The Long Gamma Working
7th September 2013
On 11th October 2011, I “resolved to develop a private, low-key, personal vehicle for long-term self-sufficiency” in the Nihonbashi Working, held at the Tokyo Stock Exchange in Japan.
I had started to trade in Australian financial markets on 8th October 2011. Standard & Poors had downgraded US debt from its AAA rating on 5th October 2011. Over the next several months the European Union’s bond markets also faced a debt crisis which led to austerity policies and intermarket volatility. Simultaneously, my university employer went through a year-long organisational restructure with several rounds of redundancies. I spent this time gathering resources; doing due diligence on early, failed trades; and researching financial markets using the ThomsonReuters Datastream information system.
On 30th July 2013 as part of the Sirius XI Working in the Esoteric Order of Beelzebub, I isolated two specific strategies that provided illustrative LBM insights. I was familiar with Charles Mackay’s influential reportage on the behavioural and sociological dynamics of speculative bubbles (Extraordinary Popular Delusions and the Madness of Crowds). In Arnold Van Gennep’s framework (The Rites of Passage), this can involve creating the conditions for rational herding (indirect, contagious, and negative taboo) that leads to crowded trades (identified by Richard D. Wyckoff and others). Alternatively, this can involve active management (animistic, direct, and sympathetic). I found evidence of the first strategy in the Drexel Burnham Lambert, Galleon, and SAC insider trading cases; and for the second strategy in Bill Ackman and David Einhorn’s hedge fund activism.
Long Gamma is a long-term illustrative GBM Working to create a preferred financial future, which builds on these insights, past Workings, and on-going practice-based research. The Working title refers to the key strategy that Nassim Nicholas Taleb articulates in his book Antifragile: Things That Gain From Disorder (New York: Penguin, 2012) (TS-5), from his experience with the Greeks (key sensitivities) as an options trader.
1. Utilisation of current research on expertise, skills building, and performance psychology.
William Chase, Anders Ericsson, Michael Howe and others have popularised strategies for expertise and skills building using cognitive engineering, deliberate practice, and fluid intelligences (from the Cattell-Horn-Carroll model of human intelligence). Chase, Ericsson and Howe’s research provides a more rigorous framework for cognitive modelling than the Neuro-linguistic Programming approach of Richard Bandler, John Grinder, and Robert Dilts. Brett N. Steenbarger, Ari Kiev, Mark Douglas and Doug Hirschorn have adapted these insights to traders’ use of performance psychology. My illustrative GBM immersion in this material builds on my Fluid Intelligence Working (30th June and 1st July 2012).
2. Development of a Long Gamma personal trading capability.
Long Gamma involves time (theta), volatility (vega), and nonlinear rates of change (gamma). Hedge fund managers use the two strategies mentioned above as an operative LBM strategy to create Long Gamma dynamics that they can profit from at others’ expense. This part of the Working builds on the Oligarchy Working of 30th January 2013. The development of a personal trading capability involves several stages:
Stage 1: 2013 – 2015
This period coincides with on-going PhD research which may involve a possible chapter on the Department of Justice and Securities & Exchange Commission’s criminal and civil cases against Steve A. Cohen and SAC. I will use this period to codify the two strategies mentioned above, and to develop case based reasoning from Wall Street biographies, history, and contemporary journalistic reportage, in order to make causal, probabilistic inferences from. I will start to develop a trading playbook of insights during this period which can be used for the later development of trading strategies.
Stage 2: 2015 – 2017
Using the case based reasoning as a starting point, more in-depth analysis of specific filter and trading rules using a Bayesian belief network to model financial markets as a complex adaptive system. Possible areas of analysis might include: (1) the impact of high-frequency trading; (2) the contributions of behavioural finance and market microstructure literature; and (3) predatory trading approaches that target the retail and institutional trader use of technical analysis. The result will be Bayesian decision rules that can be tested in live trading.
Stage 3: 2018 – 2020
Codify the Bayesian decision rules into cognitive task protocols for discretionary trade and portfolio management processes. Two more long-term options that will be explored for possible quantitative trading include: (1) the use of Markov Chain Monte Carlo simulation to test market data; and (2) the possible development of proprietary trading strategy algorithms. This may be done using a commercial platform like MetaTrader or TradeStation for retail traders; the possible emergence or the reverse engineering of a retail trader accessible option for alpha discovery comparable to Deltix’s QuantOffice for institutional traders; or using the Matlab programming language as an interface to Interactive Brokers or a similar trade execution platform. This will require an understanding of agile software development; machine learning; quantitative strategies; and test driven development.