On Minyanville’s Pivot

This week I’m reading Josh Brown and Jeff Macke’s Clash of the Financial Pundits (New York: McGraw-Hill, 2014) during my work commute. Brown and Macke interview financial media pundits and bloggers. Minyanville’s Todd Harrison has overshadowed the book’s release in announcing that the popular financial news site will pivot to financial services:

 

Our current business model does not extend to financial services, and that’s OK — it’s broken anyway. I do, however, believe that what we’ve built is extremely valuable to a broker-dealer looking to leverage a fertile audience, acquire new customers, optimize the social sphere, turn clients into community, market through new channels, engage next-generation investors, and build a lifetime relationship.

This, in my view, can be accomplished by attaching Minyanville to an existing financial services firm as an incubation lab and allocating our assets and abilities across their business model. There are several reasons this makes sense — among them, education, credible content, and creativity are rare commodities on Wall Street.

Financial institutions have been reticent to embrace the online world given regulatory and reputational concerns; they now understand the digital realm isn’t going away and the millennial generation — along with a massive transfer of wealth — is quickly approaching. If they don’t incubate the human capital and creative elements necessary to service the entire vertical across multiple channels, they will be left behind.

Minyanville provides a plug-and-play, end-to-end solution that delivers smart market commentary with editorial rigor through a FINRA- and SEC-compliant mechanism. This is not traditional research; content is the best online currency — engage the audience in a daily dialogue with one foot inside the firewall (give them a reason to stay in the walled garden) and the other foot outside the firewall (broaden the brand shadow and more effectively target the marketing spend).

 

Over 14 years ago when Richard Metzger and Gary Baddeley hired me to edit the Disinformation website they were pivoting to television production, publishing, DVD, and video-on-demand interests. Stratfor’s George Friedman planned the StratCap hedge fund before Anonymous hacked his geopolitical intelligence website.

 

Behind all of these moves are two strategic realities: (1) most web content generates zero income – a painful truth for editors and writers; and (2) value creation often lies in tailored products and services for a website’s audience. Minyanville’s version of (2) was a subscription service for premium content. Disinformation’s version was book, DVD and video-on-demand projects — the site became mostly user-generated content from March 2008. This was all prior to Henry Blodget’s career ‘second act’ with BusinessInsider.

 

I made a series of decisions about these shifts over the past decade. After undergraduate and postgraduate school I pursued a university-based research career from 2004 whilst doing a second editorial stint with Disinformation. I stopped freelancing for magazines during this period due to publishing embargoes that the research consortium I worked for placed on my research. After leaving TDC Entertainment on 29th February 2008, I turned down several offers to edit websites or to be involved in publishing projects. After March 2007, I self-funded my academic research. Today, I blog – as Josh Brown does – primarily for self-education.

 

On the surface Harrison’s pivot decision for Minyanville to partner with financial services as an “incubation lab” looks like an entrepreneurial venture. I’m a little skeptical:

 

(1) As Brown and Macke show in their new book, most financial commentary is noise that is unhelpful to traders. Twitter, Andrew Ross Sorkin’s Dealbook section in The New York Times, and a Bloomberg or Wall Street Journal subscription provides most of the major financial news and the major newswire services.

 

(2) Harrison omits that most website content is usually either for subscription traffic, or is a loss leader.

 

(3) I read Fundamentals of Stream Processing (New York: Cambridge University Press, 2014) and it confirmed that the real alpha is already in complex event processing, machine learning algorithms, news analytics, and high-frequency trading algorithms. This area is at least 4 to 5 years old in quantitative finance already. It may continue to disrupt the broker service model that Harrison has in mind. How many of Minyanville’s customers really have the financial assets to become high net worth customers for a broker?

 

(4) Harrison looks to the Millennials as the new investor class – but most of them can save money and time by paying US$1 for William Bernstein’s monograph If You Can: How Millennials Can Get Rich Slowly; investing in a low-cost index fund like Fidelity or Vanguard; and reading free web commentary for self-education. More Millennials are likely to use mobile services than subscription-based websites.

