Speculative Bias: Young, Male, Poor, Overconfident

Since 2012, whilst holidaying overseas, I visit the finance / investment section of bookstores as a barometer on their business climate. This year in Toronto, Canada, I visited Indigo Books in Fairview Mall. It had a number of books on technical analysis: reading price / volume charts to make investment and trading decisions.


TA was popular from the mid-1970s until the 1987 stockmarket crash and regained popularity during the 1995-2000 dotcom crash. Since about 2003 it’s been a dead methodology — at least in its vanilla, popular treatments — due to high-frequency trading. TA books however continue to sell to uninformed retail investors.


Arvid O.I. Hoffmann and Hersh Shefrin’s new study of 5,500 trader accounts at a Dutch discount brokerage between 2000 and 2006 has some sobering insights on TA and retail traders:


  • The study period coincides with the 2000-01 dotcom crash and the mark-up period of the 2003-07 speculative bubble in real estate.
  • TA appeals to young, male, poor, overconfident traders who want to speculate or who treat trading as a hobby.
  • TA traders had more concentrated portfolios than those who used fundamental analysis or professional advice.
  • TA traders had higher turnover; personal ambition; a short-term timeframe; and often did not consider transaction costs or taxation implications.
  • The average portfolio size in this study was $60,589 and the median age of traders was 49.79 years of age.
  • The 95th percentile included traders aged 70; who turned over their portfolios 98.19% per month; who did 43.06 trades per year (mean of 10.66 trades per year); who had 72 months experience or 6 years experience (mean of 40.21 months experience or 3.35 years); and whose portfolio was valued at EUR166,840 compared with a median of EUR15,234 and a mean of EUR45,915.


Hoffmann and Shefrin’s study suggests several things to me:


  • There are at least two identifiable sub-populations: (1) young traders who try to compound their risk capital to get rich; and (2) older investors using savings and retirement money.
  • Most traders last less than 3 years. Many over-trade or blow-up their accounts within 10-to-15 trades – in part due to very small trading accounts.
  • TA appeals to new retail traders who are really trading on rumours that can be traced back to Martin Zweig (“The trend is your friend”), Jesse Livermore, and the Edwards / Magee school of TA.
  • Interest in trading occurs at distinctive life stages: early twenties (get rich); late forties (save for retirement); and post-retirement (create a financial buffer for future spending).
  • Some trader success is due to the hot hand effect of winning streaks – which may in a social network influence a new cohort of traders – for what was more luck than skill.
  • TA traders attempt to mix indicators / signals and psychology (state management). Yet the real gap for retail traders is an understanding of transaction / execution costs.


Hoffmann and Shefrin’s study suggests several things to me about myself:


