As the global football betting markets continue their inexorable journey towards maturity, a whole host of new operators are entering the marketplace. From the Forbes listed investors, hot money and private equity firms currently buying up Premiership teams to the investment banks that are in the process of determining how to model football markets, there is a new strata of input to the beautiful game. Each of these power bases seek a psychopathic control of the markets preferably through an absolute structure that enables guaranteed profit by effectively cornering the markets.
Primary level control is absolute and, undoubtedly, one of the key reasons for the entrance of this new pool of interested parties is that ultimate influence on outcomes in liquid markets is a highly effective business strategy. To elucidate, if a Premiership team is controlled by a private equity style operation, the results of their matches may be arranged (through the input of insiders) to suit the trading strategies of such investors. There is very limited external influence that may impact on such a strategy. Minimal risk exists in the initial investment of several hundred million pounds when the equivalent amount of money is globally traded on many of the team's matches. For example, Liverpool's takeover cost Gillet and Hicks approximately £220 million ($440 million) while £350 million global market liquidity was traded on the Champions League Semi Final First Leg between Chelsea and Liverpool. There will be very little influence that the relevant authorities may exert over such corrupted markets.
The prime focus of this post relates to control structures that are not primary in foundation. Examples would include the attitude of investment boutiques to global football markets or the trading strategies of investors towards events that are not under their direct control. It is in these areas that market inefficiency will exist on an informational disassimilation level. Market analysts and traders, through experience, gain a market memory that mimics institutional memory and enables a considerable competitive advantage to be gained through pattern recognition at speed both in relation to the market fundamentals and price dynamics. The new kids on the block are unable to compete directly with such market knowledge and are consequently limited to one of two strategies.
Firstly, they can buy the knowledge in or, secondly, they may internally attempt to model the marketplace utilising computer simulations. The former strategy is distinctly preferable for the bank balances of both the new operations and the skilled analysts but one should not expect that these people will always act in their own enlightened self interest. Prior to an acceptance that external knowledge is the route, the city people always reach for their models.
Computer simulations help to build intuition or to refine calculations but they do not give birth to genuine discovery. There is a fundamental presumption in physics that the way that one understands the world is that one keeps isolating it's ingredients until one understands the stuff that one thinks is truly fundamental (think quantum physics, for example). Then one puts these fundamentals together in complicated forms in order to solve problems. This process enables simplicity to breed complexity. The same structure exists in financial markets which is one of the reasons why Wall Street is full of theoretical physicists!
Chaos underpins all such research and analysis. Chaos is ubiquitous, stable and structured and self-similarity on different scales is the key facet of chaos. These new operations will need to assess how chaotic systems exhibit linkages between major and minor details as nature favours only a few of the available pathways of disorder. All events in a financial marketplace have a range of available outcomes each of which may (or may not) occur. These potential outcomes are known as multiple equilibria and are examples of chaos creating information. Nature creates patterns. Some of these patterns are orderly in space but disorderly in time while others are orderly in time but disorderly in space. Some patterns are fractal while others produce steady or oscillating states.
Investment houses develop models to explain their universe. A model is a mathematical construct which, with the addition of certain trading rules, filtrations and thresholds, describes observed phenomena. The models generated by financial institutions do not attempt to explain and hardly even try to interpret reality. The sole purpose of these models is that they are expected to work. When analysts move into new sectors, this is not achievable as sector-specific knowledge and experience is lacking. The absence of an holistic overview undermines any potential real solution. Many trading operations utilise neural networks (NNs) in an attempt to account for these informational deficiencies. NNs are effectively black boxes that may be trained up through repeated analysis of historical data to produce outputs (solutions) from inputs (data). The key aspect regarding the success or otherwise of NNs is the choice of inputs. Anybody is able to choose inputs that have some relevance to a market structure but very few analysts are able to laterally think such inputs from their own big picture overview. Hence, most usage of NNs is invalid and spurious constructs and solutions abound. When analysis and logic clash, as is frequently the case with automated algorithmic modelling by analysts and researchers in virgin territory, there are several possible reasons for such underperformance. The mathematics may be erroneous or the starting assumptions are mistaken or perhaps intuition may not have been suitably developed. The conclusion to this process is that the analysts are unable to create a truly holistic overview of a new sector due to informational issues and a lack of sector experience.
Dietrological utilise a Unified Trading Model (UTM) for all of our market analysis. This UTM is not sector specific and it may be easily trained to address any financial market. We use NNs for approximately 20% of our trading portfolio but with the significant advantage that we are absolutely certain that our inputs to such networks are totally key. These inputs vary across time as the market structure evolves and mutates and there is no possibility of football market newcomers being able to develop such a fluid construct with a collective market sector memory.
I am entirely isolationist with respect to my UTM. I do undertake consultancy projects where aspects of the UTM are developed and enhanced for a specific area of research but the fundamental holistic reasoning is not for sale. At some point in the future, I intend to publish the UTM in an appropriate academic journal but, currently, it is much more enjoyable to simply trade the UTM to the degree necessary to achieve my rather limited financial goals.
Finally, I believe that it is this complete lack of interest in personal wealth creation that allows the required attitude to be developed in solution of financial markets. I believe in open source not as a loss of privacy or edge but as a competitive advantage. Understanding personal psychology and an effective work/life and project balance are far more influential than any degree of networking or seeking of psychopathic control. Cornered markets are profitable in the immediate term but confer no long term advantage - it is in such short-termism that the seeds of strategic failure are sown.
So, now you know...
