19th June 2013: The SmallCap Value Premia

Richard C. Marston‘s Portfolio Design: A Modern Approach to Asset Allocation (Hoboken, NJ: John Wiley & Sons, 2011) is my work commute read for this week. Wiley has excerpts here.

 

Portfolio Design focuses on return drivers and risk premia for the major asset classes used to allocate assets in diversified portfolios. The ‘modern approach’ includes a focus on data analytics and the available evidence base; a review of recent (Fama-French-influenced) quantitative finance studies; and insights on index benchmarks and portfolio construction.

 

I’m a couple of chapters in: Marston focuses on smallcap and value return drivers, in contrast to largecap and growth stocks. Although he doesn’t explore this Marston’s insights in his opening chapters are also relevant to two other areas:

 

  • Penny stick promoters rely on ‘crowded trades’ and rational herding: this is often what the ‘free’ investment newsletters and watchlists are for, to create drawdown market dynamics in relatively illiquid stocks.
  • Venture capital’s asymmetric returns can have large payoffs if opportunity evaluation screening works, and if stage one financing backs a break-out company that creates a new, large market or is a successful disruptive entrant into existing markets.

 

It’s interesting to see how an experienced, endowment investment adviser handles index benchmarks, portfolio construction, and specific return drivers. Since he is also on Yale’s investment committee Portfolio Design also makes an interesting counterpart to the influential Yale Swensen model detailed in Swensen’s Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment (rev ed.) (New York: The Free Press, 2009).

Wikinvest

Parker Conrad and Michael Sha launched Wikinvest in 2006 to gather user-generated security analysis. The project collates wiki profiles on investment concepts, fundamental analysis of companies and technical analysis of market price movements. It also appeals to MBA students with sections on personal investing, investment concepts and funds management. Conrad and Sha have graduated from Harvard dorm day traders to Web 2.0 knowledge entrepreneurs.

Claire Cain Miller’s New York Times profile makes the obligatory link with Wikipedia, the online encyclopedia. Conrad and Sha go into some detail of their verification process for data and public sources. Actually, the wiki has some specific applications for the pooling or crowdsourcing of investor insights. Sell-side analysts in the research departments of investment banks can have dual allegiances if the underwriting departments incentivise their research products to drive sales revenues. The best will gravitate to portfolio managers, dynamic asset allocation and hedge funds that use event/risk arbitrage and short-sell strategies. An investor wiki could provide a counterbalance to these influences through a broader snapshot of investor sentiment, and strategies to delimit analyst biases and groupthink. A side-effect however is that investor views are more likely to converge to a mean, and the market efficiencies may thwart value investing strategies that require information asymmetries.

In fact, the Wikipedia analogy has some limitations because analysts, traders and portfolio managers all structure and use market information in different ways to online encyclopedias. This was one of wiki creator Ward Cunningham‘s insights when he devised the Portland Patterns Repository in 1995: the value of a repository to capture domain knowledge and processes, and to codify them from tacit to explicit form using a methodology such as design patterns or object oriented programming structures. If it stays within Wikimedia’s online encyclopedia model then Wikinvest will be suited to fundamental analysis and introductory investing topics. However, it could evolve into a different form if it adopts insights from behavioural finance and tactical asset allocation into the wiki process. These areas augment Cunningham’s original schema with strategies to deal explicitly with how information quality and source selection can affect investor decisions, judgment and verification. Even these vary depending on the end-user, their self-awareness, the intended contexts of use, and what potential outcomes may occur (a normative stance on the superiority of user-generated content over ‘traditional’ media is not sufficient alone to address the concerns that these processes are meant to anticipate and solve). The pressure to change and evolve may come from sell-side brokerages which now use Wikinvest as a cost-efficient data source for market commentaries. Alternatively, it may come from Wikinvest’s end-users as the wiki gains more public prominence, and attracts a range of investor styles with knowledge of asset classes, inter-market volatilities and global dynamics. If this occurs then Wikinvest and other wikis could have a pivotal role in the democratisation of finance beyond London, New York and Chicago.

Just don’t be surprised if Icahn Reports maven Carl Icahn (video) launches a wiki raid.

Errors In Quantitative Models & Forecasting

Could the roots of the 2007 subprime crisis in collateralised debt obligations (CDOs) and residential mortgage-backed securities (RMBS) lie in financial analysts who all used similar assumptions and forecasts in their quantitative models?

Barron’s Bill Alpert argues so
, pointing to a shift of investment styles after the 2000 dotcom crash from sector-specific, momentum and growth stocks to value investing.  Investment managers who prefer the value approach then constructed their portfolios with ‘stocks that were cheap relative to their book value.’  In other words, the value investors exploited several factors — the gaps in asset valuation, asymmetries in public and private information sources, price discovery mechanisms and market participants — which contributed to mispriced stocks compared to their true value.

However, the value investing strategy had a blindspot: many of the stocks selected for investment portfolios also had a high exposure to credit and default risk.  The 2007 subprime crisis exposed this blindspot, which adversely affected value investors whose portfolios had stocks with a high degree of positive covariance.

Alpert quotes hedge fund manager Rick Bookstaber who believes that financial engineers have accelerated crises and systemic risks via the complex dynamics of new futures contracts, exotic options and swaps.  These new financial instruments create interlocking markets (capital, commodities, debt, equity, treasuries) which have the second-order effects of larger yield curve spreads and trading volatility.  Alpert and Bookstaber’s views echo Susan Strange‘s warnings a decade ago of ‘casino capitalism’  and ‘mad money’ as unconstrained forces in the international political economy.

Quantitative models also failed to foresee the 2007 subprime crisis due to excessive leverage, difficulties to achieve ‘alpha’ or above-market returns in market volatility, and the separation of risk management from the modelling process and testing.  Other commentators have raised the first two errors, which have led to changes in portfolio construction and market monitoring.  Nassim Nicholas Taleb has built a second career on the third error, with his Black Swan conjecture of high-impact events, randomness and uncertainty (see Taleb’s Long Now Foundation lecture The Future Has Always Been Crazier Than We Thought).

Alpert hints that these three errors may lead to several outcomes: (1) a new ‘arms race’ between investment managers to find the new ‘factors’ in order to construct resilient investment portfolios; (2) the integration of Taleb’s second-order creative thinking and risk management in the construction of financial models, in new companies and markets such as George Friedman’s risk boutique Stratfor; and (3) a new ‘best of breed’ manager who can make investment decisions in a global and macroeconomic environment of correlated and integrated financial markets.