IHWL – IShares Core MSCI World All Cap (AUD Hedged) ETF (factsheet).
The initial impetus for this emerged from reading S.M. Amadae, David Graeber, David Harvey, Michael Hudson, David Michael Kotz, Philip Mirowski, and James Rickards about neoliberal capitalism. These authors converged on the FIRE sector (finance, insurance, and real estate) as a socio-economic elite that would continue to charge debt and economic rents, as rentiers.
Current and former colleagues have looked to innovations like the sharing economy as a way to deal with growing economic inequality. I came to different conclusions: I spent six years learning how hedge funds and proprietary trading works. The topics ranged from macro plays (Kondratieff winter) and fund flows (Richard D. Wyckoff’s influence on technical analysis) to using global markets to hedge against home bias, and the success of momentum-value combined strategies.
I’ve dubbed the model portfolio RentierCap as it benefits from the FIRE sector. The model portfolio aims to accumulate wealth over a longer period of time than a catalyst-based intraday strategy. It uses Smart Beta and Exchange Traded Funds. I’ve selected BlackRock ETFs (in a nod to the Adam Curtis documentary HyperNormalisation), although State Street SPDRs and Vanguard ETFs could be used, and may have lower expense ratios and management fees.
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 phaseactivist 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.
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.
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.