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Behavioural finance is very clear about investor biases and the suboptimal financial outcomes that result. At least three methods exist to help mitigate behavioural biases when investing: education, investment process and goal-based investing. Lack of education is a cognitive bias and unlike emotional biases, cognitive biases are easy to mitigate by making investors more self-aware of it. By understanding how to control for behavioural influences, and the degree of control investors have over cognitive and emotional responses, investment advisors work together with their clients to achieve rational investment outcomes.

A formal investment process mitigates behavioural influences. Such practice has to be objectively designed before making any investment decisions that may be influenced by behavioural biases. A sound process is structured to eliminate or reduce the potential impact of biases.

An investment process is recommended to –

• Identify investment objectives.

• Optimise a portfolio to fund specific investment outcomes in consideration of long-term capital market expectations.

• Fulfil the asset allocation with passive, smart beta or systematic solutions (selectively active) while being cognisant of expenses and tax efficiency.

• Stay the course as prescribed in the investment process.

Goal Based Investing (GBI) has quickly become a new paradigm in modern portfolio management. It mitigates behavioural biases while also producing an optimal asset allocation that funds lifetime aspirations. It primarily exploits the mental accounting bias to produce a dynamic asset allocation customised according to a threshold target and risk preference.

By framing the investment problem around risk preference to drive portfolio selection, GBI mitigates the loss-aversion bias. Changing the focus from short-term return ambitions to a framework based on accomplishing investment outcomes, GBI provides a construct for more informed decision making, and leads toward more rational investing and outcomes.

Mental Accounting (MA) theory becomes an outcome based portfolio problem. It is a portfolio theory that combines traditional portfolio theory from Markowitz and the Behavioural Portfolio Theory of Shefrin and Statman.

Through economic interpretation, one can consider that the Mean-Variance (MV) theory is a production theory in that investors produce a range of portfolios based on a combination of expected returns and risk (measured through variance or standard deviation), and selects what is best for them. The portfolios they select are said to be efficient. Each MV investor thus faces a production function in the mean-variance space and chooses to consume from the set of efficient portfolios produced that maximise their utility i.e. seek a portfolio that fulfils a need or want.

Ultimately investors require that their portfolios satisfy outcomes over the investment horizon i.e. have enough money to retire on, fund their children’s education requirements or grow sufficient wealth to satisfy a spending need. It is prevalent that investors have different attitudes towards risk for different requirements. This is precisely the MA theory behind investor behaviour. Investors do not consider their portfolios as a whole. Instead, their total portfolio is composed of many sub-portfolios or mental accounts where each sub-portfolio is associated with a goal or target. Each of these goals are selected based on a probability of achieving that goal.

Traditionally, the notion of risk is associated with the standard deviation of investment return. However, this definition of risk is not well understood by investors. Framing the definition of risk differently ideally should dispel any ambiguity between investor and advisor, and determine the preference for taking risk more clearly. An investor typically will associate risk as the likelihood of not achieving their aspirations. Consider for a moment the notion of risk as defined by the standard deviation of the portfolio return. Decreasing ‘risk’ in an underfunded portfolio only increases the likelihood of not achieving the desired outcome.

The Behavioural Portfolio Theory (BPT) of (Shefrin & Statman, 2000) suggests that investors care about the expected return of each sub-portfolio but instead of measuring risk using the standard deviation of expected returns, they use the probability of failing to reach the threshold level of their mental account as the measure of risk. Each account faces the efficient frontier or set of mean-variance optimal portfolios and by trading off between expected return and the probability of failing to reach a threshold level associated with their goal, an optimal portfolio is selected. The benefits the MA framework are that objectives and risk preferences are framed differently and resonates better with an investor in terms of what is expected from their portfolios. This also does away with measuring portfolio performance against standardised benchmarks as the objective is quite clear - the goal is the benchmark.

Goal Based Investing is undoubtedly a paradigm shift for investors in modern portfolio management. It paves the way towards democratising the investment process through transparency and promotes an understanding of individual preferences and long-term ambitions between investor and investment advisor.

The GBI framework mitigates behavioural biases while also producing an optimal asset allocation that funds lifetime aspirations. It primarily exploits the mental accounting bias to produce a dynamic asset allocation customised according to a threshold target and risk preference. The probability of reaching or failing to reach an investment target is integral and should be considered as the modern standard for risk measurement in behavioural portfolios.

What does this mean for contemporary portfolio management? There is a basis for refining the portfolio choice problem initially designed for rational investors. By recognising that behavioural biases play a significant role in determining investment outcomes, GBI provides a framework for investors to express behavioural elements through an intuitive language of probability.

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