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代写被发现:定量交易中的技术和模式识别

在当今时代,人们有理由寻求一种称为配对交易的模式交易形式。配对交易技术是一种通常考虑股票之间的相关性,以找出哪一种股票的收益率模式最高。这是一种需要考虑两支以上股票的技术形式。通常情况下,在高波动性的情况下,定量技术是首选,因此在较大的对冲基金和共同基金中得到了应用。其次,这些定量交易的定量应用将需要大量使用软件和支持性技术。并非所有公司都能负担得起,因此,只有拥有良好金融背景的公司才能负担得起与定量交易进行互动。作为定性交易的一种替代方式,定量交易被视为以专家分析的形式提供洞见。事实上,一些常见的定量交易过程依赖于那些也可以是定性交易的一部分,但包含更多数据集的东西,这在定性交易中是可能的(Loughran, and Vijh, 1997)。

首先,在定量交易中,要进行技术和模式识别。定量交易模型也称为量化模型。这些模型将以一种确定自然发生的趋势的方式寻找财务数据,同时也将确定这些趋势中任何形式的隐藏关系。使用技术,例如股票套利,是很方便的,因为这是一种技术,在这种技术中,定量模型将能够搜索彼此密切相关的股票价格。相互密切相关的股票价格可能会彼此下跌,或者对彼此产生某种形式的密切影响。在这种情况下,使用可以分析链接的模型是非常有用的。定量交易有助于分析相同的(Loughran,和Vijh, 1997)。

例如,考虑必须分析航空公司股票价格的情况(LeBaron, 2001)。现在石油的股价与航空公司的股价紧密相连,因为无论石油环境发生什么变化,都会对航空公司环境产生影响。另一方面,考虑石油勘探公司的股票,油价的上涨会对航空公司股票的价格产生负面影响,实际上会对他们的股票产生某种形式的积极影响。石油勘探价格将从油价的上涨中受益。这里用来理解投资的量化交易技术可能最终会做空航空股(Agarwal, and Naik, 2004)。

定量价值投资是定量交易的一种形式,投资者首先会根据价格与内在价值的相对性质来寻找股票。当公司特别追求更高的回报,但市盈率较低时,这通常被用来作为市盈率。这一结果所带来的动量投资形式,实际上会使金融市场在高度波动的情况下,在较短的时间内改变方向,定量交易变得更好(Agarwal, and Naik, 2004)。

定量交易实际上依赖于市场动量,市场动量越大,对定量交易交易越有利。这是另一个需要理解的观点,它植根于现代投资组合理论的对立面。现代投资组合理论强调传统意义上的有效市场假说。另一方面,有效市场理论的规范受到量化交易的挑战。有效市场理论的运行前提是,在任何给定的市场,在任何时间,市场价格将尽可能的信息有效(Agarwal, and Naik, 2004)。


代写被发现 :定量交易中的技术和模式识别

In more current times there is a reason to seek out a form of pattern trading called a pair trading. The pair trading technique is one where correlations between stocks are usually considered in order to find out which makes the most yield pattern. This is a form of technique that would employ the consideration of more than two stocks. Usually the quantitative technique is preferred in the use of high volatile situations and as such finds application in larger hedge funds and mutual funds. Secondly these quantitative applications for quantitative trading will require much use of software and supportive technologies. Not all companies would be able to afford this and hence only companies that have a good financial background can afford to interplay with quantitative trading. As an alternative to qualitative trading, quantitative trading is seen to offer insights in the form of expert analysis. Some of the common quantitative trading processes in fact rely on those things that could also be part of the qualitative but inclusive of much more data sets that can be possible in qualitative trading (Loughran, and Vijh, 1997).
Primarily, in quantitative trading, techniques and pattern identification are made. Quantitative Trading models are also called as quant models. These models will seek out financial data in a way where naturally occurring trends would be identified and also any forms of hidden relationships in these trends will also be identified. The use of techniques, such as the stock arbitrage comes in handy as this is a technique where the quant model would be enabled for the search of stock prices which are closely related to one another. Stock prices closely related to one another might bring down one another or would have some form of a close effect on one another. In this context the use of models where the link can be analyzed is very helpful. Quantitative Trading helps in the analysis of the same (Loughran, and Vijh, 1997).
For instance, consider the situation where airline stock prices have to be analyzed (LeBaron, 2001). Now the stock price of oil is closely linked with the stock price of airlines as whatever changes that happen in the oil context will also have an impact on the airline context. On the other hand, consider the stock for oil exploration companies, an increase in the oil price which would adversely affect the stock price of airline stock would actually have a form of positive impact on their stock. Oil exploration prices will benefit from such a rise of oil price. A Quantitative Trading technique used here to understand investments might end up shorting the airline stocks (Agarwal, and Naik, 2004).
In Quantitative value investing which is a form of Quantitative Trading it is seen that the investor would first search for stocks based on the relative nature of price to intrinsic value. This is usually made use of when the company specifically seek out for higher returns but with low P/E earnings called as the price to earnings ratio. The form of momentum investing this results in would actually make Quantitative Trading better for financial markets in high volatility where they change directions in a shorter span of times (Agarwal, and Naik, 2004).
Quantitative Trading in fact relies on market momentum and the better the market momentum then the better it is for the Quantitative Trading trade. This is yet another point to understand as being rooted in the opposition of the modern portfolio theory. The modern portfolio theory strongly emphasizes on the efficient market hypothesis which has been followed in a traditional sense. On the other hand, the norms of the efficient market theory are challenged by the Quantitative Trading. Efficient market theory operates on the premise that in any given market at any time, the market prices would be as informationally efficient as possible (Agarwal, and Naik, 2004).