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.
This evening I borrowed R.P. Suri’s book Introduction to Prolog (Alpha Science International, 2007) from RMIT University’s library. There are shelves of books on C, C++, Java, Linux, Python, and Unix. There are five or six books on Prolog which was influential in the 1980s and early 1990s before being eclipsed by other computing languages. I reflected whilst looking at Prolog on unpublished research from 2006-07 in the Smart Internet Technology CRC on agile software development, design patterns, and object oriented code. I am not a programmer but the formalism of Prolog, design patterns, refactoring, and domain-driven development are useful constructs to capture information in a systematic way, and to decompose problems. I also find in going back to early 1990s books on Prolog, expert systems, and case based reasoning that there are insights about computing potential that differed from what arose in the 1995-2000 dotcom period and the later Web 2.0 era. Becoming proficient in Prolog also means I have to understand logic, reasoning, and inference. These areas prompt me to re-evaluate some past published articles and research.
Dynamic Hedge on using the Zen-like beginner’s mind to find effective simplicity:
We need a certain level of refinement to talk about the specialized problems of each industry but don’t let that garbage get in your way — especially if you’re just getting started. In other words, don’t be afraid to seek out something effective before you reach anything even resembling sophistication. Some of the best strategies are extremely simple. Low hanging fruit is still available because people overlook it. Don’t assume something won’t work before you test it out. Countless people never start because they are worried about being embarrassed or making “obvious” mistakes that will tip off the world to their amateur status. Or they latch on to a flawed ideology because it sounded complicated enough to work and the message was delivered with confidence of a sophist. [emphasis added]
I will keep this in mind as I build my Prolog decision rules for a white box trading capability.
During the past two days of my work commute I looked at Ryan Mallory’s The Part-Time Trader: Trading Stocks as a Part-Time Venture (Hoboken, NJ: John Wiley & Sons, 2013). Mallory runs the website SharePlanner.com. He was once a contracts manager for a Fortune 500 company. Much of the book deals with office politics reminiscent of The Office television series and the film Office Space which both Mallory and Venkatesh Rao like. Reading The Markets’ Brenda Jubin has a good summary of the book’s key points.
Mallory’s preference is to transition from a part-time trader whilst at work to becoming a full-time trader. This transition appeals to the United States trader subculture hence Wiley’s publishing contract for the book. What matters here is the dream image or ideal — the Ka to the ancient Egyptians — where Mallory mentors his readers to make this transition.
I am starting to form a different view to Mallory’s preference.
First, you have to treat the popular trading literature as a form of ideology about capital flows and financialisation. Mallory is best when he gives advice on heuristics: don’t look at profit/loss during a trade because this creates anchoring and representativeness biases, for instance.
Second, you have to screen out or at least ‘reality check’ the aspirational dialogue about becoming a full-time trader. Most brokerage accounts are under-capitalised and ‘blow up’ in 6 to 18 months of trading. The existence of subscription-based services like Mallory’s site hides this pattern. It also obscures the importance of survivorship bias when assessing trading records. Texan author Don Webb’s insight that occulture involves “selling water by the river” also applies to some trading books and overlaps with the entrepreneurship literature.
Third, the decision to trade full-time involves an emphasis on success in the present over long-term financial compounding. This affects both future salary and superannuation entitlements. You really need an edge and positive expectancy in your trading system. You need to be clear on what your alpha is (excess returns adjusted for risk and actively managed) and from whom it is being extracted from. These are critical details that Mallory understandably omits — but they are just as critical as specific trading set-ups.
Fourth, changes that include a mix of sustaining and disruptive technologies, and new market microstructure, affects Mallory’s advice. You can now trade on a mobile phone for intraday and swing trading, rather than relying on monitor privacy and flying ‘below the radar’. Constant monitoring of markets can create high transaction and execution costs, and can affect taxation. This is where the ideology or image of popular trading can become self-defeating when compared with proprietary, fund management, or institutional strategies.
Fifth, in the next 5-to-15 years there will be brokerage level versions of popular market algorithms and machine learning capabilities. These quantitative frameworks and tools — which exists for institutional traders yet not for retail traders — will disrupt many of the small capitalised, full-time traders who are the book’s audience. One of the key ways to also track this is the size of margin loan lending conducted in the major brokerages.
Mallory’s book was useful to think through these observations and to develop a different personal strategy.
For starters, I look more closely now at the optionality or upside potential of research programs.
