I believe high-frequency finance is turning aspects of economics and finance into a hard science. The discipline was officially inaugurated at a conference in Zurich in 1995 that was attended by over 200 of the world’s top researchers. Since then, there have been a large number of publications including a book with the title Introduction to High-frequency Finance. “High-frequency data” is a term used for tick-by-tick price information that is collected from financial markets. The tick data is valuable, because they represent transaction prices at which assets are bought and sold. The price changes are a footprint of the changing balance of buyers and sellers.
The term “high-frequency finance” has a deeper meaning and is a statement of intent indicating that research is data-driven and agnostic. There are no ex ante theories or hypotheses. We let the data speak for itself. In natural sciences this is how research is often conducted. The first step towards discovery is pure observation and coming up with a description of what has been observed – this may sound easy but is not at all the case. Only in a second step, when the facts are clearly established, do natural scientists start formulating hypotheses that are then verified with experiments.
In high-frequency finance:
- The first step involves the collecting and scrubbing of data.
- The second step is to analyse the data and identify its statistical properties.
Here one looks for stylised facts which are significant and not just spurious. Due to the masses of data points available for analysis (for many financial instruments one can collect more than 100,000 data points per day), identification of structures is straightforward, either there is a regularity or there is none.
- The third step is to formalise observations of specific patterns and seek tentative explanations, theories to explain them.
The abundance of data in high-frequency finance has profound implications for the statistical relevance of its results. Unlike in other fields of economics and finance, where there is not sufficient data to back up the inferences, this is not an issue in high-frequency finance. The results are unambiguous and turn economics and finance into a hard science, just as is the case for natural sciences. This is not a bad thing.
High-frequency data as an answer to singularity of macro events
Today we are all grappling with the global financial crisis and have to make hard decisions. In living memory, we have not seen a crisis of a similar scale, so policymakers are in a vacuum and do not have any comparable historical precedents to validate their policy decisions.
If the global economy had been in existence for 100,000 years, this would be a different matter. We would have had many crises of a similar scale, and we could use these previous events as a benchmark to evaluate the current crisis. The modern economy with financial markets linked together through high speed communication networks trading trillions of dollars on a daily basis is a new phenomenon that did not exist even 20 years ago. People refer to the events of 1929 and subsequent years, but while these events can be used as one possible point of reference, they are not meaningful in the statistical sense. On a macro level, we can make observations but no inferences because we do not have the historical data. There is a void that researchers and policymakers need to acknowledge.
Fractals: Understanding macro structure from micro data
High-frequency finance can fill the void with its huge amounts of data – if we embrace fractal theory that explains how phenomena are the same even if they occur at different scales. Fractal theory suggests that we can search for explanations of the big crisis by moving to another time scale, the short term.
At a second-by-second level, there are an abundance of crises and systemic shocks; just imagine the occurrence of the many price jumps due to unexpected news releases and political events or large market orders. Albeit on a short-term time scale, we study how regime shifts occur and how human beings react. The large number of occurrences allows for meaningful analysis. We study all facets of a crisis, how traders behave prior to the crisis, how they react to the first onslaught, how they panic, when the going gets hard and finally, how their frame of reference which previously was a kind of anchor and gave them a degree of security breaks down and how later, when the shock has passed, the excitement dies down, there is the aftershock depression and then eventually how gradual recovery to a new state of normality begins.
The everyday events sum up and shape the tomorrow
High-frequency finance has another big selling point, one which policymakers should take note of: the study of market events on a tick-by-tick basis brings to the surface the detailed flows of buying and selling that occur in the market. From this information, it is possible to build maps of how market participants build up positions and how asset bubbles develop over time. By tracking price action on a tick-by-tick basis, it is possible to infer the composition of those bubbles similar to the work of geologists studying rock formations. Researchers can identify, who has been buying and selling, on what time horizons they trade, how resilient they are to price shocks, what makes them turn their position and become net sellers as buyers. Based on this information we can make inferences of the likely collapse of those bubbles.
High-frequency finance opens the way to develop "economic weather maps". Just as in meteorology, where the large scale models rely on the most detailed information of precipitation, air pressure and wind, the same is true for the economic weather map. We have to start collecting data on a tick-by-tick level and then iteratively build large scale models. Today, the development of such a global economic weather map has barely started. The "scale of market quake" (a free Internet service) is a first instalment, but the start of an exciting development.
High-frequency finance holds out the hope of turning aspects economics and finance into a hard science by the sheer volume of data and its ability to set events into their appropriate context by mapping rare events into a short-term time scale with a near infinity of events, albeit at a shorter-term time scale. Second, the tracking of events on a tick-by-tick basis opens the door to identify underlying flows and develop economic weather maps. Surely that’s not a bad thing?
Bisig T, A Dupuis, V Impagliazzo and Richard Olsen (2009), “The scale of market quakes”, working paper, September.
Gençay, Ramazan, Michel Dacorogna, Ulrich Müller, Richard Olsen and Olivier Pictet (2001), An Introduction to High Frequency Finance, Academic Press.
Mandelbrot, Benoit (1997), Fractals and scaling in finance, Springer.
Mandelbrot, Benoit, Richard Hudson (2004), The (Mis)behavior of Markets, Basic Books.