New Books on Al Qaeda’s Strategic Culture

In 2011, my PhD supervisors asked me about a planned case study on Al Qaeda’s strategic culture. Now, there are two books out that address this issue:


  • Michael W.S. Ryan’s Decoding Al Qaeda’s Strategy: The Deep Battle Against America (New York: Columbia University Press, 2013).
  • Donald Holbrook’s The Al-Qaeda Doctrine: The Framing and Evolution of the Leadership’s Public Discourse (New York: Bloomsbury Press, 2014).


There are now some case studies and further analysis to answer this initial query.


Alastair Iain Johnston’s third generation of strategic culture focused on organisational studies. A relevant book that may link this third generation to the study of terrorist organisations is Vahid Brown and Don Rassler’s Fountainhead of Jihad: The Haqqani Nexus, 1973-2012 (New York: Oxford University Press, 2013).


I’m also looking at Peter Bergen’s reportage on Al Qaeda – so his forthcoming book United States of Jihad: The Untold Story of Al-Qaeda in America (New York: Crown, 2015) may also be relevant.

Australia’s Strategic Culture

Deakin University’s Ben Eltham and I have a new paper out in Contemporary Security Policy journal that draws on my PhD research. Taylor & Francis has the electronic copy available online now to journal and institutional subscribers; the print version is due out 23rd July.


Here’s the article’s abstract:


This article draws on fourth generation strategic culture debates to show the gap between the rhetoric of Australian defence and the more modest reality. Our analysis shows that these limits derive from tensions between national strategic culture and organizational strategic subcultures. There are serious debates in the nation regarding the preferred course of the Australian military and security policy. This article frames these debates by examining the ‘keepers’ of Australia’s national strategic culture, the existence of several competing strategic subcultures, and the importance of norm entrepreneurs in changing defence and national security thinking. Strategic subcultures foster compartmentalization, constraints, and bureaucratic silos that narrow national conceptions of security threats and opportunities, and impinge on the formation of coherent foreign and defence policy in relation to the Asia-Pacific region. This analysis shows that a distinct national strategic culture and organizational strategic subcultures endure beyond individual governments, placing potential limits on Australia’s interface with other Asia-Pacific strategic cultures in the future.


My thanks to Wooster College’s Jeffrey Lantis for organising the CSP special issue on strategic culture; the three anonymous and extremely helpful reviewers; and CSP‘s editorial and production staff.

A PhD Write-Up Update

From an email to my PhD Supervisor about what I’m working on:


  • A revised Chapter 1 on strategic culture will now include a new conceptual framework that examines and integrates the SC literature on two dimensions: theory-building and foreign policy analysis. For now, I have dubbed this a ‘spectrum framework’. It incorporates feedback from the ISA panelists, and from Jeffrey Lantis on recent theory-building / research design debates in the so-called fourth generation (post-2000) of strategic culture. I will contrast the proposed new framework with Alastair Iain Johnston’s generations framework (from his PhD and book Cultural Realism, and from the 1995 International Security article ‘Thinking About Strategic Culture’).


  • A revised Chapter 2 will include a formal model of strategic culture / subcultures in terrorist organisations. Jacob Shapiro’s recent book The Terrorist’s Dilemma: Managing Violent Covert Organizations (Princeton, NJ: Princeton University Press, 2013) is directly relevant, and may answer some of the concerns you initially raised about how to study terrorist organisations from an organisational perspective. For the chapter format I am using as a ‘writing model’ example Chapter 2 from Michael C. Horowitz’s PhD and subsequent book The Diffusion of Military Power: Causes and Consequences for International Politics (Princeton, NJ: Princeton University Press, 2010) in which Horowitz presents his Adoption-Capacity Theory.


  • Rather than a separate methodology chapter I am thinking of integrating this into methodology sections in the two case study chapters. The methods to be used are: causal / decision / process tracing of the Peter Bergen / Steve Coll / Michael Scheuer / Lawrence Wright investigative journalism  (Al Qaeda chapter), and interpretivist / qualitative / thematic text coding of Robert Jay Lifton / Haruki Marukami interviews (Aum Shinrikyo chapter). Two of the key methods books I am using are Derek Beach and Rasmus Brun Pedersen’s Process-Tracing: Foundations and Guidelines (University of Michigan Press, 2013), which I picked up at ISA, and Greg Guest, Kathleen MacQueen, and Emily E. Namey’s Applied Thematic Analysis (Thousand Oaks, CA: Sage, 2012). I attended a couple of ISA panels with Patrick Thaddeus Jackson (The Conduct of Inquiry in International Relations) that were helpful to think through methodological issues.


