Policy makers are typically concerned with long-term economic issues; so why should they be interested in the field of high frequency finance that seems to deal with short-term market phenomena? High frequency finance has the potential of biotechnology and can revolutionize economics and finance by turning accepted assumptions upside down and offering novel solutions to today’s issues.
Why high frequency finance turns economics and finance into a hard science
High frequency finance is a new discipline in economics that was officially inaugurated at a conference held in Zurich in 1995 organized by Olsen. Over 200 researchers from the most renowned universities from around the world came together to start up the new field, which has resulted in 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 and indicates that research is data driven and agnostic. There are no ex ante theories or hypothesis. We let the data speak for itself. In natural sciences this is how research is 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 hypothesis that are then verified with experiments.
In high frequency finance the first step involves collecting and scrubbing of data. As a second step, the data is analyzed and statistical properties are identified. We are on the look out for stylized facts which are significant and not just spurious. Due to the masses of data points available for analysis, for many financial instruments we collect more than 100′000 data points per day; the identification of structures is straight forward, either there is a regularity or there is none. After identifying specific patterns, we formalize our observations and provide tentative explanations and develop theories.
The abundance of data in high frequency finance has profound implications on 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; not a bad thing.
High frequency data as an answer to singularity of macro events
Today, we are all grappling with the economic crisis and have to make hard decisions. In living memory, we have not seen a crisis of a similar scale, so policy makers 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 to compare with and we could use these previous events as a benchmark to evaluate the current crisis. The modern economy with financial markets all linked up through high speed communication networks trading trillions of USD on a daily basis is a new phenomenon that did not exist 20 years ago. People do refer to the events of 1929 and subsequent years: these events can be used as one possible point of reference but they are not meaningful in the statistical sense. There is a void that researchers and policy makers need to acknowledge. On a macro level we can only make observations, but no inferences because we do not have the historical data. On a macro scale the events today are singular; policy makers need to be aware of this.
High frequency finance can fill the void with its huge amounts of data. Inspired by fractal theory that explains, how phenomena are the same at different scales, we 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 crisis 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 after shock 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, why policy makers should take note: 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 over time asset bubbles develop. By tracking price action on a tick-by-tick basis, it is possible to make inferences of 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 that Olsen offers as a free Internet service is a first installment, but just a start of an exciting development.
High frequency finance turns 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 – not a bad thing?
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Why policy makers need to take note of high frequency finance?
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