Computer-based models to replace the investment advisor?
Man versus machine
Analysis A number of recently published features and surveys evidence the continued growth in quantitative investment.
Quantitative investment is based on the deployment of computer generated investment decisions. It is reputed to be growing at 20 per cent per year. Major conventional fund managers as well as hedge funds are increasingly deploying computer-based models to make investment decisions rather than relying on human judgement - or is that a myth?
Quantitative investment managers use a model to identify sets of characteristics for their investments. Computing power is now relatively cheap. Obviously, computing power can access data almost instantaneously and simultaneously. Asset classes and financial instruments within those asset classes can then be screened and investments are selected. They reflect the manager's views.
These models normally determine the investment decisions and so replace the traditional portfolio manager's role. It is contended that this approach eliminates human emotion and personal bias, which can impede effective portfolio management.
More importantly, the models provide insight into market inefficiencies to be applied rapidly across asset classes and the vast number of financial instruments within those asset classes. Whole markets can be analysed daily for buy and sell indications at an individual instrument level. This enables portfolios to contain a larger number of instruments and reduce risk through greater diversification of the portfolio.
Computers have not taken over the investment process. Human qualities are required to set the criteria and parameters for data collection and analysis. Here the investment stars still have their place - a creative and efficient human portfolio manager to extract the value from quantitative data.
It is a complete myth that quantitative investment managers are nerdish boffins working under the direction of an all-powerful computer model. It is equally a myth, perpetuated by some technology providers, that there is reliance on a "black-box" stereotype. Dependency on programming and computer nerds has not replaced the dependency on star investment managers.
Quantitative investment managers distinguish themselves by their processes, "which combine people and technology in a framework that rigorously assesses cost, risk and return, which sets them apart from less successful managers".
Trading is a function, where it would be possible and highly irresponsible to construct/build a completely automated quantitative process. Trading is not a mechanical process. Trading is influenced by relationships, trust (and the lack of it!) and gamesmanship. Equally, trading relies on much more than the application of intuition or "gut feeling". The key role of traders is to provide the balance between pure judgement and systematic solutions.
For example, a principal programme trading, where a broker quotes a price to guarantee completion for a list of trades and executes those orders at current market prices, will encompass a model of expected cost for assessing competing quotes from brokers. Yet, the traders' selection of brokers best suited for each trade is critical to successful execution.
Some quantitative fund managers let the data set the direction by, for example, using computers to identify patterns and relationships in asset classes and instrument prices. Others are sceptical of investment ideas that originate in and from "black-box". By working with theoretically sound investment themes, they should be identified, rationalised and justified by people for performance to be stable and continuous into the future
Quantitative portfolio construction is about taking forecasts of asset class and individual instruments and the returns on them, models of portfolio risk and estimates of transaction costs and, finally, reconciliation of optimal trade-off between these components. Importantly, it is accompanied by a high degree of management and oversight.
The portfolio manager should vet the information going into the creation of forecast returns. As with other forms of data entry "rubbish in, rubbish out" is true. Portfolio managers, for example, have check major changes in earnings forecasts and examine extreme high and low valuations to make sure they truly reflect expected earnings, and are not some form of data error. Longer-term economic outlooks and less substantive issues, which may contribute to the performance, have to be mapped against the data, as do extraordinary events such as 9/11 or the recent terror alerts in the UK.
Quantitative management focuses on collection and analysis of historical data. In consequence it is alleged to have a selection tendency towards so-called "value-based" investment selections with a strong bias to selection of companies with strong cash flow and solid tangible assets. This ignores or places a lower rating on matters such as quality of management, products and services, and innovation not yet translated into cash flow such as intellectual property, for example brands and patents.
Successful quantitative managers must be innovative, "seeking to extract the best from man and machine" to produce a quantitative investment process that is fast, accurate and exceptional in execution. The process has to deliver consistent performance both of markets and competitors in the asset classes with an ability to adapt. In a fiercely competitive environment of financial markets, this is a continual and evolving challenge. An active quantitative investment approach must blend people's insight and creativity with the efficiency and speed that technology can supply.
Investment performance is ultimately dependent on the quality, innovation and insight of research. Quantitative managers should be interested in all opportunities to outperform the benchmark return. Ideas, which evolve and contribute to this achievement, will not come from a machine but from people.
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