Richard Olsen: Tuned to high-frequency Part 1

Richard Olsen wants to turn the markets into a car, or a computer. Or maybe a toaster.

That might sound like a non sequitur, but Olsen, chairman and CEO of the Zurich-based Olsen Group, as built a career out of looking at things a little differently than the people around him.

“What drives me — what fascinates me — is trying to make finance a true engineering science,” the Oxford-educated economist says. “What that means, in a way, is to make finance as sophisticated as your best gadget, such as your computer or even an elegant car — to make it as slick as some of the physical technology we encounter in our everyday lives.”

If it seems like a tall order, the 51-year-old Olsen is giving it the old college try. His specialty is analyzing “high-frequency” (i.e.,trade-by-trade, or “tick”) data in the forex market. He founded his first financial research company in this vein in 1985 and today has his hand in several financial research, trading and market-service companies related to the fruits of that labor.

In addition to Olsen Financial Technologies, which makes software tools for gathering, cleaning and analyzing high-frequency data, Olsen in 1996 founded (with professor Michael Stumm) OANDA (www.oanda.com), an online forex brokerage/market maker.

Olsen Invest (www.olseninvest.com) is a money management firm, that Olsen founded in November 2002. The company has six “reference” U.S. dollar accounts (each initially funded with $10,000) with varying risk profiles. It shows the balance of one of the accounts plus unrealized profit/loss, minus a 35-percent performance fee.

Olsen studied law before he got involved in the markets, receiving a law degree from the University of Zurich in 1979 before earning a masters in economics from Oxford University in 1980 and a Ph.D. in law from University of Zurich in 1981. “I studied law because my family thought economics wasn’t a real subject,” he says, laughing. “I didn’t realize for a while that law wasn’t a real subject, either.”

From 1983 to 1985 Olsen worked in research and FX dealing at a Zurich bank, but he quickly saw an opportunity to strike out on his own.

He started his own company (“little realizing what I was doing,” he says in retrospect) in 1985 with the goal of selling cleansed databases of FX tick data and forecasting services to banks.

“The underlying vision was to take the approach that has proved to be very successful in natural science and apply it to finance,” he says.

That scientific approach was based on the systematic analysis of forex tick databases, which Olsen says contain overlooked clues about price that often challenge popular notions of market behavior.

An Introduction to High-Frequency Finance, the book Olsen co-authored in 2001, outlines many of the principles that guide his work. The following discussion summarizes a few of its major concepts.

One of the big advantages of tick data is how much of it there is. Whenever you use statistical analysis, the more data you have to work with, the more confidence you can have in your results. Because tick data provides so many “observations” (An Introduction to High-Frequency Finance points out that a liquid market can generate as many data points in a single day as daily data can in 100 years), it provides more statistically reliable insights into price action.

Also, there is a consistency to tick data over select periods, even though a certain period might contain a multitude of data points. For example, it could be argued that comparing the day-to-day or week-to week price action of the Dow Jones Industrial Average today to its behavior 100 years go is inherently flawed because both the index and the marketplace have changed so dramatically over the past century. However, the number of “observations” in a tick database is huge over a relatively short time window — say, a month. The fundamental characteristics of the marketplace are much more stable during this period than they are for daily or weekly data spanning a century. This decreases the risk of making apples-to-oranges comparisons. Further, high-frequency finance holds that calculations such as volatility can be made more robust through the use of tick data. For example, shows the disparities between annualized volatility figures for the U.S. dollar-Japanese yen rate (USD/JPY) using the afternoon price during Japanese trading hours, the afternoon price during London trading hours and the 24 hour continuous tick data.

One contention of the majority of longer-term traders is the amount of meaningless market “noise” increases as the time frame shortens — tick data being the shortest time frame possible. However, the high frequency argument is that “low-frequency” data — daily, weekly, etc. actually hide important market dynamics observable only in high-frequency tick data, including the interaction of different market players operating on different time frames and in different geographic locations.

The book’s key findings regarding these aspects of tick data fall into three categories — scaling, seasonality and heterogeneity.

Scalability refers to the “fractal” properties of price action. For example, the book’s research showed the volatility patterns exhibited in forex prices at the 10-minute time frame are similar to those at the hourly and daily time frames. (This should sound familiar to any chartist who trades the same type of pattern on different time frames.) This concept of scaling also applies to how frequently price changes direction on different time frames.

The seasonal factor refers to regular patterns in, among other things, volatility, bid-ask spreads and tick frequency that emerge over a 24-hour trading day or over a week. These ten dencies are driven by any things, some as simple as the overlap of the end of the European FX trading session and the beginning of the U.S. session that typically produces the highest level of trading activity each day. Other examples include volatility and bid-ask patterns, such as the tendency for intraday forex volatility to peak between hours 12 and 18 (GMT) of the day and for spreads to increase on Fridays.

Market heterogeneity addresses the presence of different trading goals and styles among the differentplayers in the FX market. For example, market makers operate on the extreme shortterm, while central banks trade infrequently. Olsen contends this diversity causes news to be absorbed over time by the market, rather than all at once, as is popularly believed. In other words, an initial market reaction to an event is followed by a series of secondary reactions, which include market players reacting to each other’s reactions. You can’t predict the news, according to Olsen, but you can model the reactive process.

It is, as they say, a lot to “get your brain around.” In early September, we explored these ideas with Olsen and discussed their relevance for FX traders.
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