  • I have some material for Chapter 5 on Conclusions / Further Research.


  • I have started to scope some material that might inform future journal articles beyond the PhD, such as the use of knowledge representation / microfoundations for the underlying logics, and computational cultural psychology as one of several new methodologies for future case studies. I also found this week a parallel approach to my case studies in the new book Global Shell Games: Experiments in Transnational Relations, Crime, and Terrorism (Cambridge University Press, 2014) which uses an experimental political science approach to study organisations, and which has Jason Sharman (Griffith University) as a book co-author. So, more for post-PhD work, I am also considering experimental research methods as a possible avenue.

The Fissured Workplace

The Fissured Workplace

Contemporary workplaces are changing. Private equity models inform stretch targets and strategic plans. Operational restructures and financially engineered turnarounds now occur in mid-market firms and government departments. Contractors and outsourcing mean a more diverse workforce. Boston University professor David Weil – now the Obama Administration’s first Wage and Hour Administrator – offers a well-researched and at times confronting analysis in The Fissured Workplace: Why Work Became So Bad for So Many and What Can Be Done to Improve It (Cambridge, MA: Harvard University Press, 2014) of the impact on wages and workers’ standard of living. Weil’s book will give you some of the labor economics context for the firm and industry restructures that now occur in finance, higher education, logistics, and service industries.

Price Signals and Publishing

Today, I received notification that Contemporary Security Policy has accepted an academic article on Australian defence and national security policy I coauthored with Deakin University’s Ben Eltham.


Eltham also wrote for Australia’s New Matilda on the late economist Gary Becker and price signals:


Becker’s idea of “human capital” has been among his most influential. This is the notion that getting an education is, in a very real sense, investing in yourself. “If you’re in an environment where knowledge counts for so much, then if you don’t have much knowledge, you’re gonna be a loser,” he once said.

Attitudes like this make Becker the patron saint of neoliberalism. As no less a thinker than Michel Foucault observed, Becker saw the rational individual as an “entrepreneur of himself, being for himself his own capital, being for himself his own producer, being for himself the source of his earnings.


Juxtaposing what we wrote with Eltham’s analysis offers insights about academic publishing.


Research managers have adopted Becker’s advocacy of human capital. This means that academic publishing is often judged on three output measures: (1) journal rankings; (2) academic citations; and (3) the government income a university receives for each academic’s publication.


This has some subtle effects on academic publishing. Fields like anthropology or political science — which require fieldwork or extensive modelling — have different publication rates than some laboratory-based science. The latter enables researchers to publish more papers. This creates a Matthew Effect or Winner-Takes-All dynamic: more income is generated and hopefully more academic citations will occur. These outcomes are examples of Becker’s pricing signals: each publication becomes an output of workload activities (for cost and business process management) and a monetisable income stream (for J-curve patterns in entrepreneurial venture capital: an academic will generate more value as their career unfolds).


These price signals have anchoring, disposition, and representativeness biases that can lead some research managers to potentially misjudge the effort involved in getting a paper published. This is where Nassim Nicholas Taleb’s heuristic of having ‘skin in the game’ as a published academic author can be important to facilitate judgments. In our case, Eltham and I spent 18 months writing at least three drafts. We had to rewrite sections for two changes in Australia’s federal government. We had to address new literature. Our special issue editor also edited the paper. I edited the endnotes twice. We got extensive, critical, and helpful comments from three knowledgeable reviewers. I also got feedback during an international conference panel — where I met the journal editor — and from seeing other panels on parallel research programs.


This also involved a lot of effort and coordination that formal workload models often do not capture.


Narrow interpretations of these price signals can also ignore cumulative learning effects. Eltham and I learned several things in writing our just accepted paper. We self-funded the research as academic entrepreneurs. An earlier article draft had a comparison of United States, United Kingdom, and Australian defence and national security exercises that might become a separate article. We started to co-develop a microfoundations model of strategic culture that first arose when Eltham recommended I read Dan Little’s Microfoundations, Methods, and Causation: On the Philosophy of the Social Sciences (Transaction Publishers, 1998). I learned a lot about national security and recent Australian policymaking innovations: a socialisation process.  These are just some examples of what occurred over an 18 month period.