 

(5) As George Friedman found with his StratCap venture, developing alpha/edge in investment and trading is a very different skillset to financial news or commentary. My experience from several different contexts over a 10-year period is that news arbitrage strategies are hyped by journalists and editors — but have significant alpha decay for traders — particularly in a market dominated by high-frequency trading algorithms and low-latency arbitrage. Brown and Macke confirm that this is the case for retail traders who try to trade the news on Bloomberg or CNBC – and that the major news outlets are set-up with availability and disposition biases in mind.

 

(6) Thomas Frank’s One Market Under God: Extreme Capitalism, Market Populism, and the End of Economic Democracy (New York: Doubleday, 2000), Thomas Schuster’s The Markets and the Media: Business News and Stock Market Movements (Lanham, MD: Lexington Books, 2006), and Dean Starkman’s The Watchdog That Didn’t Bark: The Financial Crisis and the Disappearance of Financial Journalism (New York: Columbia University Press, 2014) show that the financial media-retail trader nexus has been a problem noted in the 1995-2000 dotcom and 2003-07 real estate speculative bubbles, and also in the 2007-09 global financial crisis.

 

I will keep an eye on what Harrison’s Minyanville evolves into and what it incubates. However, Harrison’s pivot decision looks like an exit.

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 Amazon.com:

 

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.

Hedge Fund Secret Source

The New Yorker‘s John Cassidy recently asked why some hedge funds make so much money.

 

Cassidy like hedge fund critic Les Leopold focuses on two primary reasons: (1) the ‘2 and 20’ fees that hedge fund managers charge investors where the funds charge a 2% administration fee and take 20% of the profits; and (2) carried interest loopholes in United States tax laws that hedge funds are structured to take advantage of.

 

Yet the other reason Cassidy does not explore is the hedge fund secret source: their trading strategies and transaction execution capabilities.

 

The vanilla version of hedge fund strategies is well known. For instance, ‘long / short’ funds take a long (upside) position in financial securities whilst ‘shorting’ (downside) others. Global macro funds profit from geopolitical risk and central bank monetary policy. Distressed debt and special event funds make profits from turnarounds or from creating situations where there are crowded trades and rational herding among investors.

 

The secret source is how a vanilla strategy is transformed into one where there is an edge or positive expectancy that is in the hedge fund’s favour. Some pre-quant hedge fund managers learned this from formative childhood experiences playing backgammon and poker. The quants studied Andrey Kolmogorov‘s probability work, and applied it to market microstructure patterns of the order book, and price / volume dynamics. Others benefited from geopolitical events: the 1973-74 growth of offshore Eurodollar markets (Paul Tudor Jones); the European Union’s Exchange Rate Mechanism and Black Wednesday (George Soros); or understanding bubble dynamics in the 1995-2000 dotcom bubble (trader Dan Zanger) and 2007-09 global financial crisis (John Paulson).

 

One of the keys to this is having a transaction execution capability. It means having a prime broker relationship with more favourable terms than retail traders get. It means having the complex event processing / stream processing capabilities to identify edges / positive expectancy and to trade them in many different financial instruments, markets, and timeframes. This is why some retail traders look at Edwards & Magee-style technical analysis and signals software; successful proprietary traders use trading psychology and market microstructure theory; and quantitative hedge funds use computational intelligence, machine learning, and software agents.

 

Cassidy and Leopold rail against hedge fund managers as a financialisation symbol of extreme income inequality. Their arguments resonate with many people who are legitimately angry about how much money some hedge funds make – even though there is survivorship bias. But what Cassidy and Leopold may obscure is the fact that – to quote the mid-1990s television show The X-Files – the information to create hedge fund-like capabilities is out there, scattered, waiting to be identified and reassembled into new forms. William Gibson, Bruce Sterling, and Charles Stross have already given fictional hints in their novels about what this proto-cyberpunk world might resemble.

 

When these hedge fund capabilities ‘cross the chasm’ from the hedge fund managers (1%) to the multitudes (90%) then things will get even more interesting.

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.

On Marni Cordell and New Matilda

Veteran independent publisher, editor and journalist Marni Cordell has penned her final editorial for Australia’s New Matilda publication.

