6th January 2013: The Failure Test Entry Working

The Failure Test Entry Working

3:30-8:30pm, Saturday 5th January 2013

Melbourne, Australia

 

Preparation Material: Adam H. GrimesThe Art and Science of Technical Analysis (New York: John Wiley & Sons, 2012); Margery Mayall’s University of Queensland sociological research on technical analysis; BusinessSource database search on academic research into technical analysis, and trader development and learning; and MarketPsych.com behavioural finance and psychological tests.

 

Aims:

 

(i) Identification of trading personal goals for 2013.

(ii) Illustrative understanding of technical analysis as a trading methodology for alpha generation.

(iii) Consideration of learning barriers to trader development.

 

Technical analysis (TA) is the study of group psychology in financial market using price, sentiment, and volume indicators, and pattern recognition. It arose in a modern context due to Charles H. Dow and Richard Schabacker’s study of market patterns in the late 1800s-early 1900s. Robert D. Edwards and John Magee’s Technical Analysis of Stock Trends became the TA bible of market patterns later promulgated in variations by Martin Pring and others. Richard D. Wyckoff (the Wyckoff Method), Robert Prechter (Elliott wave theory), and other TA theoreticians have made influential contributions. TA focuses on identification of trends, retracements, breakouts, pullbacks, support and resistance. It anticipated some aspects of current academic research programs on behavioural finance and market microstructure but from a trader or practitioner viewpoint.

 

Academics and traders remain divided on TA’s efficacy. In 1934, Alfred Cowles contended that a ‘buy and hold’ strategy beat Dow Theory trading. Early studies from 1966 to 1970 by Eugene Fama and his University of Chicago colleagues found that TA filter rules were unprofitable once transaction and execution costs were considered. Fama’s finding led academics to focus on the Efficient Markets Hypothesis, and, ultimately, mutual fund and passive index fund products. In contrast, TA became popular in the mid-late 1970s amongst trend-following Commodity Trading Advisors on volatile commodities and foreign exchange markets. The ‘housewives of Tokyo’ who speculated on currency movements now challenged the ‘gnomes of Zurich’ or institutional investment managers. Victor Sperandeo who traded for George Soros used Dow Theory. The bootlegged PBS documentary ‘Trader’ (1987) shows Paul Tudor Jones II and Peter Borish using Elliot wave theory and 1929 price data to predict a stockmarket crash in early-mid 1988. Finance theories in academic journals and hedge fund manager practices diverged into parallel universes.

 

Recent academic research has shed new light on this academic-practitioner divide. In a review of 95 academic studies on TA from 1960 to 2004, Cheol-Ho Park and Scott H. Irwin found that “56 studies find positive results regarding technical trading strategies” (“What Do We Know About the Profitability of Technical Analysis?, Journal of Economic Studies 21:4 2007, p. 786). They note data snooping problems with Edwards & Magee-style pattern recognition which other academic researchers have also identified. Importantly, Park and Irwin found that TA was profitable in spot foreign exchange and futures contracts “from the late 1970s to the early 1990s” involving “unlevered annual net returns of 2-10%” (Park & Irwin 2007, p. 795). This finding reflects the period when Sperandeo, Jones, Borish, and other non-TA traders like Martin Zweig were ascendant in financial markets. It contradicts the earlier findings of Cowles and Fama that TA has always been unprofitable.

 

Park and Irwin’s finding about TA’s period of profitability is also mirrored in other post-1988 academic studies. These find that the traders used arbitrage on anomalies; the transmission shocks of central bank monetary policies; the anchoring, crowded exits and rational herding of institutional investors; and changes to the international monetary system and political economy. However, these studies often fail to link their finding to the practitioner literature which offers independent confirmation, such as Jones II’s interview in Sebastian Mallaby’s More Money Than God: Hedge Funds and the Making of a New Elite (London: Bloomsbury Publishing, 2010). TA practitioners like Jones II were also often aware of the speculative bubble literature—Charles Mackay, Gustave Le Bon, Charles P. Kindleberger, John Kenneth Galbraith, and Hyman Minsky—which has inspired contemporary research in behavioural finance. This is why Gordon Gekko’s apartment in Wall Street: Money Never Sleeps (2010) had pictures from the Dutch Tulip bubble (1636-37). The conceptual gap between TA and behavioural finance is perhaps not as large for financial market practitioners as some academic researchers believe.

