Complexity Economics

Complexity, Resilience and Systemic Risks in Trading Desks

A trading desk is a physical location where transactions for buying and selling securities occur. Depending on the type of financial institution, the trading desk may be filled by traders trading for their own proprietary account, brokers who act as agents matching buyers and sellers, or some mixture of both.
Trading desks are found in most financial firms that are involved in facilitating trade executions in markets such as equities, fixed income securities, futures, commodities, and currencies.

Traders operating in the financial markets usually converge in a room known as the trading floor or trading room. The trading floor is made up of desks that share a large open space. Each desk, formally called a trading desk, specializes in a security type or market segment. Trading desks are where buying and selling of securities occur within a financial institution.

Before the 1970s, many banks split their capital markets business into many different departments across several regions. These institutions began consolidating these departments in the 1970s following the launch of the NASDAQ, which required all investment firms to have equity trading desks. Today, many asset managers outsource their trading desks to these larger institutions.

Today, thanks to the QCM (Quantitative Complexity Management) technology, it is possible to perform innovative real-time analyses of systemic risks in trading desks and to issue early warnings of potential imminent losses.

In order to do this all that is needed is real time information on Profit and Loss (PnL) of each trader. QCM then assembles a Complexity Map of the trading desk. A small example is shown below. Traders are aligned along the diagonal while the dots connecting the various vertical and horizontal segments represent interdependencies between traders, hence between trades. In order to minimize risk, operators try to avoid interdependencies between traders, pretty much like in a diversified portfolio. The denser the map the higher the complexity, the higher the risk. This is because a dense map means that damage can propagate faster.

Once the complexity map has been synthesized, the complexity of the entire desk is computed. An example of the complexity of a trading desk at a hedge fund is illustrated in the chart below. We know that when complexity suddenly increases this constitutes an alarm, an early warning. In the case in question, the fund suffered a huge loss on a particular day. This day coincides with the peak in complexity. However, already 4-5 days before the peak, complexity was already accelerating. This gives sufficient time to exit certain positions and to avoid losses.

One rapidly rising complexity is detected, Complexity Profiling of the desk provides a breakdown of the overall risk, indicating which traders concentrate the risk. In this simple case it is traders 15, 16 4, 5 and 20. A Complexity Profile of the trading desk on the day of the maximum loss is illustrated below.

Note that the traders who are responsible for the risk – and who have triggered the huge loss – are the ones that are indicated in the Complexity Map with the large boxes. These traders have the largest number of interdependencies with the other traders, hence are those that are coupling the entire desk. When an alarm is triggered, the said traders can be put on standby in order to reduce exposure.

Similar analyses allow not only to measure the complexity, i.e., the systemic risk in a trading desk in real time, but also its instantaneous resilience.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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