Often, research managers bring up price signals in terms of value creation. However, can be in the narrow sense above of a journal ranking; citation metric; or a dollar value for income generated. Whilst these are important they are only part of the full spectrum of potential value creation that can occur when academic coauthors collaborate on a research article or a project. Yet the conversation is often as if tools like Real Options valuation or Balanced Scorecard reporting (which acknowledges learning) were never created. The problem isn’t the use of managerial frameworks: it’s that they can be used in a shallow and superficial way for less-optimal outcomes.


Collectively, these challenges mean that academics and institutions alike never realise the full spectrum of potential value creation from an academic publication. Becker saw investment. Foucault saw entrepreneurship. I see the potential for knowledge commons arbitrage. Perhaps that’s why academics enjoy the international conference circuit so much. Sometimes the potential value creation can be more like work-life balance: Taleb wrote Antifragile: Things That Gain From Disorder (New York: Penguin Press, 2012) in solitude, to distill his life experience as an options trader and his love of classical philosophy. Read it on your next study leave period.

Life Alpha Sources

Alpha in investment usually means: (1) excess return; (2) adjusted by risk; and (3) earned by active management.


More generally, alpha signifies the people and resources that contribute to life significance.


This morning I made a list of alpha sources in my life. These ranged from my PhD studies and cumulative research experience to membership of international scholarly organisations. Several themes emerged:


  • The alpha sources fell into three major categories: (1) capital; (2) knowledge / networks (as resources); and (3) decisions that led to specific, mindful actions that established cause-effect chains (causality).
  • Positive changes to capital and knowledge / networks also expanded my decision scope.
  • Utilisation means better actions on capital and knowledge / networks (pragmatics).
  • Academic success has a winner-takes-all dynamic that is also a J-curve with asymmetric payoffs for those who can survive the ‘up or out’ career dynamic.


I also noted the following after completing most of my annual tax expense estimates:


  • At least 10% of my yearly income goes to resources for personal research projects.
  • What if the personal research projects were income-producing?
  • I have spent the past decade in combinatorial search through various disciplines; now I am developing a personal synthesis that draws on all of these experiences.
  • I have access to academic networks and institutional libraries that expand the scope and reach of my personal research projects. This can be a problem: shifting goal-posts.
  • The employers I have worked for are increasingly top-down, fragilista (Taleb), and run on a private equity model.
  • Informational resources are expanding; bureaucracies are creating new ideological myths in response.
  • I signed some bad contract deals early in life that still financially affect me (namely, student debt).
  • Having ‘skin in the game’ heuristic (Taleb) changes how to deal with sucker bet dynamics: you become aware that you are placed in a sucker position (single point of failure) even if you can’t change it (external costs shifted to you).


Some final thoughts:


  • The core resources I need for personal research — a computer, a personal research library, an inter-library loans card, and personal blog facilities — can be run on a tighter budget than I have allowed over the past decade.
  • The core challenges I have are: (1) having enough capital (including to hedge downside risks); (2) cultivating high energy / focus to do optimal research work; and (3) making daily progress amidst life changes and work commitments / routines.

Sebastian Mallaby @ CFR on Hedge Funds


Sebastian Mallaby’s More Money Than God: Hedge Funds and the Making of a New Elite (New York: The Penguin Press, 2010) is an informative history and defence of hedge funds as an alternative investment vehicle. Mallaby’s 2010 talk at the Council on Foreign Relations captures the book’s major talking points and illustrates how policymakers talk to each-other. Author Chrystia Freeland handles the Q&A giving an early glimpse of themes from her book Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else (New York: Penguin Press, 2013). The Mallaby-Freeland exchange suggests a possible invisible college or citation network around 1% socio-economic elites. This work informs post-PhD research into the possible strategic subcultures of specific hedge funds and hedge fund managers.

Literature Review on the Black Box

Rishi K. Narang’s book Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading (Hoboken, NJ: John Wiley & Sons, 2013) proposes a generic model of a black box trading system.


Narang’s generic model features: (1) an alpha module for alpha generation; (2) a risk module for risk management; and (3) a transaction cost module for costs. These feed into (4) a portfolio management module, which then feeds into (5) an execution module.


