Advantages of Systematic Investing

Aspect Capital Insight Series


Writing in 1923 about the famous discretionary speculator Jesse Livermore, the American author Edwin Lefèvre captured in a quote the frailties of human psychology when it comes to investing in financial markets: “It is inseparable from human nature to hope and to fear. In speculation when the market goes against you – you hope that every day will be the last day – and you lose more than you should had you not listened to hope… And when the market goes your way you become fearful that the next day will take away your profit, and you get out – too soon. Fear keeps you from making as much money as you ought to.”[1]

In this paper, we review the key advantages of the systematic approach to investing. In addition to the avoidance of investment error due to psychological bias, a systematic approach offers several key benefits including: the scalability to invest with a consistent approach twenty four hours per day across a global portfolio of securities; the implementation of consistent risk management at security, asset class and portfolio level; and, the scientific rigor which can be devoted to the continuous development of the core investing approach. We also review academic evidence comparing the performance of systematic and discretionary CTAs and hedge funds.

1. Human Psychology
Up until the dotcom bubble academic finance was slow to accept the link between investor psychology and financial market behaviour. However, the field of behavioural finance is now well established, identifying two main areas of relevance to a discussion of systematic investing. First, there is now a considerable body of theoretical and empirical evidence identifying how difficult it is for discretionary investors to make consistently reliable forecasts and decisions about risk allocation. Second, there is now recognition of the effect these psychological biases have on financial markets and the consequent opportunities for systematic investors.

1.1. Discretionary trading and human psychology
In 1979 two psychologists, Daniel Kahneman and Amos Tversky, published a ground breaking paper on decision making under uncertainty [2]. The authors describe a theory supported by experimental evidence, which predicts (irrational) attitudes to risk aversion and risk seeking, when individuals are making decisions with no certainty about the outcome.

The principle decision making error by individuals is that they avoid crystallising small losses (risk seeking when it comes to small losses) while at the same time realise profits too soon (risk averse when it comes to profits). Though the focus of the two psychologists’ work was not financial markets, this is a feature of investing behaviour that was recognised at least as far back as Jesse Livermore, in the quote above.

Interestingly, there is also a further element of Kahneman and Tversky’s theory which gets less attention. People are risk seeking when it comes to the opportunity to make very large profits (the large demand for lotteries being a classic example) and generally avoid the small probability of very large losses (evidenced by the large demand for insurance products).

In related research Kahneman, Tversky and others have documented several additional errors that individuals tend to make around decisions, due to psychological biases. For example, generally individuals will rely on relatively little historical information to make predictions with high confidence (the law of small numbers), will tend to be overconfident about their own forecasting abilities and are generally overly optimistic. This is particularly acute when it comes to estimating the probability of future events occurring [3].

Subsequently the scientific literature has evolved to incorporate a deeper understanding of how financial market participant biases affect decision making. These biases are driven by additional factors which include cultural issues and extreme life events, some of which are documented in Fig.1.

There is strong evidence that individual investors like to buy stocks with lottery-like characteristics. This phenomenon is particularly prevalent in areas where there is high lottery participation, and appears to be driven by cultural issues. Factors which induce greater expenditure in lotteries also induce greater investment in lottery-type stocks. Poor, young men who live in urban, Republican dominated regions and belong to specific minority and religious groups invest more in lottery type stocks [4].

Extreme life events such as marriage and divorce have been shown to affect fund manager performance. In the years surrounding both marriage and divorce investors exhibit lower realized returns, their stock selection skills are poorer, and their risk adjusted returns deteriorate, and are weaker compared to control samples. This also holds for hedge fund managers [5].

Recent evidence by researchers at the University of Miami documents investors tending to favour funds managed by managers from a similar cultural background to themselves. Their evidence shows that US mutual funds managed by individuals with foreign sounding names experience 10% per annum lower inflows than otherwise identical funds [6].

All of the above evidence points to the human frailties which make discretionary investing so difficult to pursue successfully over a long period in a consistent fashion.

1.2. How human behaviour generates opportunities for systematic traders
There is also growing scientific evidence demonstrating how the human weaknesses discussed in the preceding section create opportunities in financial markets which can be systematically exploited. The most recognised example is how behavioural models link what we know about investor overconfidence and changes in risk aversion to initial under-reaction and then over-reaction to new information and consequently cause momentum (trends) in asset prices [7] [8].

A classic example of how this can manifest itself occurred in commodity markets in 2014. With oil trading at over $100 per barrel analysts were predicting oil prices would average $105 during 20141. Despite emerging evidence of excess oil supply, by the middle of the year investors’ focus was the growing violence in Iraq and the upward effect it would have on oil prices2. A month later, a down trend in the price of oil had emerged and Aspect Capital’s systematic trend following model identified this and established a short position, which grew steadily as the trend continued. Eventually analysts lowered their forecasts and discretionary investors aggressively liquidated long positions purchased at elevated levels. When the market was dropping fastest in November, Aspect’s models began buying back and providing liquidity.

