure of sentiment effect in Stock Index futures market
The North American Journal of Economics and Finance, 2014Co-Authors: Chunpeng Yang, Bin GaoAbstract:In this paper, we construct Stock Index futures sentiment and Stock Index sentiment at daily, weekly, and monthly frequencies. We empirically study the contribution to Stock Index futures returns of the related Stock Index futures sentiment and Stock Index sentiment. The empirical results show the term structure character of Stock Index futures sentiment and Stock Index sentiment, i.e., sentiment aggregate effect and sentiment spillover effect are both monotonous decreasing function of the time term and sentiment aggregate effect is more significant than sentiment spillover effect to the futures returns. Short maturity contract is more significantly affected by Stock Index futures sentiment and Stock Index sentiment than long maturity contract. Our results are helpful for understanding the financial phenomena that irrational factors have more effect in short-term decision-making and broaden the sentiment research perspective.
Chunpeng Yang - One of the best experts on this subject based on the ideXlab platform.
-
Forecasting Stock Index futures returns with mixed-frequency sentiment
International Review of Economics & Finance, 2017Co-Authors: Bin Gao, Chunpeng YangAbstract:Using the data in Chinese financial market, mixed-frequency Stock Index futures sentiment and mixed-frequency Stock Index sentiment are constructed according to MIDAS model. We test whether mixed-frequency Stock Index futures sentiment and mixed-frequency Stock Index sentiment have predictive power on Stock Index futures returns. The empirical results show that mixed-frequency Stock Index futures sentiment factors have more predictive power than mixed-frequency Stock Index sentiment factors and Fama-French three factors. In out-sample forecast, we show that sentiment trading strategy provides a more positive returns than time series momentum trading strategy and passive long positions.
-
The term structure of sentiment effect in Stock Index futures market
The North American Journal of Economics and Finance, 2014Co-Authors: Chunpeng Yang, Bin GaoAbstract:In this paper, we construct Stock Index futures sentiment and Stock Index sentiment at daily, weekly, and monthly frequencies. We empirically study the contribution to Stock Index futures returns of the related Stock Index futures sentiment and Stock Index sentiment. The empirical results show the term structure character of Stock Index futures sentiment and Stock Index sentiment, i.e., sentiment aggregate effect and sentiment spillover effect are both monotonous decreasing function of the time term and sentiment aggregate effect is more significant than sentiment spillover effect to the futures returns. Short maturity contract is more significantly affected by Stock Index futures sentiment and Stock Index sentiment than long maturity contract. Our results are helpful for understanding the financial phenomena that irrational factors have more effect in short-term decision-making and broaden the sentiment research perspective.
Hsinghui Chu - One of the best experts on this subject based on the ideXlab platform.
-
fuzzy dual factor time series for Stock Index forecasting
Expert Systems With Applications, 2009Co-Authors: Hsinghui Chu, Tailiang Chen, Chinghsue Cheng, Chenchi HuangAbstract:There is an old Wall Street adage goes, ''It takes volume to make price move''. The contemporaneous relation between trading volume and Stock returns has been studied since Stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China's Stock market. Pacific-Basin Finace Journal, 12, 541-564], Hodgson et al. [Hodgson, A., Masih, A. M. M., & Masih, R. (2006). Futures trading volume as a determinant of prices in different momentum phases. International Review of Financial Analysis, 15, 68-85], and Ting [Ting, J. J. L. (2003). Causalities of the Taiwan Stock market. Physica A, 324, 285-295] have found the correlation between Stock volume and price in Stock markets. To verify this saying, in this paper, we propose a dual-factor modified fuzzy time-series model, which take Stock Index and trading volume as forecasting factors to predict Stock Index. In empirical analysis, we employ the TAIEX (Taiwan Stock exchange capitalization weighted Stock Index) and NASDAQ (National Association of Securities Dealers Automated Quotations) as experimental datasets and two multiple-factor models, Chen's [Chen, S. M. (2000). Temperature prediction using fuzzy time-series. IEEE Transactions on Cybernetics, 30 (2), 263-275] and Huarng and Yu's [Huarng, K. H., & Yu, H. K. (2005). A type 2 fuzzy time-series model for Stock Index forecasting. Physica A, 353, 445-462], as comparison models. The experimental results indicate that the proposed model outperforms the listing models and the employed factors, Stock Index and the volume technical indicator, VR(t), are effective in Stock Index forecasting.
Tailiang Chen - One of the best experts on this subject based on the ideXlab platform.
-
fuzzy dual factor time series for Stock Index forecasting
Expert Systems With Applications, 2009Co-Authors: Hsinghui Chu, Tailiang Chen, Chinghsue Cheng, Chenchi HuangAbstract:There is an old Wall Street adage goes, ''It takes volume to make price move''. The contemporaneous relation between trading volume and Stock returns has been studied since Stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China's Stock market. Pacific-Basin Finace Journal, 12, 541-564], Hodgson et al. [Hodgson, A., Masih, A. M. M., & Masih, R. (2006). Futures trading volume as a determinant of prices in different momentum phases. International Review of Financial Analysis, 15, 68-85], and Ting [Ting, J. J. L. (2003). Causalities of the Taiwan Stock market. Physica A, 324, 285-295] have found the correlation between Stock volume and price in Stock markets. To verify this saying, in this paper, we propose a dual-factor modified fuzzy time-series model, which take Stock Index and trading volume as forecasting factors to predict Stock Index. In empirical analysis, we employ the TAIEX (Taiwan Stock exchange capitalization weighted Stock Index) and NASDAQ (National Association of Securities Dealers Automated Quotations) as experimental datasets and two multiple-factor models, Chen's [Chen, S. M. (2000). Temperature prediction using fuzzy time-series. IEEE Transactions on Cybernetics, 30 (2), 263-275] and Huarng and Yu's [Huarng, K. H., & Yu, H. K. (2005). A type 2 fuzzy time-series model for Stock Index forecasting. Physica A, 353, 445-462], as comparison models. The experimental results indicate that the proposed model outperforms the listing models and the employed factors, Stock Index and the volume technical indicator, VR(t), are effective in Stock Index forecasting.
