Beat the Market II

Gary Smith
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Abstract

Nowadays, technical analysts are called quants. Being overly impressed by computers, we are overly impressed by quants using computers instead of pencils and graph paper. Quants do not think about whether the patterns they discover make sense. Their mantra is, “Just show me the data.” Indeed, many quants have PhDs in physics or mathematics and only the most rudimentary knowledge of economics or finance. That does not deter them. If anything, their ignorance encourages them to search for patterns in the most unlikely places. The logical conclusion of moving from technical analysts using pencils to quants using computers is to eliminate humans entirely. Just turn the technical analysis over to computers. A 2011 article in the wonderful technology magazine Wired was filled with awe and admiration for computerized stock trading systems. These black-box systems are called algorithmic traders (algos) because the computers decide to buy and sell using computer algorithms in place of human judgment. Humans write the algorithms that guide the computers but, after that, the computers are on their own. Some humans are dumbstruck. After Pepperdine University invested 10 percent of its portfolio in quant funds in 2016, the director of investments argued that, “Finding a company with good prospects makes sense, since we look for under valued things in our daily lives, but quant strategies have nothing to do with our lives.” He thinks that not having the wisdom and common sense acquired by being alive is an argument for computers. He is not alone. Black-box investment algorithms now account for nearly a third of all U.S. stock trades. Some of these systems track stock prices; others look at economic and noneconomic data and dissect news stories. They all look for patterns. A momentum algorithm might notice that when a particular stock trades at a higher price for five straight days, the price is usually higher on the sixth day. A mean-reversion algorithm might notice that when a stock trades at a higher price for eight straight days, the price is usually lower on the ninth day. A pairs-trading algorithm might notice that two stock prices usually move up and down together, suggesting an opportunity when one price moves up and the other doesn’t.
战胜市场2
如今,技术分析师被称为量化分析师。由于对计算机印象深刻,我们对使用计算机而不是铅笔和方格纸的量化分析师印象深刻。量化分析师不会考虑他们发现的模式是否有意义。他们的口头禅是:“给我看数据。”事实上,许多量化分析师拥有物理学或数学博士学位,但对经济学或金融学只有最基本的了解。这并不能阻止他们。如果说有什么不同的话,那就是他们的无知促使他们在最不可能的地方寻找模式。从使用铅笔的技术分析师转变为使用计算机的量化分析师,合乎逻辑的结论是完全消除人类。把技术分析交给电脑就行了。2011年,优秀的科技杂志《连线》(Wired)发表了一篇文章,充满了对计算机化股票交易系统的敬畏和钦佩。这些黑箱系统被称为算法交易员(algos),因为计算机使用计算机算法代替人类判断来决定买卖。人类编写指导计算机的算法,但在那之后,计算机就只能靠自己了。有些人目瞪口呆。2016年,佩珀代因大学(Pepperdine University)将其投资组合的10%投资于量化基金后,投资总监认为,“找到一家前景良好的公司是有道理的,因为我们在日常生活中寻找被低估的东西,但量化策略与我们的生活无关。”他认为,没有通过活着获得的智慧和常识是支持计算机的理由。他并不孤单。黑箱投资算法目前占美国股票交易总量的近三分之一。其中一些系统跟踪股票价格;其他人则关注经济和非经济数据,剖析新闻报道。他们都在寻找模式。动量算法可能会注意到,当某只股票连续5天以较高价格交易时,第六天的价格通常会更高。均值回归算法可能会注意到,当一只股票连续8天以较高价格交易时,第9天的价格通常较低。配对交易算法可能会注意到,两只股票的价格通常同时上下波动,这表明当一个价格上涨而另一个价格下跌时存在机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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