  • I’m in what Paul Fussell calls the High-Proletarian level of the middle class: university educated; but without the financial security of Fussell’s Upper Middle class.
  • I had encounters with financial markets from my early teens to my early twenties, but was not an investor in early life due in part to the adverse experiences of recessions and stockmarket crashes.
  • My serious interest in financial markets emerged after formative experiences around the 1995-2000 dotcom crash; the 1998 collapse of Long-Term Capital Management; and an encounter with Sir James Goldsmith’s life philosophy in 1995, re-explored in 2010.
  • I began research in 2009 and first traded on 5th August 2011 – days after a ratings agency downgrade in United States sovereign debt and into a Eurozone financial crisis.
  • I started with an account size in the 25th-30th percentile of the study – about $A5,600 to trade. I soon ran into psychological barriers about getting out of trades in a deteriorating market situation where I had hoped a market retracement might occur. I continued to hold positions despite passing my stop-loss limit.
  • This loss aversion led me later to more closely study the research on behavioural finance. I found that my initial trading hypothesis was correct — but the reason why was that it was also being ‘gamed by convertible arbitrageurs, prop desk traders, and high-frequency trading firms. I lost several thousand dollars before I exited the trade. In October 2011, whilst in Tokyo, Japan, I put the pieces together involving a series of trades by the Mitsubishi UFJ Bank which was warehousing trades for foreign hedge funds. This involved a Gurdjieffian shock – I knew what to do but emotionally I was unable to Act at the appropriate time to exit the trades. I sat in the Starbucks above the Shibuya Crossing and considered the implications.
  • This initial experience in live trading led me to pull back and examine what I knew about financial markets; what algorithmic and high-frequency trading was; why retail traders fail; and how professional traders work.
  • From 2011 to 2013, I bought most of the core literature on finance, wealth management, funds management, trading, behavioural finance, and market psychology to fill in some major knowledge gaps. This led to what will possibly be a post-PhD strand in my research program on the sociology of finance, and hedge funds / private equity funds as strategic subcultures.
  • From 2011 to 2014, I made a series of personal oath-promises to myself about personal self-sufficiency (Nihonbashi), long-run gains (Long Gamma), and shifting from a naive retail trader to understanding the institutional mindset (Toronto-Dominion).
  • Rather than trade I dealt with saving for retirement via employer defined contribution plans, employer co-payments, and legal tax minimisation strategies.
  • Rather than TA signals I began to study market microstructure (the study of price dynamics in order book flows) and money market flows between funds. Recently, I have downloaded several Springer books on high-frequency econometrics from a university database.
  • I found the major lesson about trading was about the psychology of decision-making and money management. These are skills I Needed to learn yet lacked.


In conclusion I fit one of Hoffmann and Shefrin’s sub-populations and past trading strategies. Reading their study is an important ‘reality check’ that helps me to identify what I can change to build a more resilient financial future. At least, I didn’t lose a million dollars.

Birinyi / Wyckoff

Wyckoff Market Cycle (Source: StockCharts.com)
Wyckoff Market Cycle (Source: StockCharts.com)

For several months I have been playing around with Richard D. Wyckoff‘s market cycle. Wyckoff influenced contemporary practitioners of technical analysis including Adam H. Grimes and David H. Weis.


One of Wyckoff’s major contributions is his Market Cycle: an algorithm of the interrelationship between price changes, market phases, and institutional money flows. In the Accumulation phase activist hedge fund managers, value investors and proprietary trading desks accumulate a position in a stock. In the Markup phase trend-followers emerge, hedge funds trade on catalysts or rapidly moving stocks, and speculative bubbles begin to form. The Distribution phase is where the remaining institutional trading desks sell to retail investors, and rational herding in range-bound markets occur. The Markdown phase involves crashes, panics, short-selling, and distressed debt.


Wyckoff’s Market Cycle was an attempt prior to market microstructure theories to explain phase shifts in financial market dynamics.


This week I read the first couple of chapters from Laszlo Birinyi‘s book The Master Trader: Birinyi’s Secrets to Understanding the Market (Hoboken, NJ: John Wiley & Sons, 2013). Birinyi’s first three chapters use event and observation studies to debunk a naive use of Edwards & Magee-style indicators for market sentiment. In the fifth chapter Birinyi introduces his Money Flow analysis on block trades, and flows in and out of a stock. For Birinyi, the Money Flow indicates market circumstances where there will likely be high-probability shifts in stocks. He also acknowledges that dark pools, high frequency trading, and other recent market innovations now affect the reliability and construct validity of Money Flow analysis as a predictive tool.


In that moment I made an abductive inference: what if traders combined Birinyi and Wyckoff? Birinyi’s Money Flow analysis shows that money flows into stocks from hedge funds and proprietary trading desks during the Accumulation and the early Markup phase; and to trend-followers and retail investors during the Markup phase. Money flows between these different traders during the Distribution phase. Money flows out from the majority of investors during the Markdown phase to short-sellers and distressed debt / value investors.