High frequency trading’s innovation was to alter the alpha /risk / portfolio equation through changes to transaction cost and execution strategies.


As an exercise I used Narang’s categories from his generic module to organise some Amazon Kindle trading books:


  • Alpha module: 83 books.
  • Risk module: 65 books.
  • Transaction Cost module: 22 books
  • Portfolio Construction module: 38 books
  • Execution module: 85 books


There is some overlap in books between each of the categories. I also used some additional categories:


  • Algorithmic trading: 80 books
  • Trading strategies: 229 books
  • Trading psychology (including therapeutic manuals): 174 books
  • Funds: 76 books


A couple of observations from this initial cumulative literature review of black box trading systems:


  • Most of the public trading literature deals in an unstructured way with alpha strategies or with trading strategies – the overwhelming emphasis is on momentum and trend-following strategies that high-frequency trading has now disrupted. Some of these books are still variants on trading strategies from the pre-dotcom 1990s. Some publishers recycle themes using art design, new authors, and small, cumulative information. In contrast, some of the most interesting information comes from outlier authors. I have screened out most of the cheap Kindle books that now add noise to new retail traders.
  • The real sources of institutional or proprietary alpha are only hinted at in the publicly available trading literature – and is more often glimpsed in investigative journalism accounts. Many trading books are written by pseudo-retail traders who have developed white box trading systems using basic technical analysis, risk, and money management rules. Jack Schwager’s Market Wizards series remains influential and significant in part because it offers a glimpse of how professional traders and successful money managers actually think.
  • The literature on trading psychology developed in part as a way to deal with the methodological limitations of the Edwards and Magee school of technical analysis that focused on signals and indicators.
  • The portfolio construction literature emerged from Harry Markowitz’s work in corporate finance, and later, David Swensen’s development of the Yale endowment model of foundation investment.
  • The risk literature covers either traditional corporate finance models, value at risk models, post-Taleb extreme value models on tail risk, or recent applications of Bayesian probabilities to portfolio models.
  • The funds literature covers white box versions of hedge fund, mutual fund, and sovereign wealth fund strategies.
  • The algorithmic trading literature covers general overviews, order types, white box strategies, and some computer science / programming manuals on algorithms. There is very little publication of actual trading algorithms or code. A computer science / programming background is helpful for quantitative finance.
  • The rise of high frequency trading has led to a greater focus on transaction costs and execution as sources of competitive edge. This emphasis differs from the Edwards and Magee focus on signals and indicators that provide set-ups for possible trades. It’s also what is missing from much of the publicly available literature on trading systems (which itself is very fractured). Thus, most trading books suffer from transaction / execution cost decays.


This initial literature review suggests the following strategies for future systems development:


  • Continue to find potential sources of alpha whilst noting the patterns of alpha decay (i.e. how alpha ends).
  • Decompose the alpha-risk-portfolio literature into checklists, an expert system, portfolio screens, or rules with an awareness of Bayesian probabilities (= edge / positive expectancy as ‘go / no go’ criteria: if there is no real edge then don’t trade – and this also involves understanding other traders and known trading algorithms). Eventually, this ‘explicit’ codification may be integrated with an off-the-shelf machine learning system such as David Aronson and Timothy Masters’ TSSB software.
  • Screen out the trading strategies that are now unsuccessful in the current market environment (strategy decay): focusing on transaction / execution costs will be helpful.
  • Continue to do developmental / therapeutic work for cultivating expertise and improving decision heuristics / judgment.
  • Search for new opportunities that involve more competitive transaction / competitive costs (although this is difficult as an Australian-based retail trader due to broker / exchange / platform  limitations).

Thematic Analysis of a Reading List on Investment Alpha

I recently did a thematic analysis of a reading list on investment alpha, which involves:


1. Excess return.

2. Active management.

3. Adjusted risk.


The following themes emerged from the reading list, and from also checking the rankings of several hundred books at


1. Excess return: fund type (hedge fund, private equity, venture capital); return drivers (including asset class); and quantitative models.


2. Active management: discretionary (human trading, portfolio composition and rebalancing, options, technical analysis) and algorithmic (algorithmic trading; complex event / stream processing; computational intelligence; genetic algorithms; machine learning; neural nets; and software agents).


