weekly calendar effects 周日历效应
Unlike low frequency data, high frequency data has the calendar effects and long memory volatility.
与低频数据不同，高频数据通常具有“日历效应”和波动长记忆性。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN 是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
Calendar effects mean that market returns associate with the specific transaction date in stock market, there re two important forms: day of the week effects and month of the year effects.
日历效应是指证券市场出现的在某一特定时间进行交易可以获得超额收益率的现象，它的表现形式主要有星期效应和月份效应。
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