WE: I have a question about “reengineering finance,” as you put it. Engineering a computer, a car or a toaster is one thing, but the market — consisting of all its flawed, emotional human beings — how can you engineer that?
RO: It has to begin with a different perception of how things work. If you read physics or chemistrybooks from 150 years ago, they have some strange stories in them. At the time there were very good reasons the stories were formulated that way, but in hindsight they were really just children’s stories. In the natural sciences, this vicious circle ended when people really started to analyze the data. Based on their insights, they suddenly discovered reality is much different from what our theories tell us. A very simple example is (British physicist) Ernest Rutherford, who revolutionized our view of how atoms are built. Previously, people thought atoms were a single object or body. It was only because of his very simple experiment that we learned the atom has a kind of core, but then there is a huge, empty space [around it]. In my view, the same thing is true in finance. If you really look at tick data, certain basic statements we make about financial markets prove to be wrong.
WE: For example?
RO: Let’s start with something simple you’ll find in any student’s basic economics or finance textbook: Demand and supply determines the price — singular, “the price.” But if you look at any financial market, there is a bid and an ask. You might see only one or the other, but there are definitely always two prices. So, the basic textbook definition has a fundamental mistake.
WE: I’m not entirely sure what you’re getting at because, regardless of the bid and ask prices at a given moment, there’s only one transactionprice. A market could be 10 bid and 20 ask, and a trade could occur at 10 or 20 — or 15 for that matter.
RO: From the moment of the transaction, of course, there is only one “real price,” but until that moment there is a range of potential prices. Understanding the market process requires more than just looking at supply and demand. Was the transaction traded at the bid or ask price? Was it a big or small trade? Was it an ‘outlier’ price — that is, how does this transaction price relate to the previous and subsequent prices? How was volatility priced at the time of the transaction? What were the interest rates at that moment in time? By oversimplifying the process and assuming demand and supply are the sole determinants of price, we divert our attention from the complexities of the price-formation process and fail to uncover the multiple forces at work. Natural science progressed because scientists had the patience to first describe natural phenomena without giving in to the temptation to explain it. They had the courage to assemble evidence and differentiate between areas they understood and those that were unanswered riddles. They were good [detectives]. In finance, we presume, too often, to explain. It’s important that we stand back and identify areas we don’t understand. What I’m arguing is, whenever there’s a disconnect with your basic model, take it very seriously. There are many other properties that are very different from what the textbooks suggest. Some of the stories people use to explain things in the market are quite contrived. For example, people in quantitative economics might refer to a time series as “continuous.” I say, if you plot that on a graph it might appear continuous, but financial markets are discontinuous. They make discrete jumps. And they can be big or small.
WE: You’re referring to analyzing things from a tick perspective, correct?
RO: Yes. For me, 20 years ago, it was a very obvious thing: Let’s put together a tick-by-tick database as a foundation and build upon that. Today, that’s still not taken for granted — very few people take this scientific approach.
WE: Are the “discreet jumps” you refer to in price or time – or both?
RO: Prices move in discrete jumps, as does time. Time is event-driven and, thus, is non-continuous. Time can come to a virtual standstill, for example, prior to news releases and then jump ahead in the seconds after the announcement. I think discontinuity is the most important statistical property in finance. It has practical implications on many levels.
WE: For example?
RO: First of all, discontinuity highlights the risks involved in trading. Prices can move far faster and in bigger jumps than intuition suggests. A related idea is that established relationships of the past need not hold in the future. Volatility and correlation regimes that have been valid for months or several years can suddenly break down. Finally, hidden behind the discontinuities are long-term memory processes that impact the future.
WE: What do you mean by “longterm memory processes?”
RO: Long-term processes are “memory effects” of, for example, market participants remembering extraordinary events, such as 9/11. The memory of these events influences current market behavior The discontinuities interrupt these long-term processes, creating a complex pattern of price movement that’s shaped by both extremely long-term memory effects and short-term price shocks. I’m highlighting the dichotomy of the simultaneous impact of extremely long-term and short-term forces in the markets.
WE: What are the insights tick data offers that are not available on other time frames — besides the discontinuity characteristic you mentioned?