After a brief recovery in mid-2015, the downtrend continued and finally ended in January 2016 at which point Aspect’s trend following models held a small residual position in oil. Interestingly, by this point discretionary analysts were continuing to lower their forecasts for oil prices for 2016 with some forecasting prices under $20 per barrel3.

By maintaining the discipline of operating systematically, the trend following strategy in this example managed both to avoid human behavioural biases, and also to exploit the trends created by this behaviour. This example emphasised how effective the simple philosophy of running profits and cutting losing positions can be, but how difficult it is in practice for discretionary investors to achieve.

2. Implementation/Scalability of Systematic Investment
A systematic investment approach offers key additional benefits in terms of implementation, particularly when operated at scale. Large systematic managers such as Aspect are able to trade globally and operate around the clock. A typical system in the futures space can trade well over 100 assets on multiple exchanges around the world, and a systematic equity trading strategy may monitor and trade in several thousand stocks. Because all of the decision making and investment processes are automated, processing of new information, risk monitoring, signal generation and trade execution can be implemented continuously.

Fig.3 provides a snapshot of the current trading hours for the main products on a variety of exchanges typically traded by systematic managers. These begin with Asian markets which will trade from around midnight to approximately 06.00 GMT. European markets then open at approximately 08.00 GMT, before North American markets open at 14.00 GMT and typically trade until 21.30 GMT. At the same time a lot of trade in foreign currencies is over the counter (OTC) and these markets trade with good liquidity twenty four hours per day.

There is a real challenge for a manager who is not systematic to trade this number of markets with a consistent approach. Even monitoring this number of markets is almost impossible without a very large number of discretionary traders. Obviously, adding additional discretionary traders creates the challenge of consistent implementation of the core strategy.

This scalability of both markets traded and strategies which can be utilised increases the potential diversification in a systematically operated portfolio. And this in turn means that more potential trading strategies become worthwhile pursuing: systematic traders can focus on marginal effects in the markets which may only have a small expected benefit. A strategy with a small edge might not be attractive or practical to operate in a manual or discretionary manner, but might yield far more compelling results when spread over many markets and traded repeatedly.

3. Risk Management
The implementation of a systematic investment approach also allows for major innovation in disciplined risk management, which can be built into the strategy rather than applied as an afterthought. Typically a systematic manager will operate a targeted level of portfolio risk, while some managers will allow the risk level to vary within a target range.

Statistical techniques allow for relatively accurate forecasting of short term volatility. This means systematic managers are able to ensure realised asset level risk lies within a relatively narrow band. Augmenting this with correlation forecasts allows the portfolio to be constructed using rigorous statistical methods.

Typically a systematic manager will begin by generating risk forecasts for the different markets in the portfolio and estimating the correlation between different assets. The portfolio is constructed by combining these estimates and the signals generated by the trading models.

In addition to relying on scientific methods to target volatility, the systematic approach allows hard limits to be set and monitored to control risks and exposures. For example these could include hard limits for volatility, Value at Risk and leverage at the asset, asset class and portfolio level. Once these limits are reached position sizes can be automatically capped or reduced.

FIg.4 provides an overview of some of the typical risk management controls that can be implemented in a systematic portfolio and the effect of those tools on portfolio implementation.

4. Scientific Approach
Aspect’s investment philosophy has remained consistent since the company’s inception and focuses on a scientific approach to investment driven by our belief that market prices are not random but display persistent, statistically measurable and predictable behaviour and idiosyncrasies.

Of the 134* employees currently working at Aspect Capital, over 81* work in Research and Development. The aim of our research is to reject or validate statistically testable investment ideas. The research group define in advance the expectations for an investment hypothesis, which must have a theoretical basis. Testing is then conducted, referring back to these expectations. To avoid overfitting our researchers avoid optimisation, instead using a range of models and parameters for each test. In our advanced research environment it is possible to model trading costs, including commissions, trade slippage and bid-ask spreads based upon historical data for each market.

Aspect has an extremely high quality proprietary database of financial data. Initial testing and development is conducted on a sub-sample of the historical dataset, typically excluding some markets and time periods entirely. If the hypothesis is not rejected in the initial test, the test is repeated using the markets and historic periods which had been kept out of sample. This multi-stage approach minimises data mining pitfalls. The next step in the research process is to examine the sensitivity of results to assumptions. Alternative methods of testing the hypothesis are also specified by an independent risk review which validates all research findings and ensures that each new strategy fits well into the overall portfolio. Finally, it is possible to monitor the prototype strategy in a paper trading period to assess real world performance, prior to commissioning. This rigorous scientific method is in stark contrast to the rather ad hoc approach of a typical discretionary manager.

