Picking the Right Approach

Ahhough proper money management guidelines are the foundations needed to support any sort of trading strategy, al the core of all trading is the fundamen¬tal reasoning. a model or system used by traders to enter and exit positions. Broadly speaking, FX traders can be divided into the fundame ntalist and the tech¬nical crowd. On the one hand, fundamentalists choose to place their bets based on macroeconomic factors such as interest rates. GDP, inflation, CUf fe nt account imbalances, etc .. and their relation to a currency's intrinsic "value", Much like equity managers who like La buy "undervalued" companies, fu ndamental traders use economic models to forecast theoretical exchange rates a nd trade deviations from these.
The technical crowd, on the other hand, cares less about the underlying economic picture and instead prefers to rely on two things only: time and price. In their think ing, a currency's past behaviour is the best predictor of future exchange rates, so they focus on identifying purely mathematical reasons for entering/exiting trades, such as buying a currency after it moves X % in a one direction or using chart patterns to guide their trading.
Although the fundamentalist approach may seem like the more logical way to go, extensive research into the matter actually indicates that technical trading is a much morc profitable way to trade FX. Although the "value investor" mindsel may pay off in equities, it seems that this line of reasoning is utterly useless in forecasting exchange rates (especially in the short run) because of central bank intervention and other market nuances, and it gets decidedly beaten by using a simple randomizer model. This may explain why economists' forecasts are undeni;:lbly horrible, and to the short-term trader it means that they should focus their attention on the technical
side of trading, if only for the simple reason that it seems to be more profitable. Thai being said, technical trading is no sure road 10 riches either.


Since the advent of trading. the lrad ing community has been obsessed with ways of predicting or forecasting the future through their use of models. As computa¬tional power increased over time, so did the popularity of technical or quantitative trading models and now a wide variety of these are used, ranging from sim¬ple moving-average systems to complex neural network algorithms. Unfortunately most, if not all, models have built-in biases, so an unquestioning belief in them is eXLrcmely dangerous. To prove this point. a famous study was conducted where
thousands of different indicators and technical 100is were tested in an cffort to find the best forecaster of US GDP growth. After an exhaustive search and counlless regressions, one leading indicator was found that seemed to fit the data pertectly: buttermilk production rates in Bangladesh! Yet I still have not figured out how to get that on my Bloomberg.
Most risk models still in use today consider the one-day October 1987 mar¬ket crash to be a one-in-a-billion event, or the statistical equivalenl of getting hit by lightning
In order to understand beuer why all probability-driven models have limitations that will eventually lead them to fail spectacularly. il is useful 10 look at a very simple example. Imagine yourself silting at a stop waiting ror the bus to come. You know the frequency of the bus service (every ten minutes), but not the actual arrival lime. If the buses run according to schedule, then the probability of a bus arriving in the first minute of you getting to

Rest 3ssured that after a sufficient amount of time has passed, there wi ll be no one left at the bus stop no.