There are a couple of ways to build a combined Birinyi-Wyckoff trading system:


  • Write out the Birinyi and Wyckoff models as a series of If-Then-Else-ElseIf nested loops or develop an expert system.
  • Use Case Based Reasoning on historical examples such as Markup manias and Markdown phase panics and crashes.
  • Do market microstructure analysis of the order book, volume, and order flow.
  • Use complex event processing and stream processing to develop a real-time system using market data, Bayesian belief nets, and machine learning.


These options for capability development are part of what a post-PhD project on the sociology of finance might explore.

Trinitarian Trade Rules

Post-ISA 2014, I am delving into formal models and the scientific method. I’m reading Patrick Thaddeus Jackson on scientific models of international relations; writing declarative statements in SWI Prolog; and considering the potential microfoundations of my PhD topic on strategic culture. This is all new territory for theory-testing.


This evening I looked at the first chapter of David Aronson’s book Evidence-Based Technical Analysis (Hoboken, NJ: John Wiley & Sons, 2006). Technical analysis (TA) usually involves pattern recognition (Edwards & Magee); geometric angles and waves (Gann and the Elliott Wave); or institutional money flows (Wyckoff). Aronson suggests the majority of TA approaches involve superstitious, magical thinking. In contrast Karl Popper’s falsifiability provides a way to develop what Jackson would describe as neopositivist TA models.


Aronson’s book tests 6402 trading rules (some significance tests are here). He uses a binary structure to codify each of the trading rules: (1) +1 is a long recommendation; and (2) -1 is a short recommendation (pp. 16-17). For Aronson, “An investment strategy based on a binary long/short rule is always in either a long or short position in the market being traded” (p. 17). This binary structure enables Aronson to combine Boolean logic and Popperian falsifiability in order to test each of the 6402 trading rules. Thresholds (pp. 17-18) mean Aronson can transform the binary trading rules to create If-Then nested loops of declarative rule conditions.


Aronson’s binary structure assumes that traders are trading in the market at all times – just switching between long and short positions. However, prop traders and high-frequency traders may close-out positions – such as at end-of-day to avoid overnight exposure and gap risk. Some TA proponents like Richard D. Wyckoff note that close-out positions can also have strategic uses: first accumulating a position and then profit-taking via selling to trend-followers.


My initial solution was to change Aronson’s binary structure into a trinitarian trade rule. The additional rule / outcome:  (3) 0 means the trader is out of the market. This necessitates a sell order closes out any current market positions. This could be done either as a declarative rule condition or as a nested If-Then-Else loop.


One benefit of the scientific method is more rigorous exploration of how such formal models work.

18th June 2013: Algorithmic Trading Goes Retail

Fortune Magazine reports that EquaMetrics is now selling a cloud-based app that creates Technical Analysis-based algorithmic trading strategies for retail trading subscribers:


EquaMetrics’ app is simply designed and since its software firepower comes from the cloud, it doesn’t require anything more than the typical PC. You can drag and drop colored tiles to assemble your own algorithm. Day traders can choose between 30 variables to build their formulas. The options are built on so-called technical indicators, metrics that reflect trading patterns as opposed to stock fundamentals such as the price-earnings ratio. After you’re done, you run the program to buy and sell stocks and currencies.


The web application is relatively inexpensive: it costs $99 a month or $250 a month, depending on how many algorithms you want to run. That’s a steal compared to the alternative of hiring a quantitative programmer for $200,000 a year. EquaMetrics gives you the stuff a programmer could produce. Then it’s up to you to assemble your own strategy.


I have been expecting apps like this for several months, and have been monitoring other initiatives like the Quantopian community. The popular literature on algorithmic trading strategies evolved from Technical Analysis mechanical systems (Tushar S. Chande’s Beyond Technical Analysis) to back-testing (Robert Pardo’s The Evaluation and Optimization of Trading Strategies) and then to algo trading using Matlab software (Ernie Chan’s Quantitative Trading and his new Algorithmic Trading; and Barry Johnson’s Algorithmic Trading & DMA). This period spans the post-dotcom collapse; the 2003-08 speculative bubble in real estate and asset-backed securitisation; and institutional experimentation with high-frequency trading platforms, and transaction and execution costs.


