Bernd Scherer

A New Look at Minimum Variance Investing

Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular rule driven, i.e. new passive concepts. I show in this paper theoretically and empirically that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk-adjusted outperformance (alpha). This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition.

Pooling Trades in a Quantitative Investment Process

Trading for several clients or accounts at the same time involves two challenges, one technical and one philosophical. The technical challenge is to take account of the market impact of aggregate trading in forming each clients individual portfolio. The philosophical challenge is to replace the traditional goal of maximizing utility for a single client with the goal of ensuring that all clients are treated fairly in the pursuit of their collective good. These challenges require a model of an efficient market for liquidity, which allows multi-account optimization—to ensure fairness, based on the principle that efficient markets are fair.

Individualization of Robo-Advice

Robo-advisors assign risky portfolios to individual investors using web-based investment algorithms with minimum human interaction. We provide insights into the working and current state of individualization of this new type of fintech company. Rather than singling out individual firms, our approach is use questions typically asked by robo-advisors to define a generic (average) robo-advisor as a benchmark model that we suggest can be improved upon in various dimensions. Given the missing human advisor, we believe the ability to individualize will be a distinguishing feature among robo-advisors. Our discussion aims at understanding the current state of personalization and helping users of robo-advice to better evaluate the services provided.

Fairness in Trading: A Microeconomic Interpretation

This article shows that nonlinear transaction costs generate external effects between accounts due to trade-volume-dependent marginal transaction costs. For an asset manager with multiple clients, this raises the question of fairness: How do I ensure that all clients are treated fairly? In general, two possible solutions exist. The first is the so-called COURNOT/NASH solution, where each account is optimized under the assumption that trading in the remaining accounts is given. However, in a COURNOT/NASH equilibrium, each client pays the average costs of trading but creates higher marginal costs (under the assumption of nonlinear transaction costs) on the “community” of accounts. Ignoring this interdependence will hurt performance in all accounts. The authors model optimal trading with mean variance preferences as a duopoly game. This allows the use of well-developed microeconomic tools for analyzing the optimal trading problem and linking it with the literature on external effects and their solution, i.e., the COASE theorem.

Asset Allocation with Shadow Assets

The wealth of most investors contains both financial assets as well as nonfinancial assets. The author defines shadow assets as (mostly) nonfinancial and nontradable assets that are exogenous to the investor’s asset allocation decision, such as human capital, nonfinancial sovereign assets (e.g., underground oil reserves), the present value of future alumni contributions for university endowments, or the nonlisted family business for the client of a family office. Ignoring shadow assets is unfortunate, as it is the existence and nature of shadow assets that distinguishes private investors, university endowments, sovereign wealth funds, and family offices and hence leads to different demands for risky securities. Shadow assets influence the outcome of asset allocation decisions via their covariance with financial assets and their effect on total wealth. Adding shadow assets has two effects on investors: It makes investors more aggressive and can create demand for hedges. Investment advice in the client’s best interests needs to incorporate shadow assets as well as shadow liabilities, as optimal allocations differ considerably when shadow components are properly accounted for

Invited Editorial Comment: Frictional Costs of Diversification

1. Bernd Scherer 1. is chief investment officer at FTC Capital Vienna in Vienna, Austria. (scherer{at}ftc.at) With the establishment of modern portfolio theory, researchers started to test how well observed portfolios reflect the theory’s advice on normative diversification. Most academic

Practical Applications of Individualization of Robo-Advice

Practical Applications Summary Automated asset management advisory firms, often called robo-advisors, assign portfolios to individual investors based on investment algorithms. These algorithms use investor characteristics such as age, net income, and assessments of individual risk aversion to recommend suitable asset allocations. Client interaction and delivery of portfolio advice are web-based and without human interaction. Robo-advice disintermediates the classical distribution model, which is now widely recognized as expensive, difficult to scale, and dependent on the individual advisor’s skill level. As a result, those with lower levels of wealth who do not meet minimum account limits are left stranded with little access to financial planning advice. This is where the robo-advisor comes in. In Individualization of Robo-Advice, published in the Summer 2017 issue of The Journal of Wealth Management, Michael Faloon and Bernd Scherer discuss the pros and cons of robo-advisors, highlighting their lack of sophistication in asset allocation based on investors’ individual needs.

Frictional Diversification Costs: Evidence from a Panel of Fund of Hedge Fund Holdings

We analyze the diversification choices of fund of hedge fund managers. Diversification is not a free lunch. It is not available for every fund of fund. Instead we find a positive log-linear relation between the number of constituent funds in a fund of hedge fund (n) and the respective assets under management, (AuM ). More precisely it takes the form: n^2 proportional to AuM. This relation is consistent with the predictions from a model of naïve diversification (1/n) with frictional diversification costs such as due diligence costs. Finally, we demonstrate that individual FoFs diversifying more in line with our model’s predictions deliver superior performance and fail less likely.

Buy-Side Risk Management: Hedging HedgeFund Outflows

In an ideal world, hedging the risk of fund outflows would simply involve the purchase of state-dependent securities that pay a nominal amount if outflows occur and nothing in other states of the world. However, securities of this type are not contractable, given that fund outflows might be performance related or otherwise self-induced. Alternatively, the risk of fund outflows can be (imperfectly) hedged with out-of-the-money digital calls, which pay off one monetary unit if volatility (represented by the VIX) increases by more than a pre-specified amount. The rationale is that strongly increasing risk often leads to both deteriorating hedge fund performance as well as increased client risk aversion. Both factors are likely to trigger hedge fund redemptions, are outside the manager’s control, and represent an exogenous event.The article develops a model to derive the optimal hedging demand and applies it to aggregated hedge fund data.