 

The decline in TA profitability after the early 1990s can be attributed to changes in central bank policy coordination, market microstructure, and the growth of algorithmic trading. For instance, the Wyckoff Method identifies institutional trading and market patterns also found in Robert Shiller’s study of ‘irrational exuberance’ and speculative bubbles. But the growth of new trading—options, futures, and high-frequency systems—have altered what the Wyckoff Method found in pre-World War II financial markets.  Collectively, the above developments over the past two decades have changed markets and volatility from trending to more range-bound dynamics. Edwards & Magee’s TA indicators, and support and resistance levels, can now be programmed into algorithms that actively trade against institutional and retail traders who still use traditional TA methods. This Darwinian-like evolution has led to the demise of dotcom era day traders (1995-2000), and trend followers who benefited from asset price valuations due to housing and commodities speculative bubbles (2003-2008).

 

Academic researchers rarely refer to the TA practitioner literature beyond introductory books by Alexander Elder, Van Tharp, and other authors. Academics often state incorrectly that TA remains unstructured as a knowledge domain: Edwards & Magee, the Wyckoff Method, Elliott wave, Fibonacci, Japanese Candlesticks, and other major TA methods and schools each have their exponents and adherents. Instead, TA now involves an industry of books, consultants and custom indicators targeted at the retail investor. University of Queensland sociologist Margery Mayall found that TA indicators shaped the self-beliefs, mindsets, and decisions of the Australian retail traders who she interviewed. Some of Mayall’s retail traders became focused on the never-ending Holy Grail Quest to find the ‘right’ TA indicator or system.

 

In contrast, proprietary trading desks now combine TA with behavioural finance, game theory, and market microstructure. Professional traders seek what Michael Steinhardt called contrarian ‘variant perception’ in financial markets compared with the ‘consensus perception’ of retail traders. There is always someone else on the other side of the trade even if it is a market-making algorithm. Academic researchers could bridge the gap with TA practitioners if the popular models were evaluated and back-tested in a more rigorous manner. However, recent work by Andrew Lo and other authors on rehabilitating TA remains at the interview or memoir stage, rather than using a robust empirical research design. Recent TA practitioner work by Adam H. Grimes, Xin Xie, Charles D. Kirkpatrick II, Julie R. Dahlquist, L.A. Little, David R. Aronson, and others looks promising. Grimes links TA and trader development to George Leonard’s Aikido model of self-mastery; to Daniel Kahneman’s prospect theory and behavioural finance study of cognitive biases; and to Mihaly Csikzentmihalyi’s study of creativity, flow, and optimal experience. This augments earlier work by the late Ari Kiev, Brett N. Steenbarger, and Mark Douglas on trading and performance psychology.

 

Since circa 1992, a subset of TA academic research has also used genetic algorithms and high-frequency tick data analysis to identify trading rules. The findings from this research often either remain proprietary or reflect mathematical and quantitative models. Hedge fund managers who use TA are closer to Aaron C. Brown’s Bayesian risk managers who revise and update their beliefs. Such hedge fund managers are often aware of confirmation bias, the disposition effect, overconfidence, model risk, and other cognitive biases identified in the behavioural finance literature. Hedge fund managers and professional traders now use TA in a mixed methods approach – if they have not already been replaced by algorithmic trading systems. Another problem with the genetic algorithms research is that whilst it identifies trading rules it often does not include trader learning, risk and money management practices. These are what Sperandeo, Jones II, Borish and other TA traders use, and thus these practices modify the efficacy of the trading rules identified. For instance, the PBS ‘Trader’ documentary (1987) shows Jones II using deception and rumour – closer to the Chinese 36 Strategies – to mask his order size and to influence other traders. Academic researchers using genetic algorithms and other methods have often overlooked this cunning or metic intelligence.