EquaMetrics’ strategy reflects this decade-long evolution:

  • Its initial offering is Technical Analysis strategies: at a time when: (a) high-frequency trading has ‘broken’ many trend-following and momentum indicators; and (b) hedge funds and proprietary trading desks use predatory trading to clean out TA-oriented retail traders.
  • The model is subscription-based software as a service — which could eventually disrupt or change the economics of agile software programming if this offering scales up in a significant way. Will the $US99-250 per month price point remain? Or will another platform develop a lower-priced offering and trigger a ‘race to the bottom’ competitive dynamic?
  • It opens the way for the licensing of specific TA indicators and proprietary methods as ancillary revenue streams, and as a way to build a market around the core product offering (which NinjaTrader, MetaStation, and ESignal have all done with their respective platforms).
  • The quality and scope of the back-tested data is important: quantitative hedge funds like Jim Simons’ Renaissance and David Shaw’s D.E. Shaw & Co each clean their own data.
  • EquaMetrics’ move into fundamental indicators reflects some recently published work on the quantitative analysis of these strategies (notably, Richard Tortoriello’s Quantitative Strategies for Achieving Alpha, and Wesley Gray and Tobias Carlisle’s Quantitative Value).
  • EquaMetrics’ choice of FXCM and Interactive Brokers as prime brokers to process client trades is significant: brokerage transaction and execution costs can mean a potential, new trading strategy is actually unprofitable to execute, or that its profit-taking ability declines over time, especially in correlated and ‘crowded trade’ markets.
  • The focus on TA and fundamental indicators does not address some of the quantitative, statistical or machine learning strategies that quantitative hedge funds use to develop algorithms; how correlation testing of model variables might occur; and what might happen to retail investors once several different competing firms have back-tested and issued dueling algorithms (a factor in high-frequency markets where scalping and order front-running occurs).


Still, the EquaMetrics offering has me interested: I’ve been waiting for algorithmic trading to ‘value migrate’ (Adrian Slywotzky) to retail traders, for awhile. It’s a first step towards post-human trading (Charles Stross’s novel Accelerando).

16th June 2013: My First Trade

My First Trade
My First Trade (click to enlarge)


Foreign Policy‘s Dan Drezner asks: “Hey, remember when Standard & Poor’s downgraded U.S. sovereign debt back in 2011?”


I sure do.


S&P downgraded US debt on 5th August 2011. I placed my first trade on 8th August 2011: 1041 ASX:LYC @$1.92 ($2003.31 including $15 brokerage fee).


(ASX:LYC closed Friday +4.44% @$0.47. I caught the tail end of the 2008-10 speculative bubble in rare earths. Lynas Corporation has since faced project delays in Malaysia; activist lawsuits; headline risk; and regular ‘shorting’ due to convertible bond arbitrageurs and exchange traded funds. I entered the market on a distribution phase — expecting a further rise — and instead faced a markdown, in terms of Richard D. Wyckoff‘s technical analysis methodology.)


The next five or so months got very interesting regarding market volatility and contagion effects. I read up again on international political economy. I also learned more about transmission shocks; political risk; hedge fund activism; and share ‘warehousing’. In October 2011, I did some further research whilst on holiday in Tokyo, Japan, including an eventful visit to the Tokyo Stock Exchange.


Drezner and I are both political scientists. One book I turned to was Timothy J. Sinclair’s The New Masters of Capital: American Bond Rating Agencies and the Politics of Creditworthiness (Ithaca, NY: Cornell University Press, 2005). A gem I discovered by accident in Sinclair’s book was about how Victoria’s conservative Kennett Government used S&P and Moodys ratings downgrades in 1993 to cut $A730 million “from Victoria’s education, health, and other programs” (Sinclair 2005: 103). In 1992, my father had co-founded Victoria’s nursing agency Psychiatric Care Consultants, which responded to the new competitive market environment. So, the S&P and Moodys downgrades had deeper personal and familial significance.


