Can Private Investors Compete With Algorithmic Trading?
By Markus
A. Dieckmann
In the last ten years, the automated computer-based trading of securities, commonly known as algorithmic-trading or short algo-trading, has become more and more popular. Big hedge funds and leading investment banks are using algorithmic trading to improve their investing performance on the stock, currency and commodity markets and are investing huge amounts of money into the development of high performance computers and related programs. At the beginning, algorithmic trading was all about splitting bigger orders to hide the real intention of the buyer; today, however, high frequency trading (HFT) is very popular. This is a form of algorithmic trading where market data is analyzed in real-time, using even the smallest information advantages to open a position by way of an arbitrage deal. The investor earns due entirely to having a time advantage in a certain market and acting before others can react to information inputs. The usage of ultra-low latency networks and powerful software programs is key to success here. Even today, three quarters of the trading volume in the US is generated by algorithmic trading. So what consequences does algorithmic trading have for the private investor? There is no way to avoid the conclusion that private investors can hardly compete with the superior technical strength of big financial companies. This means that private investors have to accept the fact that they can no longer make money from using early financial information. They can only try to exploit the weak points of algorithmic trading programs in order to be successful on the stock markets. But what are the weak points of algorithmic trading and how can one use them? First of all, it's important to remember that the majority of algorithmic trading transactions have an average position holding time of seconds or minutes. Only a few automated strategies define a holding time of a day or more. This is, after all, mostly unnecessary, because most of these strategies are based upon using an information advantage, rather than economic or corporate development. This is exactly the point at which private investors can step in and outmaneuver the technical superiority of the algorithmic trading programs. The investor must focus his trading strategies in such a way that they are independent of short-term information or quote development, but rather are based on mid-term corporate development. As soon as the holding period of an investment is moved to weeks or months, the private investor is no longer confronting algorithmic trading programs head on, but moving freely in market-areas on which they have no influence. The reason for this is that the data analyzed in algorithmic trading says nothing about the mid- or long-term development of a stock position. The current ask/bid positions within the order book of a stock, for example, contains no information about the current valuation of the company or future market opportunities. This doesn't mean that private investors cannot use technical stock analysis data to determine the right timing to open a position too, but the major criteria should nonetheless always be the financial values of the underlying company. The following figures are always worth keeping an eye on: development of corporate revenue as well as earnings and cash flow in comparison with its nearest peers. Other values worthy of note are: the price/earnings ratio, the debt ratio and the revenue/earnings ratio. If investors combine various such corporate values to define investing goals, they will be able to select appropriate candidates from the huge range of stocks on the market; but this is an almost impossible task without some kind of support tool. How then can private investors execute this kind of trading strategy without a huge investment of time and manual effort? Every year, more and more computer-based stock analysis tools are entering the market for precisely this purpose. Many of them offer automated definition and execution of complex trading strategies, and some online brokers now offer their customers strategy-based stock screening tools. Some internet-based market analysis tools provide the user with both predefined trading strategies as well as the opportunity to define individual trading strategies, all based on the rules of technical and fundamental stock analysis. The system then uses these strategy rules to permanently check relevant market data and notifies the user immediately whenever a strategy match occurs. The investor then can decide whether to open, close or hold a corresponding position. When defining their own trading strategies, users need to take care not to share the pitch with algorithmic trading programs; the way to do this is to avoid defining short-term investment goals. But is strategy-based stock analysis not another kind of algorithmic trading? Not at all! Since strategy-based stock analysis doesn't actually trigger any stock transactions, it is more of an improvement on well-known stock screening rather than algorithmic trading. This approach supports and optimizes the decision making process of investors by looking for suitable investment candidates; strategy-based stock analysis supports private investors in their quest to be able to compete with professional trading on an otherwise uneven playing field. Of course, the big players are looking to beat other market participants in this area too, but they cannot achieve advantages as easily due to the fact that these strategies are based on data available to the public.
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