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題名:理性投機行為與厚尾現象研究:最適化拔靴法
作者:林岳賢
作者(外文):Yuexian Lin
校院名稱:臺灣大學
系所名稱:財務金融學研究所
指導教授:楊朝成
學位類別:博士
出版日期:2009
主題關鍵詞:拔靴法理性投機正向回饋策略厚尾現象rational speculationpositive feedback tradingheavy-tail phenomenaboostrap
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中文摘要
傳統弱勢效率市場理論宣稱無法透過研究過去價格歷史持續獲利,本文嘗試探討另一種可能性:對無優勢資訊交易者而言,僅透過歷史價格擬定進出決策,長期獲利是否可能?若該企圖在實際資料能夠被實現,將與弱式效率市場不符。方法上,只要找到一種交易策略或法則,此法則不僅能持續獲利,且必須通過拔靴法 (bootstrapping methods) 測試,免於曲線過度配適問題 (curve fitting problems),則弱勢效率無法成立。理由是效率市場世界中,完全否定了這類交易策略或法則存在的可能性。研究亦發現,該獲利來源可能與厚尾現象有關。
本研究找到一個反例──正向回饋交易策略 (positive feedback strategy)──為一種可長期獲利之進出決策。正向回饋策略是指投資人對風險性資產的需求是價格的遞增函數,因此交易者表現於外的,即是追高殺低的行為。顯然,該法則與動能投資(momentum investing)密切相關。然而本文主張在動能交易中所建構之零成本投資組合在出清部位之前,有可能「先」發生巨大的浮動損失,而先前文獻的研究方法並沒有考慮到這點,因此文獻上對動能投資法獲利潛力的估計或許高估了。只要市場參與者存在財富限制,在實現獲利之前,投資人可能因為市場巨大的波動而被迫提前出場,若真是如此,那麼邏輯上出場後的市場行情,已經跟該投資人的損益沒有關係。這個效果在期貨市場尤為明顯,因為就面對財富限制的市場參與者而言,其風險忍受能力很大部分取決於槓桿使用程度。
本文目的有二:第一、從 DeLong et al. (1990) 理論模型出發,本文針對理性投機行為提出理論性觀點,並將之與市場厚尾現象連結。DeLong et al. (1990) 模型指出,追高殺低策略 (正向回饋) 極可能是理性的,特別是資訊越不明朗時,價格偏離基本面價值程度越高。我們根據這個理論的指導,設計一專門捕捉厚尾之正向回饋交易法則。該法則使用日內資料,要求交易者每天該做的事,就是追高殺低。然而在訊號出現當下,無情報優勢的交易者無法確知該筆交易帶來的將是利潤,或是虧損;交易者擁有的只是「信念」,相信市場有厚尾的信念。厚尾所代表的意義是,極端報酬出現機率足夠大,大到不僅超過交易成本,且彌補執行過程可能先出現的一連串小損失後,期望值仍為非負;然後在市場上追高殺低的後果,創造出厚尾。換言之,交易者是「先」有信念,然後根據該信念採取行動,若行動的結果的確創造出厚尾,將支撐該行動背後的信念,構成均衡。
本文第二個目的是回答一個關於交易領域公開的難題 (open question):全市場都在問,為何歷史回測結果,與實際績效不僅差異很大,且差異幾乎無例外地向下偏誤?曲線過度配適問題應如何避免?附錄中本文提出最適化拔靴法,嘗試回答此問題,並據以提出本文的意見與相關的解決之道。
Traditional wisdom regarding market efficiency claims that there is no hope making trading profits consistently by investigating the price history only. In this monograph,
we discover an alternative possibility---the result is against the weak-form efficiency. Methodologically, if a consistently profitable trading rule can be found and is well-adjusted for data-snooping bias, then the weak-form efficiency hypothesis could not be
sustained. This is true because such a thing cannot exist in the efficient market world.
The positive-feedback trading requires the demand function for a risky asset is increasing with respect to prices and is intimately related with the momentum investing. Nonetheless, we argue that a zero cost portfolio might generate enormous interim losses
before the liquidation of positions or realization of momentum profits. Since no one has unlimited wealth, investors who use momentum strategies in the hope of future
profits might be forced to exit the market prematurely due to huge interim paper losses. Consequently, the potential benefits from momentum investing must be conceded if
investors are wiped out from the market beforehand. This effect is especially significant in futures markets, where the leverage is crucial for participants with wealth constraint.
The purpose of this study is twofold. Firstly, originating from the idea of DeLong et. al (1990), we propose a theoretical argument for rational speculation that relates the positive-feedback trading to the heavy-tail phenomena. A simple yet realistic positive-feedback trading system is then constructed to capture heavy-tails in asset returns.
We realistically incorporate standard risk control mechanisms and examine the rule via resampling techniques to avoid curve fitting problems. The positive-feedback trading rule has passed the challenges of non-parametric blockwise bootstrapping methods. Extensive numerical experiments suggest that these trading profits might arise from the heavy-tail phenomena, and verse visa.
Secondly, there is an open question prevailing in the realm of program trading: why are the actual trading performances, with few exceptions, systematically worse than the historical back-testing results? It turns out that the answer is due to the institutional regularities and is quite straightforward under the perspectives of the bootstrapped optimization scheme. In our opinion, all the trading performance measurements would better be represented in a manner of bootstrapped distributions, rather than based on a single sample path. We hope our investigation for rational speculation and quantitative
trading provides helpful insights for both practitioners and academic researchers.
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