These examples illustrate how research can change the researcher.

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.




(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.

25th January 2011: Rare Earths ‘Day Trading’

In mid-2010, Ben Eltham and I discussed various ’emerging’ threats to traditional military strategy. One of them was rare earths: 11 elements used in defence, automobile applications, consumer electronics, and next generation turbines. We foresaw but didn’t act on the speculative bubble that occurred in rare earths between October and December 2010. There’s a lot driving market sentiment: ‘China’, ‘commodities’, ‘political risk’, ‘first mover advantage’, ‘iPods’, ‘greentech’, ‘next generation automobiles’, and ‘defence’.

Jason Miklian, a researcher at Oslo’s Peace Research Institute did act. Miklian invested in ‘day trade’ stocks of rare earth companies using $US9000 in personal savings. His account is revealing for several reasons. Miklian also foresaw the speculative bubble and continued to do fundamental research on the sector and markets. He timed his market entry. Then, Miklian lost what he had gained through attempting to ‘short’ the market in December 2010.

Miklian blames the market but perhaps the error lies in his ‘day trading’ strategy. Miklian traded a small account. He used options which increased his potential profits yet could quickly engulf his trading account if wrong. He bet on firms like the US-based Molycorp (MCP) which, although their stockprice doubled, are still years away from resolving the production problems with its Mountain Pass facility. Many other firms had questionable earnings and their stocks rose on mainly speculative activity. Others are relying on bullish activity when new production facilities come online in the next 18 months and major deals are signed. What Miklian perhaps needed was a valuation model and assessment of future earnings as well as his sector research. Finally, Miklian mistimed his exit. The volume of trade activity means that despite some market skepticism, trading in major stocks will continue. Technical analysis suggests that stocks of rare earths companies will trade within a range, rather than suddenly collapse.

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.

Trading Chaos

Williams, Bill & Justine Gregory-Williams.  Trading Chaos: Maximise Profits With Proven Technical Techniques (2nd ed.), John Wiley & Sons, New York, 2004.

The father-daughter authors summarise a personal methodology based primarily on: (1) the technical analysis of oscillations in market securities; and (2) the opportunities for day traders and swing traders to appropriate value from institutional funds through ‘countertrend’ signals which occur in commodities futures and currency/foreign exchange (forex) markets.  The first (1995) and second (2004) editions coincided with the IT and subprime bubbles which created day trading subcultures and market volatility, so it would be interesting to see how the authors have fared during the 2007-08 global financial crisis.

The book’s first half synthesises various ideas on formulating a trading plan and the psychology of market trading.  The ideas include a social constructionist view of money as a holder of value (John Searle); crowd psychology and rational herds in markets (Gustave Le Bon, Charles Mackay); the new paradigm of chaos theory in markets and how fractals and self-similarity create new trading perceptions about pricing and signals (Benoit Mandelbrot), and the popularity of Eastern belief systems amongst traders as models for skills acquisition and stress management (notably Western popularisations of Zen and Taoism).  Thus an awareness of broader intellectual trends can be useful to unpack the building blocks of a system and for comparative analysis with other theorists and models.

