Investment strategy – lessons from the chess Grandmasters

May 26, 2016 Categories: Investment, Investment Strategy, Markets

On May 11th 1997, chess genius Garry Kasparov had a bad day. His defeat by IBM’s Deep Blue computer programme was the first time that a reigning world champion had lost to artificial intelligence, and was widely seen as the watershed moment when man became an also-ran in the evolutionary race with machines. But what followed was transformational for the world of chess, for mankind – and for investment strategy.

After this spectacular match, and many other matches against computers, Garry Kasparov had the idea to invent a new form of chess in which humans and computers co-operate, instead of contending with each other. Kasparov named this form of chess ‘Advanced Chess’. In 2005 a variation or superset of Advanced Chess emerged – ‘freestyle chess’, where consultation teams are also allowed. Each innovation provided new insights into how the human + machine combination could reduce the risk of errors, increase the level of achievement to new heights, and provide greater transparency into the thought-processes behind effective decision-making.

In our strategist team, we face the same issues. Humans bring experience, strategic insight and intuition to the investment process. Machines can process huge amounts of data quickly and entirely free of prejudice or doctrine. Hence in our investment process, which focuses on cycle, valuation and sentiment factors (CVS), the qualitative discussion gets a lot of attention, but the quantitative elements are equally important.

Quantitative models have a number of benefits:

  • Models help us understand the sensitivity of financial variables to different factors. For example, the average time to mean reversion in currencies or the sensitivity of equity markets to a range of macroeconomic indicators. Models let us undertake scenario analysis and allow us to look at the distribution of potential outcomes.
  • Models summarise data into signals that can be easily understood.
  • Models can forecast the macro indicators (business cycle, jobs, CPI, central bank policy target rates) that matter for our investment decisions.

We should, however, be careful of relying too much on quant models.

  • Models of financial variables (such as Treasury yields and exchange rates) generally have poor forecasting ability and do not predict turning points.
  • Models that have forecasting ability will generally be complex and will not be trusted by portfolio managers without an intuitive understanding of how they work (nobody trusts a ‘black-box’).

Quantitative modelling acts as an anchor point for our views, helps us to overcome behavioural biases, forecasts predictable macro indicators, and organises large amounts of data into a form that can be integrated into a structured decision-making process. In our strategist team, we construct our narrative around the building blocks of cycle, valuation and sentiment. Quantitative modelling contributes to these building blocks. It also runs alongside our CVS process as a check on the qualitative decisions. Properly done, a melding of qualitative and quantitative methods may yield better results than either on their own.

In our process, we aim to be directionally correct without aiming for spurious precision – ‘better roughly right than precisely wrong’. Markets might be driven by fundamentals and mean reversion over the long term, but in the short-term, they are subject to cycles of fear and greed, and by reactions to information that may contain more noise than signal. Navigating those cycles and screening out the noise ultimately requires skilled human judgement.

After all, as grandmaster Viswanathan Anand remarked after winning Advanced Chess tournaments in three consecutive years (and then losing the fourth in the final)

‘You can do a lot of things with the computer but you still have to play good chess.’

Andrew Pease, Global Head of Investment Strategy

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