High frequency finance focuses on the messiness of the tick by tick data produced by financial markets. The discipline is not hamstrung by theories that have been formulated in the abstract, as is the case for classical economics. The approach is to first study the detailed statistical properties of the data and in an iterative process build models that explain the observations. The discipline follows the approach that natural scientists take for granted: to first observe the phenomena of nature and then, inspired by these observations, to build appropriate theories that explain the recorded facts.

Today, in the age of electronic trading there are masses of data. Liquid financial markets spew out market prices every second and faster. So for one instrument alone it is possible to collect from 50′000 to 100′000 and more price ticks every day. These masses of data points are meaningful, because market makers set prices by monitoring price updates from competing market makers on a tick by tick basis and benchmark their prices relative to the overall market. They set prices in a way to balance demand and supply. Even the slightest deviations may mean huge losses. On an ongoing basis market makers fine tune their pricing to ensure that the prices published are at exactly the right levels.

In high frequency finance investigating these masses of prices, we have discovered 17 new scaling laws that hold over several orders of magnitude, see scientific article with the title ‘An extensive set of scaling laws and the FX coastline’ by J. B. Glattfelder et al.. Scaling laws are observed, when two properties maintain the same proportions over a range of values. In our case, the scaling laws persist from ultra-small values of 0.01 percent price changes to magnitudes of 5 percent price changes and more. An example of such a scaling law is the size of average price overshoot that occurs, whenever a new price trend has started. Say you observe that a market price has moved down by 1 percent from its recent peak, how much further will the price move down? Will the price revert after an additional average price move of 0.1 percent, 0.5, 1 or more percent? as it turns out, the average overshoot is 1 percent: this property scales and holds true for thresholds of 0.01 percent up to 5 percent and possibly higher. So, if the price has moved down from its recent high by 2 percent, then on average there will follow another 2 percent move.

The remarkable feature of this and the other scaling laws described in the above paper is that they apply to both intra-day market moves and longer-term price movements spanning days, weeks and months. There is no difference in behavior for intra-day and longer-term data. This result is remarkable: it indicates that we can use the abundance of intra-day data to develop robust models and then inspired by fractal theory use these models appropriately scaled to explain long-term data.

The world is evolving at a rapid pace: just think of the changes that have occurred over the past ten years. Today, we take the Internet,  mobile phones, etc. for granted – only 10 years ago, these technologies were in their infancy. Economists cannot hope to develop robust economic models that are fed with long-term data alone. They would need access to a 100′000 year history of our modern economy to build models with long-term data alone, but this does not exist. By building fractal models that are primed using the abundance of intra-day data and then scale them to be applicable for long-term phenomena, we overcome the fact that we do not have sufficient long-term data. For traditional economists this is a big challenge and will require an extra effort. They will first have to focus on developing models for liquid markets and then expand their scope. At Olsen, we have started this process and have succeeded in building models that are the same for short-term and long-term time horizons, the only difference is their respective time scale.

The discipline of high frequency finance is still in its infancy. If we compare its prowess with the success story of computer sciences, then in terms Moore’s law, which was published 1965, high frequency finance as the technology stands today is comparable to 1968 computer technology, so a long way to go. How will a break through in economics help us? Today, we do not access to efficient predictive services for our economy. We can use the technology to build a global information system for the economy and its financial market. Olsen’s Scale of Market Quake service, which is the equivalent of a Richter scale for the foreign exchange markets, is a step in this direction.

Author: Richard B. Olsen, Founder and CEO of Olsen Ltd