Ben Williams’ original contribution is to explain how his background as a psychologist informs his trading approach.  Chapter 7 outlines a generic model of skills acquisition — novice, advance beginner, competent, proficient and expert — that was explored in the book’s first edition, and can be integrated with Agile, CMMI and other frameworks for integrating operations and strategy.  Williams summarises exercises from autogenic training for stress control in the face of market volatility, symbolic interactionist approaches to align the trader’s individual psyche with the market, and cognitive psychology techniques such as cognitive chaining for surfacing deeply held beliefs which lead to self-sabotage and trying to trade out of a losing position without stop losses.  The cognitive psychology approach reminds me of physician John Lilly‘s mid-career work on meta-beliefs and it also parallels recent work in behavioural finance.  However, some descriptions — such as a section on Taoism, Zen and visualising the market as a river which follows the path of least resistance — seem to be closer to New Age beliefs about zero point fields which integrate consciousness and matter than the original metaphysical systems.  I agree these systems can be applied to training however they need far more grounding than detailed here.

From the earlier material on trading approaches, the book’s second half develops a trading system to anticipate the price movements in market securities through fractals and self-similarity which occur in volatility.  It’s always interesting to see how traders justify their approaches and the example trades given.  I’m closer to the adaptive markets, event arbitrage and behavioural finance schools of investing and remain to be convinced about the validity of technical analysis that the Williams propose, beyond the obvious role of pattern recognition.  Actually perceiving nonlinear dynamics and turbulence can be very different to the language and paradigmatic thought that makes chaos theory a popular explanation.

I did experience some perception changes after reading Trading Chaos: (1) charts might be interpreted in a different psychological frame using fractal, self-similarity and volatility metaphors; (2) viewing charts at different timescales (e.g. 1 hour, 1 day, 1 week) might develop the cognition skills to quickly scan signals in a real-time environment; and (3) the juxtaposition of lead and lag signals for trading decisions and triggers has potential, particularly if combined with game theoretic modelling of the market and volatility effects from institutional investors, monetary policy and rational herds.  It remains to be seen if these perceptions are sustainable and verifiable in trading conditions, and not just subjective reactions based on past research about chaos theory models.

That said, the trading system may also have several criticisms and weaknesses. Finding signals in oscillations and nonlinear dynamics may be difficult in a volatile market.  Analysts can be subjective particularly if de-leveraging and other actions by institutional investors are not factored in.  Swing traders may be exposed to market sensitivities (aka the Greeks): Gamma (the rate of change in the underlying security’s price), Vega (sensitivity to volatility), Theta (time-decay) and Rho (time-decay of interest rates).  Finally, modelling turbulence and uncertainty in a grey or white box system remains a major challenge for financial engineers in new market environments.

Threaded throughout Trading Chaos are the mix of useful insights and shibboleths in day trading subcultures.  CNBC, investment experts, and the plethora of courses and newsletters thrive on investor insecurity yet create noise (pp. 34, 42, 56).  Trading decisions, trading volume, and speed and type of momentum may be lead indicators of price volatility (p. 126).  Broad market knowledge purports to trump expert/specialist understanding (p. 135).  Market facts must be distinguished from opinions and beliefs (pp. 8-11).  Trader personalities shape risk tolerance, time horizon, the asset allocation process and types of controls (pp. 92, 155), a factor relevant to human resources consultants and the ‘transition in’ process for trading desks in investment banks.  Analysis risk involves emotions and perceptions of a signal (pp. 52-53).  The interest in Fibonacci numbers and Golden ratios are partly because they are iterative, geometric structures applicable to price movement forecasting (pp. 22-23).  Grey and white box systems with transparent, programmable rules are preferable to expensive, high-end black box systems which use artificial intelligence and neural net algorithms (pp. 53, 56).  A useful bibliography highlights the Santa Fe Institute‘s influence on chaos theory applications in finance and macroeconomics.  It suggests this area needs far more research to verify the claims and provisional findings in this book, to separate the gold from the dross.

Perhaps the most pivotal insight of Trading Chaos is buried in the text.  “We all trade our belief systems.  When some of you think about this, it produces a crisis,” the authors assert.
  Now that could be the basis for a ‘contrarian’ trading system — probably the one that hedge funds with a short/event arbitrage approach use to scalp day traders in currency/forex and commodities futures markets.