Tonight, I started programming a knowledge base in the Prolog computer language used in artificial intelligence. This will also form part of how I map out the underlying logics of two PhD case studies on strategic culture before I get to specific research methods (process tracing for one case study and thematic coding analysis for the other). Whilst coding I read Cullen Roche’s post on why stockmarket speculation is neither investment nor usually wealth generation:
Now, this doesn’t mean it’s impossible to become wealthy picking stocks on a secondary market. There will always be people who “beat the market” and ride stocks to riches. But that’s not the point of this story. The point is, in the aggregate, the market returns the market return and the market return isn’t likely to make investors rich quickly in the aggregate. In fact, you’re almost certainly buying an asset that has already made someone else rich well before you ever had the opportunity to own a claim on that asset’s cash flows. This doesn’t mean that buying stocks and bonds is bad. It doesn’t even mean you can’t “beat the market”, but we should be careful about the concept of “investing” and how it actually leads to us becoming wealthy. You’re much more likely to become wealthy investing in your own ability to generate future production than you are by buying an asset that was actually someone else’s investment. [emphasis added]
Buried in Roche’s analysis is a comparison of cowboy intraday traders / momentum traders / trend-followers and Jack Bogle style index investment. This is like a battle between a discredited, naive form of active management and low-fee passive management that disrupts mutual funds. It’s a narrative that turns up in a lot of introductory books for retail investors.
But there are at least two other options found more in funds management: (1) using arbitrage to find or to create market inefficiencies; and (2) trading the asset price changes that occur in market microstructure due to the order flow (and transaction / execution algorithms) of different market participants (such as proprietary trading desks; hedge funds; sovereign wealth funds; company directors; and noise traders).
The first is a goal of activist hedge funds who use behavioural finance to create situations of crowded trades and rational herding; the second informs quantitative trading techniques like machine learning and statistical arbitrage.
The first leaks to the media; the second uses news and text analytics to anticipate microstructure changes in asset prices. I spent a decade studying the first in media analysis, publishing and research. Now, I am interested in the quantitative insights of the second. This feels like a self-disruption of past work.
System designers combine both options mentioned above in funds management: the first uses human intelligence; the second relies on computational intelligence.
It will take a few years to complete the Prolog knowledge base project.
When I’m done, I will have decomposed the relevant literature into white box models (Bayesian decision rules). Then, I will start on the black box version with live data. I have access to live data now – but I need more of a background in algorithms, machine learning, stream processing, and high-frequency econometrics – to really begin work on the black box version.
The foundations are in place.
Jessica Pressler’s profile of Washio and Silicon Valley’s laundry wars features this gem:
Salama did a small inner fist pump. This was just the kind of inefficiency he’d sought to exploit as a trader at SAC Capital, the notoriously hypercompetitive Greenwich hedge fund where he’d worked after college. He’d left the job in 2008 after the market crashed and the atmosphere took a turn for the worse. “When times are good, it’s semi-poisonous,” says Salama, now 31. “When times are bad, it’s just outright hostile.” But just as he can’t shake his Brooklyn accent, he retains a certain amount of SAC muscle memory. “You’re trying to beat a market,” he says. “So you realize the importance of trying to think in ways other people are not.” [Emphasis added; for examples read Michael Mauboussin‘s investment work.]
Also this anecdote:
While their competitors were elbowing one another in Silicon Valley, FlyCleaners had been scouring Silicon Alley for a team as tough and experienced as the founders themselves. Among its recruits was Brian Tiemann, a software engineer from Bridgewater Capital, the world’s largest hedge fund. “You were expecting laundry machines,” Tiemann intones from behind the Star Trek–like array of screens, when I enter FlyCleaners’ Flatiron offices on a recent visit. Blond and bespectacled, Tiemann is that rare breed of tech nerd who took a job at a hedge fund not for the money but because of the technological opportunities it afforded. From the looks of him, he doesn’t know from fabric softener, but he enjoys the logistics of getting laundry and dry-cleaning all the places it needs to go. Squinting at the screen, Tiemann types in a command, enabling a driver to avoid a traffic jam on North 6th Street in Williamsburg.
In New York, hiring drivers on Washio’s Uber-inspired model wasn’t an option. FlyCleaners had to use trucks, and because of the traffic and narrow streets, the trucks had to be efficient. They built racks for laundry bags, and Tiemann, whose hobby is pimping out cars for the Bullrun, the annual race in which billionaires in souped-up vehicles race each other cross-country, outfitted each one with a tablet that provides drivers with order details, alternate traffic routes, selective streaming from accident-mapping services, and direct communication with headquarters. With guys like this at the controls, mom-and-pops don’t stand a chance. “That’s the idea,” Tiemann says grimly, sinking back into his screens. [emphasis added]
Jehane Noujaim’s third film The Square tracks the lives of four Egyptian protesters during two years in Tahrir Square, Egypt. The Square covers the fall of Egyptian leader Hosni Mubarak; the Egyptian Army’s deal with the Muslim Brotherhood; the 2012-13 term of president Mohamed Morsi; and how the protesters interacted with the Egyptian Army and Western media.
