摘要
创立金融市场的原本目的是促进价格向价值的回归，但这种回归并不如预想的简单。由于市场内外各方力量对于价格和价值的认同并不一致，以至于金融市场波动性的产生。这就允许市场参与者无需掌握实体资本，仅仅通过分析价格走势，判断波动程度，就能获取丰厚利润。这大大刺激了金融市场的投机行为。而21世纪以来，现代金融市场中涌现了大量基于微观交易策略的投资方式。这种投资方式完全不同于CAPM所定义的投资组合，仅仅通过特定的技术指标和参数优化所得出的交易信号进行交易。这种技术分析色彩极强的交易策略长久以来都被学术界所质疑和批判，但技术分析仍然顽强的存活到现在，并在1990年代出现了复兴。这有两个方面的原因。
其一，在1970年代提出的EMH理论不断地受到质疑，大量的市场研究都证实金融市场的有效性只存在于数学模型之中。真实世界的投资者不仅不具备完全信息，其理性程度也是参差不齐的。代表性的重大发现应当是行为金融学中提出的反馈交易和羊群行为。前者指出投资者的非理性所导致的追涨杀跌行为，这一行为加重了股票价格的涨跌趋势、并延缓了价格恢复的过程。而后者指出，投资者群体性的非理性会导致股票价格与股票价值的长期偏离，在羊群行为突出的金融市场，市场对于资本的配置是极度低效率的。
其二，技术分析的争论促进了有关定量分析方法的产生。例如各种重抽样方法、滑动估计法、利用统计方法量化技术指标、交易指标选择中的后验知识判断等。虽然主流观点认为：在考虑了交易成本的情况下，技术分析基本无效。那为何技术分析为何没有消亡，反而广泛流传?进行了大量文献回顾后，我们发现了一些被主流研究忽略的但对于技术分析十分重要的地方：首先，以往的实证分析所选用的技术指标以过滤规则、移动平均法则为主。但仅凭这几类指标的实证结论就断言技术分析的无效，显得过于主观；其次，技术分析可以很灵敏的发现异常的价格波动，同时也会出现大量不显著的交易。对这些异常价格观测值在进行统计分析时，会破坏有关统计检验的分布假设。而大量不显著的交易会降低技术分析通过统计检验显著性；最后，技术分析强调的时效性被实证研究所忽略。本文所回顾的文献几乎都采用了逐日数据，甚至逐周数据。然而逐日数据仅提供了当天交易的信息汇总，本身并不能提供价格变化的过程信息，并不适合用于技术分析。
本研究选择了包括了40只全年正常交易的股票的逐笔交易数据，原始数据占用的硬盘空间超过1．3G。可以说，本研究所采用的逐笔数据无论是从数据质量上、或是从数据规模上都是所查阅到的同类研究无法达到的。在完成了耗时一月之久的实证分析后，所得出的大量分析结论不仅具有很强的理论参考价值，对于微观交易策略制定也具有极强的实践指导意义。具体的，本研究的主要内容有如下一些。
(一)通过遗传算法赋予交易策略更好的参数估计，通过对交易策略组合的逐日收益率分析，发现了一些微观交易策略的基本特征：(1)交易策略对于价格波动具有很强的敏感性，如果波动程度越强，则获得高额收益的可能越大，相反则无所获利；(2)交易策略对于抽样频率很敏感。即使是同一种类型的交易策略，应用在不同频率的交易价格数据上所得到的结果也相差很大；(3)由于交易策略的高度并发性，并不符合统计检验假设的前提条件，但可以采用VaR方法从潜在损失的角度评价交易策略优劣。
(二)以经典配对交易为代表的微观交易策略采用一定的规则选择配对资产，不仅交易规则简单，还可以同时对多项配对资产进行同时交易。强烈的实时价格驱动机制与平行交易的特点，使得配对交易在获取巨额收益率的同时也会出现巨额的亏损，可见对于微观交易策略，保持流动性和风险控制显得尤为重要。而考虑到金融资产价格序列的特点，本文提出对对数价格差建立协整关系，并依照协整关系的显著性建立配对资产。这一交易策略的逐日收益率水平要优于经典方法。特别是在抽样频率为30分钟和60分钟时，即使考虑交易成本，VaR值与无交易成本的情形也十分接近。这表明协整关系在验证配对资产价差关系的稳定性和动态性上要好于经典的标准差方法。
(三)结合套利定价理论，本文重新建立了以收益率差余量的状态空间模型，并采用卡尔曼滤波预测交易信号。所得到的组合逐日收益率不仅具有超高的异常值出现，而且在各个抽样频率下都表现出了明显的正偏性。虽然会面临超过20倍的亏损，但其最高40倍的获利能力不容小视，大量的异常值的出现代表了交易策略对超额收益的追求，而大量正异常值所导致的正偏性，表明了基于滤波的配对交易策略的有效性。
(四)对上述的各种微观交易策略进一步总结。在市场中性和套利定价理论的框架下，重新剖析了交易策略的超额收益的来源。比较深入的讨论了基于市场中性的微观交易策略的含义和背后所蕴含的风险溢价。随后提出观点：所谓市场中性其实不过是利用目前还没有被整个市场所发现或所考虑的个别风险因素进行套利。并建立了一个简明的动态模型解释交易策略对于真实的市场收益率波动性的影响机制，很好的解释了由于选择投资方式的个体逐利行为导致了整个市场的真实波动性增加的现象。
The purpose of running a financial market is to contribute to the regression of asset price to its value. Since various parts in and out of this market show their opinions with bids and asks, which lead to the chaos of prices. Among chaotic prices, professionals cultivate returns by predicting trends and estimating volatilities. And no capital asset is required in this business. Such a free lunch stimulates strong speculation in financial markets. From 21th century on, lots of trading strategy-based investment prevailed over financial market. This kind of investments is quite different from the definition of CAPM theory, it generates trading signals by propitiate technical indicators, so called technical analysis. This strategy has burden heavy critics from academia for decades. However technical analysis survived and even shew prosperity in the late 1990s. In this research, two reasons of many are given to explain such a prosperity.
