本文選題：小波分析　切入點：消噪　出處：《上海交通大學》2012年碩士論文　論文類型：學位論文

【摘要】：本文從噪聲交易著手，引入對于市場有效性的質(zhì)疑，介紹了挑戰(zhàn)有效市場假說的三種理論，分別是市場微觀結構理論，，行為金融學和分形市場假說。 基于以上介紹，本文認為市場是不完全有效的，而正是基于此，趨勢交易才能長期存在于市場。然而，趨勢交易也存在其特有的問題，那就是在震蕩行情下其收益率回撤可能會過大，以至于引發(fā)可能的清盤風險。 為了減小可能的收益率回撤，本文試圖通過小波消噪將行情中的短期的交易噪音過濾掉，突出行情的趨勢性，減少噪聲交易對趨勢交易產(chǎn)生干擾，從而提高趨勢交易的績效。 為此，本文著重介紹了小波分析的相關理論，并以此為基礎，應用HAAR小波對股指日線數(shù)據(jù)，股指15分鐘數(shù)據(jù)，上證綜指，深證成指，深ETF100日線數(shù)據(jù)等原始行情進行了一級分解和二級分解，并對各級分解的小波系數(shù)進行消噪，使用的消噪方法有默認閥值小波消噪方法，給定閥值小波消噪方法和強制小波消噪方法，最后得到不同的消噪后的行情。 通過比較同一套趨勢交易策略在原始行情及其相應的消噪后行情上的歷史回測績效，驗證小波消噪對于提升趨勢交易績效的作用。實證結果表明小波消噪對于趨勢交易績效確實有顯著的提升作用。 本文還發(fā)現(xiàn)，小波二級分解效果普遍優(yōu)于一級分解，最簡單的強制小波消噪方法效果并不比其他算法相對較為復雜的小波消噪方法差。 基于以上實證結果和發(fā)現(xiàn)，本文認為小波消噪技術在趨勢交易中具備相當?shù)膽脙r值。 本文涉及的小波變換及消噪在MatLab上編程完成，趨勢交易策略代碼編寫及歷史績效回測在行情分析系統(tǒng)MultiCharts上完成，相關代碼參見附錄。
[Abstract]:This paper starts with noise trading, introduces the doubts about the effectiveness of the market, and introduces three theories that challenge the effective market hypothesis, which are market microstructure theory, behavioral finance and fractal market hypothesis respectively.
Based on the above introduction, this paper thinks that the market is not fully effective, which is based on this, the long-term trend trading can exist in the market. However, the trend of trading still has its problems, it is in the market shocks the yield retracement may be too large, that may lead to the risk of liquidation.
In order to reduce the possible return rate, we try to filter out the short-term trading noise through the wavelet denoising, highlighting the trend of the market, reducing the interference of the noise trading to the trend trading, and improving the performance of the trend trading.
Therefore, this paper introduces the theory of wavelet analysis, and on this basis, the application of HAAR wavelet on stock index daily data, the stock index data of 15 minutes, Shanghai, Shenzhen, deep ETF100 data of the original daily market level decomposition and two level decomposition, and the decomposition levels of wavelet coefficient denoising. Denoising method using the default threshold wavelet denoising method, wavelet threshold denoising method is given and the forced wavelet denoising method, finally obtains the different denoising after market.
By comparing the same trend trading strategy in the original market and the corresponding market after denoising the history test performance, verify the wavelet denoising for improving the performance of trend trading role. The empirical results show that the wavelet denoising has significantly improved function for the performance of trend trading.
It is also found that the effect of wavelet two level decomposition is generally better than that of the first order decomposition, and the simplest coercive wavelet denoising method is no more effective than other algorithms.
Based on the above empirical results and discovery, this paper holds that the wavelet denoising technology has considerable application value in the trend transaction.
The wavelet transform and de noising in MatLab are programmed. The trend trading strategy code compilation and historical performance re test are completed on the market analysis system MultiCharts. The related codes are attached to the appendix.

【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.51;F224

【參考文獻】

相關期刊論文 前10條

1 孫偉;;簡析有效市場假說與分形市場假說[J];北方經(jīng)濟;2010年24期

2 曾康霖;解讀行為金融學[J];財經(jīng)科學;2003年02期

3 何振亞,李文化,蔚承建;用于信號逼近的自適應時延小波神經(jīng)網(wǎng)絡[J];電子科學學刊;1998年05期

4 侯木舟,袁修貴;基于MATLAB的小波分析在股市技術分析中的應用[J];系統(tǒng)工程;2001年05期

5 李建華;李萬社;;小波理論發(fā)展及其應用(綜述)[J];河西學院學報;2006年02期

6 李平,曾勇,唐小我;市場微觀結構理論綜述[J];管理科學學報;2003年05期

7 朱朱朱;張君;秦虹;蔡旭;;一種基于實時數(shù)據(jù)誤差補償?shù)母盗⑷~測頻算法[J];電力系統(tǒng)保護與控制;2009年22期

8 陳軍飛,申富饒,王嘉松;股價指數(shù)時間序列的分形性質(zhì)分析[J];經(jīng)濟數(shù)學;2000年01期

9 孟衛(wèi)東,嚴太華,丁首營;上證指數(shù)奇異信號統(tǒng)計檢測的小波分析[J];經(jīng)濟問題;2001年02期

10 唐賢瑛,姜山;基于小波變換的股市技術分析模型與仿真[J];計算機仿真;2003年04期

相關碩士學位論文 前2條

1 郭曉霞;基于小波變換的超聲檢測信號去噪方法研究[D];江南大學;2008年

2 鄭紀安;基于小波分析和神經(jīng)網(wǎng)絡的金融時間序列預測研究[D];廈門大學;2009年

本文編號：1570940