Portfolio Management

Portfolio Management

Stanford Brown manages in excess of $1bn on behalf of our clients. We have a rigorous and clearly defined investment philosophy built from over three decades of experience.

Investment philosophies and processes

Stanford Brown’s portfolio services are delivered within the framework of a clearly defined, highly coherent, investment philosophy that is based on how markets work in the real world, rather than a dogmatic adherence to financial theory. The cornerstone of which is that investment success is about achieving investors’ goals.

Stanford Brown’s investment philosophy is based on an assessment of hard data over multi decade periods – generally at least 50 years in length. The evidence in support of our beliefs is extensive and we have prepared a variety of presentations and papers in this regard.

Stanford Brown’s key investment beliefs include the following:

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Overwhelmingly, the single largest failing we see in the design and construction of long term investment portfolios is the failure to properly understand and consider risk. Lunar’s role is to provide insight to our clients on these risks, to manage portfolio risk over time, and to articulate the risks so that investors can make informed decisions about which portfolio is most likely to achieve their long term goals within their own risk tolerance.

Traditional measures of risk are really just measures of volatility, and they are fatally flawed. Risk is much more than volatility, and the theoretical “Standard Deviation” is itself a very flawed measure of volatility. (Traditional finance theory assumes that the ‘standard deviation’ is the single number that comprehensively measures volatility, and that volatility is ‘risk’).

Our portfolios are designed to achieve investors’ specific financial objectives within their defined risk limitations. In addition to the traditional risk measures like ‘standard deviation’, we define, measure and manage investment risk in portfolios in terms of real-world risks for investors, including:

  • Risk of failing to meet a specific investor goal.
  • Risk of running out of money.
  • Risk failing to keep pace with their expense inflation (which is often not CPI inflation).
  • Risk of unacceptably deep and/or long drawdowns.
  • Risk of going backwards in real or nominal terms.
  • Risk that deviations from the path toward the goal will cause the investor to abandon the strategy.e strategy.

Traditional measures of risk assume markets are random and normally distributed, and they assume volatility is static, stable and constant over time. In the real world, markets are none of these things, whether the measurement period is in micro-seconds or decades or anything in between. There are numerous examples of extreme market movements (from the 1987 crash to the recent ‘flash crashes’ that make a mockery of theoretical academic assumptions of about ‘normal’ statistical distributions’).

Models that are based on random, normally distributed returns – for example the current fad of using ‘stochastic’ or ‘monte carlo’ (i.e. random) distribution models are deeply flawed in that they severely under-estimate real world risks. Instead of traditional text-book volatility measures we use a variety of other real-world volatility measures, including asymmetric, non-linear and non-parametric measures. In formulating portfolio outlooks and estimating portfolio risks we use the most conservative of these measures (and the most conservative is almost never the traditional ‘standard deviation’ measure).

The ‘efficient markets’ family of theories underpin many professional portfolio management processes around the world. As elegant as these theories are they simply do not accord with what happens in the real world. Asset allocation and security selection must be undertaken with access to robust assessment of valuations and return outlooks and these valuations and outlooks need to be regularly updated and acted upon. The notion that if you buy assets with a long term perspective and hold them long enough you will do well is deeply flawed.

Traditional portfolio management and corporate finance theories are based on the hypothetical existence of a ‘risk-free’ asset. In the real world we know that there is no such thing as a ‘risk-free’ asset. Government bonds (even if they don’t default), cash, and even gold, all can and do suffer deeper drawdowns in real total returns and take longer to recover their value in real terms than many so-called ‘risky’ assets. For example, government bonds of AAA rated countries, can and have historically, generated in real total return losses for several decades at a time recovering. So-called ‘risk-free’ government bonds have historically had deeper real total return drawdowns and take longer to recover than so-called ‘risky’ assets like shares. The same is true of other so-called safe-haven’ assets like cash and gold.

The traditional theory-based approach of: ‘buy & hold’, ‘set & forget’, passively rebalancing back to a static asset allocation and hoping markets ‘come good’ – does not work in the real world.

Every asset class in the world can and does go backwards in real terms for decades at a time before recovering, and every mix of asset classes in the world can and does go backwards in real terms for decades at a time before recovering.

Utilising a dynamic asset allocation approach allows us to shift monies in a meaningful way between asset classes in order to take advantage of the return outlooks for each asset class.

Most assessment of investments, and most performance measurement of investments, takes place on a pre-fees and taxes basis. This can easily distort investment thinking. We evaluate investments and active asset allocation decisions after consideration of the fee and tax impacts of their structure and approach.

Experience tells us that opportunities for unusual gains are usually accompanied by higher risk. We are fortunate to have access to institutional and unique investment opportunities; however, we see these opportunities as an optional complement to a core multi-asset class portfolio, and not a replacement for it. Within our portfolios we are constantly vigilant and will make substantial adjustments to portfolios as relative market and security valuations warrant, but we do not employ high turnover strategies in an endeavour to generate gains from high volume trading.

Enormous amounts of wealth are destroyed around the world every year through panic selling in booms (often using gearing at the top of the market), buying into fads, and panic selling in busts. Our process is based on hard facts using many decades of data (much of which is not available elsewhere) and is designed to avoid these pitfalls via strong market and security valuation disciplines and rigorous security valuation frameworks applied by experienced personnel.

Investment philosophies and processes

    Asset allocation in almost all other multi-sector portfolios in the market are run on one of three strategies:
  1. Regular rebalancing back to a static asset allocation; or
  2. Dynamic asset allocation based on fundamental valuation metrics (e.g. Shiller CAPE ratios); or
  3. Dynamic asset allocation based on short term indicators (e.g. volatility, momentum)
Our multi-factor multi-time-frame approach overcomes the flaws with the three strategies employed in the market. These flaws are:
Regular rebalancing back to a static asset allocation can lead to portfolios falling off the path to reach investors’ goals for years or even very long periods of a decade or more because every static mix of assets and asset classes can and does go backwards in real terms for decades at a time (including – and especially – portfolios dominated by so-called ‘safe’ assets like government bonds and/or cash). Most investors cannot tolerate under-performance (relative to the portfolio benchmark or peer funds, or relative to their financial goals) for more than a year or so before giving up and switching funds or abandoning the goal altogether.
Dynamic asset allocation based on fundamental valuation metrics (e.g. Shiller CAPE ratios) can often lead to under-performance for several years at a time, because markets can remain over-priced or under-priced for many years. Also, basing asset allocation decision on long term valuation measures can lead to over-weighting in early-mid downturns, right before severe sell-offs – for example in mid-late 2008 when stock markets around the world became “cheap” on fundamental measures, luring investors back into equities and exacerbating their losses in the worst parts of market sell-offs;
Dynamic asset allocation based on short term indicators (e.g. volatility, momentum) generally lead to excessive trading, high turnover, high transaction costs and high tax drag. They also usually miss the big swings in markets because they focus on short term moves. Popular dynamic AA models based on market volatility are also flawed because volatility only ‘clusters’ or trends for around 10-15 days in almost all cases, which is much too short a period for DAA in long term portfolios, and it is usually arbitraged away by nimble hedge funds and market makers anyway. By the time the volatility signals are evident the market has already fallen (thus causing the measured volatility to show up in the first place).

Other private wealth division services:


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