On the one side, the famous EMH theory born in 1970s had been doubted for years, and hundreds of empirical analysis shew the fact that efficient market lives only in mathematical models. In the real world, investor is neither fully-informed nor homogeneous rationale. Two concrete discoveries from behavior finance are moment trading and herding behavior. The former tells us that investors who are lack of rationale, tend to follow the price and buy when price goes up, and sell when down. While the latter indicates that the irrational behavior at market level will force price divert from its value for quite a while, and hinder probable recursive process in a longer period. So in a market existing strong herding behaviors, the function of asset allocation is quite trivial.
On the other side, discussions against technical analysis lead to the generation of resampling methods, sliding estimation, statistical analysis-embedded technical analysis, bayesian judgement for choosing trading indicators, data selection techniques, and etc. However, the mainstream opinion still goes like "given the condition of transaction cost, technical analysis is inefficient" . Then following question appears hereby: if technical analysis is useless, then why it still lives with us and quite well? Is it because of defaults hidden in the opponents' critics, or simply because of the stickiness of professionals and analysts? After a broad review, we find aspects which had been neglected by mainstream study but quite essential to the success of technical analysis: (1) most recent empirical research are based on filter rules, moving averages and etc, which are easy for programming and quite popular among analysts. But there are still other hundreds of technical indicators being used in the same time. So by testing the defaults of the few and alleging all are not suitable and not reasonable as well. (2) technical indicator aims to capture variation in prices, not to explain it. In order to do well in predicting variations, more inferior predictions can be made. The extremes are never the favor of statistical tests, and a great number of insignificant trading signals can also lower the significant level. (3) the timing ability are essential to technical analysis, while it rarely appears on the formal research works. Among those empirical works reviewed, daily close price are mostly used, which obviously only supplies the result of daily trading, daily prices variations, which is the main source of interest for technical analysis, can be found in close price, plus some even use weekly data, whose conclusions maybe far from reality.
This study use a huge by-hand dataset over 1.3 gigabytes, containing each transaction records of 40 stocks in a year. Not a dataset used in all research works we reviewed, no matter in quality or in data scale, can be compared with ours. After one month's empirical study, results and conclusions show values for theoretical references, and it is quite valuable for practical designing of micro-level trading strategy as well. The main works done can be concluded as follows.
Firstly, we use genetic algorithm to give joint technical indicators a better estimation for their parameters. Some basic characteristics of trading strategy are found: (1) trading strategy is quite sensitive to the price variation,the greater the variation, the higher the gains; otherwise less or few. (2) trading strategy can be sensitive to the price sampling intervals. Given the same strategy, the different sampling frequency can generate many different results. (3) due to its parallel trading property, the behavior of its returns cannot be fitted in the traditional statistical analysis. Yet the value at risk method shows some potential to solve this problem.
Secondly, we use classical pairs trading to select pairing assets, which is quite easy and has good property of parallel trading. Strong price-driven mechanism and parallel trading, can give huge return and lost together. This reminds us that keeping an eye on liquidity and risk management is the key to success of trading strategy. Further considering the general attributes of financial times series, co-integration method is introduced in this study. After some modification, i.e., using the difference of logged prices instead of logged prices, we show that co-integration based pairs trading method outperforms classical one in many ways. Especially, in the sampling frequency of 30 and 60 minutes, even considering transaction costs, co-integration pairs trading does the same works as the performance of considering no transaction costs, and their VaRs are also close. So we concludes that co-integration methods shows better fitness for dynamical stability for pairs assets.
Thirdly, under the arbitrage pricing theory(APT), we use the difference of returns to build a state space model. To make better prediction, kalman filter is incorporated. Results show that strategy-based portfolio's daily returns has extremely high observations, and positive skewness shows the potential of positive gains. Facing with 20 times lost, its 40 times gain is still lucrative. Again here we see, extremes represents eagerness of pursuing extreme profits.
Lastly, to give a more general view of these trading strategies discussed, we incorporate ideas of market neutral investing strategy and APT theory to decompose sources of returns of trading strategy. After an in-depth discussion about market neutral and risk premium, we conclude that: market neutral strategy is simply arbitrage over risks which is treated as unique risk or has not been considered by most investors. Additionally, we develop a easy and clear dynamic model to explain the mechanism that how strategy impacts market volatility. This model shows clearly that profit-pursuing investment-swinging behavior is one main source of volatility in financial market.
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