 

Cordell has been NM‘s editor for seven years, publisher for four years, and has done important reportage on West Papua, Timor-Leste, and social justice issues. She has supported coauthor and collaborator Ben Eltham’s national affairs reportage.

 

In her final editorial Cordell thanked NM‘s team, contributors, and readers. I would also like to thank Cordell: being an editor and publisher is a rewarding yet demanding job that requires significant behind-the-scenes investment of expertise, time, money, mentoring, and risk-taking. Each editor brings their own unique style and vision to the publication they edit – Cordell has strengthened Australian independent journalism.

 

Cordell announced NM‘s new editor is journalist Chris Graham. Cordell joins Australia’s Crikey as an editor.

H.R. Giger, 1940-2014

H.R. Giger 1940-2014

 

The Swiss surrealist artist H.R. Giger has turned up throughout my life in various ways.

 

When I was 5, my step-mother accidentally gave me a copy of Richard J. Anobile’s photo-novel for Ridley Scott’s Alien (New York: Avon, 1979) with Giger’s biomechanoid designs. In my mid teens I listened to Swiss avant-garde metal band Celtic Frost. I played and failed to complete the Dark Seed computer game (2002); and I got a Giger tarot deck of the major arcana. In my early twenties I read psychologist Stanislav Grof on Giger (PDF). In 2000, I wrote a Giger profile for the dotcom era incarnation of the Disinformation website (the archived version will have dead links). In 2003, I did a longitudinal analysis of how Giger’s aesthetic had influenced my life (an appendix omitted from the public PDF version). In October 2011, I discovered the unauthorised Giger bar had closed in Tokyo, Japan. More recently, I listened to Tryptikon who featured Giger’s artwork on their first two albums (Thomas Gabriel Fischer penned a Giger obituary), and used Giger’s tarot deck for readings with international friends.

 

Giger’s aesthetic vision and mastery set him apart from others who attempt to evoke daemonic beauty. He lived in his own self-created world. Giger’s perinatal imagery is a calling card from a possible, biomechanoid future.

ISA 2014 Reflection: A Spectrum of Strategic Culture Theory-building

At ISA 2014, I saw a range of panels on strategic culture, constructivism, causality, counterfactuals, forecasting, and intelligence analysis.

 

Four insights emerged immediately from attending these ISA 2014 panels and discussions:

 

(1) Strategic culture is a framework to understand how long-term, culturally transmitted factors and shared socialisation experiences can shape leaders and politico-military elites. I reached a different view to Michael Desch: strategic culture is not necessarily oppositional to Waltzian structural neorealism and can learn from the theory-building rigour of neopositivist international relations.

 

(2) Desch’s framing of strategic culture and structural neorealism as rival schools was in part due to constructivism’s popularity in the mid-1990s. Alexander Wendt, Peter Katzenstein, Martha Finnemore and others explored the role of agency compared with Kenneth Waltz’s emphasis on structure. Strategic culture was posited as a dependent or mid-range variable. Yet if strategic culture is decoupled from this Lakatosian comparison of two rival schools then it can learn theory-building insights from both constructivism and structural neorealism: it becomes part of a spectrum.

 

(3) Strategic culture’s theory-building cycles suffer from the exodus of potential theory-builders. The first generation’s Colin S. Gray and Ken Booth each developed richer interpretative and theoretical approaches later in their careers. The second generation’s Bradley S. Klein delved more into critical and postmodernist theory. Alastair Iain Johnston left strategic culture after the so-called Gray-Johnston debate. The so-called fourth generation has focused more on foreign policy analysis as a means for theory-testing rather than the first generation’s emphasis on grand theory-building. ISA2014 made me consider that theory-building and foreign policy analysis are another possible spectrum to explore.

 

(4)  My most immediate theoretical interest with strategic culture theory-building lies in the development of formal models – specifically on the potential microfoundations of strategic culture. ISA2014 had a series of panels on puzzles and formal models that I did not get to see but that I took note of. John Vasquez and Richard Ned Lebow suggest different possibilities I will explore further.