RO: Related to discontinuities are non stable statistical properties, by which I mean properties that change depending on the observation interval. An example are the “fat tails” (referring to the greater preponderance of extreme values that occur in a data set vs. what a normal bell curve would imply) or price extremes. The shorter the time interval of observation, the bigger the fat tails or price extremes become. Only by having access to the full spectrum of data — from long-term to tick-by-tick price intervals — can we study the statistical properties at different time resolutions and evaluate their stability.
WE: But don’t you think price behavior is similar — or “fractal,” or however one chooses to define it — across time frames?
RO: Yes, without doubt. The fractal property is a key characteristic of financial market data. And technical analysis has leveraged this property without being aware of it. Technical analysis books explain that their tools can be used for every time frame from intraday to long-term — something that is only possible because the behavior of financial market prices is fractal.
WE: Do you consider what you’re doing a work in progress?
RO: Very much. I have only done a small fraction of what I really intend to do. To me, finance will become a kind of big industry that will be very comparable to the pharmaceutical industry, in which thousands of researchers analyze statistical properties and build complex models to understand certain features. It will be very different 10, 20 or 30 years from now than what it is today.
WE: With that level of sophistication, do you think there will still be a role for smaller individual traders?
RO: There is always a role for them, but they will have to adjust to the fact that their role will shift. Take the analogy of cars. Initially we all got around on foot, then we began to ride horses and today we drive cars. As this new technology evolves, the type of indicators we’ll be using will become increasingly sophisticated. Traders will have to learn about those indicators and explore the different ways to apply them correctly.
WE: Are you essentially talking about quantitative, statistical analysis of price movement, as opposed to what people usually think of as technical indicators?
RO: Yes. For me, though, technical indicators are, in part, an anticipation of the new type of indicators that will evolve. And I don’t see a divide between fundamental indicators and technical indicators they’ll all fold into one big thing.
WE: What’s your definition of an “indicator?” What you’re talking about might be different from what most people associate with that word.
RO: An indicator is a mapping of price or other information — such as expected news announcements— to constitute a signal. For example, overbought or oversold indicators are simple indicators. In traditional technical analysis packages, the overbought and oversold indicators are primitive. The quality of these indicators can be improved by taking into account the 24-hour seasonality of volatility.
WE: Is this a matter of calculating indicators on a tick basis?
RO: Yes. Currency markets — and, by the way, all other markets — exhibit different levels of volatility during the course of a 24-hour day. The currency markets are highly volatile when Europe and the U.S. are open at the same time, whereas volatility is at its low point during the Asian lunch break. When volatility is typically high, it takes a bigger price movement to generate an overbought or oversold signal than in a period that generally is very quiet, such as the Asian lunch break. With the simple overbought and oversold signals in use today, traders get false signals. This could be improved.
WE: Using the overbought-oversold indicator example, what might go into an “improved” calculation?
RO: If you compute the over bought over sold indicator on the basis of two moving averages — long term and short-term — you rescale the moving averages with the 24-hour seasonal volatility. For moving averages with hourly data, you rescale each hour with the seasonal volatility of that respective hour, or with five-minute intervals with the seasonal volatility for each five-minute interval.
WE: Where does OANDA fit into what you’re doing?
RO: Within the world of OANDA I’m interested in building the most efficient currency broker there has ever been in foreign exchange. That means offering the narrowest spreads in the industry and the most seamless transaction process. There also are some other features, such as second-by-second interest payments. If you actually look at how financial markets and typical currency markets operate, you discover the financial system pays daily rates of interest. This contributes to instability. The market would be much more stable if there were continuous interest rate payments. The explanation is very simple: Central banks use interest rates as a kind of lever to manage demand and supply. If a currency comes under pressure they can hike interest rates, and by doing so they can shift the demand and supply. But there’s a catch: This was fine as long as 80 percent of the market consisted of people taking positions for one or several days. But in the modern currency market, 90 percent of the volume is intraday. So when the central banks hike interest rates, they impact only about 5 to 10 percent of the market which means that when they do hike rates, they have to hike them very dramatically to have any impact at all.
Continued………………..
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