Noujaim continues themes from her earlier films including using a small group as a symbol of broader social forces (Startup.com‘s study of the 1995-2000 dotcom crash); and how senior army officials deal with social activist media (Control Room).
The dialogue between activists about the Muslim Brotherhood recalls P.R. Sarkar’s analysis of the Hindu case system articulated by Pakistani futurist Sohail Inayatullah in the Sarkar Game: the Egyptian Army (khsatriya military) deal with the protesters (shudra workers, vipra intellectuals) in a vacuum of enlightened Sadvipran leadership, and in which the Muslim Brotherhood broker deals that affect election outcomes (vaeshya merchants). The Square captures the gap between revolutionary ideals of social illuminism and how these play out amidst different power dynamics, values, and worldviews.
The protesters filmed express a still-forming revolutionary praxis and worldview: they might have benefited from awareness of the late sociologist Charles Tilly’s study of protests and regime responses (Regimes and Repertoires); Gene Sharp’s work in peace studies on nonviolent strategies; and constructivist institutionalism theories in political science on how to transform major social institutions like the government, judiciary, armed forces, and political parties.
The Square hints at unexplored topics that might inform other documentaries on Egypt’s sociopolitical changes. These unexplored topics include: the history and role of Western governments who backed Mubarak’s regime; the Egyptian Secret Police’s targeting of domestic political dissent; political Islamist controversies involving the Muslim Brotherhood in Egypt; and the CNN Effect’s variability that involves Western media pundits, geopolitical flashpoints, and human rights challenges.
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.
Portfolio manager Conor Sen suggest active management will survive due to several factors: Gen-X / Millennial investors; crowded trades in passive investing strategies; “opportunistic CEO’s”; and developments in algorithmic trading. Sen also notes that hedge fund and institutional money managers still have an aura: this is a cultural myth sustained by endowment and pension fund flows.
In the long-term, I see a new rentier class emerging to service the 1% elite. Today’s asset classes will be modelled as alpha generation, trading, and execution algorithms. New types of arbitrage will be developed. The existing cultural myths will continue as a smokescreen. The truth may filter out in other sources: investigative journalism exposes of offshore tax havens; or Russian download sites on foreign exchange trading in Eurodollar markets.
Sen’s active managers of the future may be closer to Charles Stross or William Gibson than the 1980s era of the Big Swinging Dick trader (Michael Lewis) or Masters of the Universe (Tom Wolfe).
The New Yorker reports that two University College London researchers have empirically validated the existence of hot hand effects — shifting probabilities made in consecutive bets during a winning streak — in online betting:
Juemin Xu and Nigel Harvey, the study’s authors, took a sampling of 569,915 bets taken on an online sports-gambling site and tracked how previous wins and losses affected the probability of wins in the future. Over all, the winning percentage of the bets was somewhere around forty eight per cent. Xu and Harvey isolated the winners and tracked how they fared in their subsequent bets. In bet two, winners won at a rate of forty-nine per cent. From there, the numbers go haywire. A player who had won two bets in a row won his third bet at a rate of fifty-seven per cent. His fourth bet won sixty-seven percent of the time, his fifth bet seventy-two. The best gamblers in Las Vegas expect to win fifty-five per cent of their bets every year. Seventy-two per cent verges on omniscience. The hot hand, it appears, is real.
Losers, unsurprisingly, continued to lose. Of the 190,359 bettors who lost their initial bet, fifty-three per cent lost their next, and those who had enough money left for a third round lost sixty per cent of the time. When unfortunate bettors got to five straight losses, their chance of winning dropped to twenty-three per cent. The losing streaks should be familiar to problem gamblers and can be explained by another well-worn theory called the gambler’s fallacy. If you’ve ever called heads on a coin flip, seen the coin land tails up, and then called heads again because “heads is due,” you’ve been caught up in the gambler’s fallacy. [emphasis added]
The original study can be found here.
The study’s findings have implications for the subjective judgment of independent events; the difference between skill and chance; the halo and winner effects due to hot hand streaks; and the survivorship bias of traders and funds who survive over a long period of time – until asset classes have alpha decay, and financial markets undergo market microstructure or regime changes.
The New Yorker‘s commentary notes but does not explore the statistical edge that the best Las Vegas gamblers develop: the study suggests that such an edge may be situational or fleeting.
This behavioural finance research is extremely useful to understand how cognitive biases can affect decision-making under uncertainty.