Technology on Wall Street

Quantitative finance is the application of mathematical tools to price securities. They are most heavily used to price complex derivative such as options and convertible bonds but its uses also extend to simple cash equity and fixed income. Quantitative finance can be split into two main disciplines 1. Pricing complex assets which requires theory on fair value of securities and 2. Back testing trading strategies. Both disciplines make use of statistics but the former however, is used to  theoretically derive values of assets based on a model which assumes that prices are perfectly available to all practitioners and that there is no arbitrage potential. For example an equity option is priced by assuming that there is no benefit to being a buyer than a seller in an option. If that benefit exists the trader will simply take the more profitable position, which will anyway be arbitraged away in a perfect market.

Back-testing is the method of creating trading rules and testing if the methods work using historical data. For example an individual investor might believe that if a stock has a P/E less than 10X and has a return on assets more than 15% it should beat the index until the P/E reaches the same average level as the benchmark index. Back-testing validates the hypothesis. There are much more sophisticated back testing strategies which incorporate high level technical analysis of moving averages and high/low closing prices. The real skill in back testing is being able to use the correct statistical tests to determine if the outcome is expected based on a relationship between various financial variables or is it simply a random occurrence that the strategy returned a profit.   

Early Quants

Benjamin Graham purports that the ideal method of investing is to analyze whether a company is cheap relative to what its assets and earnings are worth. His methodology is known as ‘value investing’. Value investors look at financial ratios to assess relative value. The most common ratios are price to earnings ratio (P/E) and price to book ratio (P/B). The price earnings ratio is simply the price per share divided by the net income per share of a company. Similarly the price to book ratio is the price per share divided by the net equity (assets – liabilities). If a company has a price to book ratio of less than 1 times an investor can buy the company’s assets for less than what it’s actually worth.

Before the advent of financial software, investors had to read through many pages of financial news papers to pick stocks which had low P/E and P/B ratios. This was a very tedious task. During the past 30 years, financial data software such as Bloomberg have been developed which enable investors to punch in any financial criteria they wish to receive an output of stocks. This can also be done on financial websites such as Yahoo! Finance and Fidelity.com. Computers have enabled the dissemination of financial information and have made the stock market more efficient as a result of it.

 Algorithms and Electronic Trading.

Besides the ability to disseminate important information quickly, computers also allow for the automation of processes, which save a lot of time and money. Electronic trading is the process where instead of calling a broker to buy or sell a stock one sends in orders electronically through the computer. Very often the computer will send the message to the broker on the exchange. Once the transaction happens a message is sent back to the trader automatically with the confirmation. Electronic trading is cheap and very time efficient. Furthermore electronic trading can be incorporated with high level algorithms which execute orders automatically with little or no need of a human being.

If a back testing strategy has successfully been confirmed as valid, a statistical arbitrage team at a hedge fund might want to put the strategy to work. The trading engine will have a set of conditions which will send in buy and sell orders via the direct market access service. This method also allows for sophisticated pre and post trade analysis to be made which allow for further optimization techniques to lower costs.

Many brokerage firms are using quantitative analysis to optimize the price they can achieve on complicated basket trades. Methods are used that scan through bid ask spreads and the volatility, with the aim to beat the volume weighted average price of a stock trade.

According to many consultancy companies 1/3 of equity trading executed in the US were driven by automatic programs. Theybelieve that this figure will reach 50% by 2010. Quants have beaten non quants in terms of returns between 2001 and 2005. According to a consultancy firm, large cap US equity funds have produced median returns of 5.6% compared to non quants which produced a 4.5% return during the same period.   

Future of Quantitative Trading

Amongst the advances of quantitative programming and trading are methods used to back-test trading rules and strategies. As described above these tests require high level statistical analysis to confirm that the strategies are true patters and not just random occurrences. These tests are done by using a computer that go through billions of trades to spot subtle patters that can be arbitraged away. These tests can involve fundamental as well technical analysis. Some are even attempting to strip of human emotions like fear and greed.  

Amongst the more innovative quants, attempts are being made at creating a computer that can anticipate the affect of a sudden death of an executive on a stock price. Columbia University is in the process of researching a method to build an electronic investing guru that can tell the effects or benefits of events like two companies merging with each other, or the effects of sending more troops to Iraq.     

An area of research called “Natural Language Processing” is involved with creating a program that understands human language and can read through a research report with the ability to transfer information gained into making investment decisions. If this program works it can have the benefit of sifting through millions of websites to pick information. Once the information has been gathered it will then be able to make trading or investment decisions automatically.

The skeptics of artificial intelligence in trading argue that there is a limit to how much a computer can achieve to replicate certain human understandings. The skepticism is related to a movie that was released in 1968 by the name of “2001: A Space Odyssey” which was about a computer fitted in a spaceship that was able to think like a human being. In 2007, a machine of this type still is far from being built. Many of the optimists of artificial intelligence are inspired by the ability to build a machine that is able to beat world champions at chess. Many claim that equally a computer can be build that can beat the stock market index. However what many don’t realize is that in computer and mathematics language a chess computer is a closed system where a limited number of possible moves exists. The stock market is an open system where any event can perturb the normal movements of the markets.  Furthermore a computer  will have a very hard time to be able to use common sense and educated guesses as well as personal expressions and sarcasms which news commentators and analysts project when giving out information.

Skepticism of quantitative analysis is also related to the fall of a hedge fund by the name of Long Term Capital Management which was developed by the most sophisticated mathematicians and scientists (one of whom was a Noble Prize winner). This fund eventually went bankrupt and even went as far as creating a minor financial crisis. Truth is that whatever the successes and failures, quantitative analysis has been proven to be useful and beat many non quant methods. Whether one will be able to build a computer that can forecast stock prices, the innovation of technology in the financial markets is by many means improving its efficiency.