TRADING STRATEGIES AND THE SCIENTIFIC METHOD

TRADING STRATEGIES AND THE SCIENTIFIC METHOD
Trading is an unbelievably competitive business. Unlike other industries, there are
no barriers to entry and the capital requirements are very low. These days, anyone
in America can open an online trading account in minutes. Concurrently, given the
competition in the brokerage industry, trading costs such as commissions have
declined. With the market open to so many participants, different styles of trading
and investing have emerged. Speak with 100 traders and it’s likely that you’ll hear
100 different trading philosophies. Momentum, value, trend following, and pairs
trading are a few of the trading methodologies used today. Instead of declaring one
strategy superior to any other, my personal approach as a trader is to test as many
strategies on as much historical data as possible in order to scientifically study the
merits of each methodology. Assessing historical performance means:
1. Following the scientific method by creating a hypothesis (our trading method)
2. Testing the hypothesis (back-test on historical data)
3. Drawing conclusions based on our data (evaluating results and implementing
a trading program)
When we analyze the markets within the context of the scientific method, we become quantitative traders.
The life of a quantitative trader is unique. While the trading process itself is similar from day to day, the results and outcomes are always unknown. Intrigued by the possibility of new trading theories, quantitative traders research ideas every day that have never been explored before. Today may very well be the day a trader discovers a new strategy that puts his or her trading over the top.
So much about trading has changed in such a short time. With the advances in technology and the advent of the home computer, there’s been an increase in the number of quantitative traders using statistical and numerical methods to determine when to buy and when to sell. While these methods are sometimes complex computer programs whose calculations require hours to perform, more often the
strategies are simple rules that can be described on the back of an envelope.
New software has allowed traders to test ideas without having to risk a dime of capital. Before, traders could only speculate if their methods had any historic precedent of profitability. These days, using the new software, strategies can be tested over thousands of markets spanning the globe, giving traders the confidence that their methods have stood the test of time. The entire process can now be accomplished in a couple of minutes.
Of course, before all this wonderful technology became so readily available,
most trading decisions were made by analyzing news and price charts, and being in
touch with gut feelings. Some of these so called “discretionary traders” naturally
possess this gut feel of market direction and can trade profitably without the need
for systematic rules, but it’s rare. It requires getting a handle on one’s emotions and
being able to process information in an unbiased manner, and only a handful of
very talented discretionary traders have achieved this and been successful.
One question that’s long been argued is whether discretionary traders are on
the whole better than their quantitative trading counterparts. The Barclay Group, a
research group dedicated to the field of hedge funds and managed futures, has
maintained performance records of various Commodity Trading Advisers based
on their trading style. CTAs are individuals or firms that advise others about buying
or selling futures and futures options, with some of the largest CTAs managing
over $2 billion. Any CTA whose trading is at least 75 percent discretionary or
judgment-oriented is categorized as a discretionary trader by Barclays, while any
CTA whose trading is at least 95 percent systematic is classified as systematic.
From these two categories, Barclays maintains the Barclays Systematic Traders
Index and the Barclays Discretionary Traders Index. Both indices are compiled
based on the monthly profit and loss of the underlying money managers.
At any rate, concerning our question about discretionary versus quantitative
traders: Between 1996 and the end of 2001, the average annual return on the systematic
(or quantitative) group was 7.12 percent, versus only 0.58 percent for the
discretionary group. What’s more, the systematic index outperformed the discretionary
index in five out of the six years in the test period. These statistics suggest
that we may want to focus our trading on the systematic side.