 

I resolved in 2013 to integrate TA’s relevant insights into a personal knowledge base and bespoke trading system for alpha generation. Academic research rigour can be combined with professional trading insights whilst retail trading myths promulgated by the TA industry and self-styled trading coaches can be avoided. A mixed methods research approach looks promising: where TA sees trends and retracements – a market microstructure researcher may see the interaction of strategic traders, order flow, and order types – and a behavioural finance proponent may find specific cognitive biases and decision heuristics. All three approaches look at the same market data via different lenses and vantage points. I took several MarketPsych.com tests to identify and to understand personal cognitive biases and psychological preferences. Once identified, I then compared the personal cognitive biases with past trades using an after action review approach. This illustrative research will inform operative action research to improve decision heuristics, mental models, and risk preferences for future alpha generation.

We Are All Traders Now?

Mark Pesce pointed me to Bernard Lunn’s article which contends netizens now live in a real-time Web. Lunn suggests that journalists and traders are two models for information filtering in this environment, and that potential applications include real-time markets for digital goods, supply chain management and location-based service delivery.

Lunn’s analogy to journalists and traders has interested me for over a decade. In the mid-1990s I read the Australian theorist McKenzie Wark muse about CNN and how coverage of real-time events can reflexively affect the journalists who cover them. As the one-time editor for an Internet news site I wrote an undergraduate essay to reflect on its editorial process for decisions. I then looked at the case studies on analytic misperception during crisis diplomacy, intelligence, and policymaker decisions under uncertainty. For the past year, I’ve read and re-read work in behavioural finance, information markets and the sociology of traders: how the financial media outlets create noise which serious traders do not pay attention to (here and here), what traders actually do (here, here, and perhaps here on the novice-to-journeyman transition), and the information strategies of hedge fund mavens such as George Soros, Victor Niederhoffer, David Einhorn, Paul Tudor Jones II and Barton Biggs. This body of research is not so much about financial trading systems, as it is about the individual routines and strategies which journalists and traders have developed to cope with a real-time world. (Of course, technology can trump judgment, such as Wall Street’s current debate about high-frequency trade systems which leaves many traders’ expertise and strategies redundant.)

Lunn raises an interesting analogy: How are journalists and financial traders the potential models for living in a real-time world? He raises some useful knowledge gaps: “. . . we also need to master the ability to deal with a lot of real-time
information in a mode of relaxed concentration. In other words, we need
to study how great traders work.” The sources cited above indicate how some ‘great traders work’, at least in terms of what they explicitly espouse as their routines. To this body of work, we can add research on human factors and decision environments such as critical infrastructure, disaster and emergency management, and high-stress jobs such as air traffic control.

Making the wrong decisions in a crisis or real-time environment can cost lives.

It would be helpful if Lunn and others who use this analogy are informed about what good journalists and financial traders actually do. As it stands Lunn mixes his analogy with inferences and marketing copy that really do not convey the expertise he is trying to model. For instance, the traders above do not generally rely on Bloomberg or Reuters, which as information sources are more relevant to event-based arbitrage or technical analysts. (They might subscribe to Barron’s or the Wall Street Journal, as although the information in these outlets is public knowledge, there is still an attention-decision premia compared to other outlets.) Some traders don’t ‘turn off’ when they leave the trading room (now actually an electronic communication network), which leaves their spouses and families to question why anyone would want to live in a 24-7 real-time world. Investigative journalists do not generally write their scoops on Twitter. ‘Traditional’ journalists invest significant human capital in sources and confidential relationships which also do not show up on Facebook or Twitter. These are ‘tacit’ knowledge and routines which a Web 2.0 platform or another technology solution will not be the silver bullet for, anytime soon.

You might feel that I’m missing Lunn’s point, and that’s fine. In a way, I’m using his article to raise some more general concerns about sell-side analysts who have a  ‘long’ position on Web 2.0. But if you want to truly understand and model expertise such as that of journalists and financial traders, then a few strategies may prove helpful. Step out of the headspace of advocacy and predetermined solutions — particularly if your analogy relies on a knowledge domain or field of expertise which is not your own. Be more like an anthropologist than a Web 2.0 evangelist or consultant: Understand (verstehen) and have empathy for the people and their expertise on its own terms, not what you may want to portray it as. Otherwise, you may miss the routines and practices which you are trying to model. And, rather than commentary informed by experiential insight, you may end up promoting some myths and hype cycles of your own.