3. Adjusted risk: Bayesian probabilities; investor psychology; market microstructure; and risk management models (such as Monte Carlo simulation, Value at Risk, and systematic risk)


This core work suggests the following query line:


SELECT return drivers (Bayesian belief network) (multi-asset) (portfolio) (fund)



WHERE risk (Bayesian probability) (exposures) (exposures – investor decisions) (exposures – market microstructure) AND trade (algorithms)


ORDER BY Bayesian (belief network, probability); return drivers (multi-asset); risk (exposures); and trading (algorithms).


This thematic analysis will help to focus my post-PhD research on the sociology of finance into the following initial research questions:


1. What is the spectrum of possible return drivers in a multi-asset world?


A good model for this is David Swensen’s Yale endowment portfolio detailed in Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment (New York: The Free Press, 2009). Antti Ilmanen’s magisterial Expected Returns: An Investor’s Guide to Harvesting Market Rewards (Hoboken, NJ: John Wiley & Sons, 2011) has information on the return drivers of specific asset classes. Matthew Hudson’s recent Funds: Private Equity, Hedge Funds, and All Core Structures (Hoboken, NJ: John Wiley & Sons, 2014) deals with global fund structures.


2. What specific risk exposures might these multi-assets face, and under what conditions?


Richard C. Grinold and Ronald Kahn’s Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk (New York: McGraw-Hill, 1999) is the classic book on institutional portfolio models. Morton Glantz and Robert Kissell’s Multi-Asset Risk Modeling: Techniques for a Global Economy in an Electronic and Algorithmic Trading Era (San Diego, CA: Academic Press, 2014) is a recent book I will look at. Charles Albert-Lehalle and Sophie Larulle’s Market Microstructure in Practice (Singapore: World Scientific Publishing Company, 2014), and Thierry Foucault, Marco Pagano, and Ailsa Roell’s Market Liquidity: Theory, Evidence, and Policy (New York: Oxford University Press, 2013) deal respectively with the practice and theory of contemporary financial markets. There are many books on behavioural finance and investor psychology: two recent ones are H. Kent Baker and Victor Ricciardi’s collection Investor Behavior: The Psychology of Financial Planning and Investing (Hoboken, NJ: John Wiley & Sons, 2014), and Tim Richards’ Investing Psychology: The Effects of Behavioral Finance on Investment Choice and Bias (Hoboken, NJ: John Wiley & Sons, 2014).


3. How can algorithmic trading and computational techniques model the risk-return dynamics of alpha generation?


Despite its flaws Rishi K. Narang’s Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading (New York: John Wiley & Sons, 2013) opened my eyes to the structures needed for alpha generation. The Bayesian approach is detailed in David Barber’s Bayesian Reasoning and Machine Learning (New York: Cambridge University Press, 2012). Barry Johnson’s Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies (London: 4Myeloma Press, 2010) and Robert Kissell’s The Science of Algorithmic Trading and Portfolio Management (San Diego, CA: Academic Press, 2013) deal with order types in algorithmic trading. Christian Dunis, Spiros Likothanassis, Andreas Karathanasopoulos, Georgios Sermpinis, and Konstantinos Theofilatos have edited a recent collection on Computational Intelligence Techniques for Trading and Investment (New York: Routledge, 2014). Eugene A. Durenard’s Professional Automated Trading: Theory and Practice (New York: John Wiley & Sons, 2013) covers software agents. For retail trader-oriented applications of data mining, machine learning, and Monte Carlo simulations there is Kevin Davey’s Building Algorithmic Trading Systems: A Trader’s Journey from Data Mining to Monte Carlo Simulation to Live Trading (New York: John Wiley & Sons, 2014), and David Aronson and Timothy Masters’ Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB (CreateSpace, 2013).


What this means is that for an investment of about $US1,000 a new researcher can gain some of the core books on institutional, quantitative portfolio and risk management; behavioural finance and market microstructure as potential sources for edges; and some recent practitioner-oriented literature on algorithmic / automated trading that uses computational intelligence.


In deference to Mao and McKenzie Wark’s vectoralist class:


Let a thousand algorithmic / quantitative micro-funds bloom.

Birinyi / Wyckoff

Wyckoff Market Cycle (Source:

Wyckoff Market Cycle (Source:

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.