5. Performance Analysis Review
There is emerging scientific literature comparing the performance of systematic versus discretionary investing styles. The evidence so far shows that systematic managers appear to survive longer, generate higher performance and are superior to discretionary managers when it comes to market timing.

Recent evidence from Julia Arnold and Paolo Zaffaroni, at Imperial College London, documents that Systematic CTAs survive far longer than discretionary CTAs. The authors divide CTAs from the BarclayHedge database into systematic and discretionary sub-samples. They find that systematic CTAs have an average life to date of twelve years versus eight years for your average discretionary manager [9].

The evidence on performance is also consistent. The team at Imperial College London has produced another paper which finds that performance is strongest for larger systematic CTAs [10]. This finding supports our earlier discussion around the advantages of scalability and trading twenty four hours per day offered by a systematic investment process, operated at scale. In an article forthcoming in the Journal of Alternative Investments, researchers from the Centre for Investment Research at University College Cork have provided a detailed analysis of the performance of different categories of CTA. They find systematic trend following managers have higher returns and superior performance to other categories of CTA [11]. Similarly, in a recently released paper, researchers from Man Group analyse the relative performance of systematic and discretionary managers. Their results show that systematic macro funds outperform discretionary macro funds [12].

The evidence on market timing is less extensive but researchers at the Centre for International Securities and Derivatives Markets (CISDM) at the University of Massachusetts, Amherst do find evidence of superior market timing by systematic CTAs relative to discretionary managers [13].
6. Conclusion

Investors who have embraced systematic techniques more recently were perhaps initially influenced by their very strong performance in the depths of the global financial crisis. But in our regular meetings with clients we have noticed tremendous advances in awareness of the broader benefits of a systematic approach in all market environments.

This bodes well for the future of the investment industry. By harnessing the advantages of a scientific approach to investment management outlined in this paper, clients should be well placed to face the uncertainty inherent in investing in financial markets.


1. “Prices will average $105 a barrel in 2014, from $108.71 in 2013, according to the median of estimates from the seven analysts who most accurately predicted this year’s level in a survey last December.” Bloomberg 30th December 2013.
2. “Iraq violence lights fuse to oil price spike” Financial Times 20th June 2014. “Oil prices spike as Iraq violence flares” CNN 12th June 2014
3. “Moody’s slashes oil forecast for 2016 by $10 a barrel” CNBC 15th December 2015. “Banks Slash Oil-Price Forecasts Again”, Wall Street Journal 12th January 2016.


[1] Lefèvre, E., 1923. Reminiscences of a stock operator, John Wiley and Sons
[2] Kahneman, D., and A. Tversky, 1979. Prospect theory: An analysis of decision under risk, Econometrica: Journal of the econometric society, 263-291.
[3] Tversky, A., and D. Kahneman, 1971. Belief in the law of small numbers, Psychological bulletin 76, 105.
[4] Kumar, A., 2009. Who gambles in the stock market?, Journal of Finance 64, 1889-1933.
[5] Lu, Y., S. Ray, and M. Teo, 2016. Limited attention, marital events, and hedge funds, Journal of Financial Economics Forthcoming.
[6] Kumar, A., A. Niessen-Ruenzi, and O. G. Spalt, 2015. What’s in a Name? Mutual Fund Flows When Managers Have Foreign-Sounding Names, Review of Financial Studies 28, 2281-2321.
[7] Daniel, K., D. Hirshleifer, and A. Subrahmanyam, 1998. Investor psychology and security market under-and overreactions, Journal of Finance 53, 1839-1885.
[8] Hong, H., and J. C. Stein, 1999. A unified theory of underreaction, momentum trading, and overreaction in asset markets, Journal of Finance 54, 2143-2184.
[9] Arnold, J., and P. Zaffaroni, 2012. Survival of commodity trading advisors: Systematic vs. discretionary CTAs, Working Paper.
[10] Arnold, J., 2012. Performance, risk and persistence of the cta industry: Systematic vs. discretionary ctas, Working Paper.
[11] Foran, J., M. C. Hutchinson, D. F. McCarthy, and J. J. O’Brien, 2016. Just a One Trick Pony? An Analysis of CTA Risk and Return, Journal of Alternative Investments Forthcoming.
[12] Harvey, C. R., S. Rattray, A. C. Sinclair, and O. Van Hemert, 2016. Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance, Working Paper.
[13] Kazemi, H., and Y. Li, 2009. Market timing of CTAs: An examination of systematic CTAs vs. discretionary CTAs, Journal of Futures Markets 29, 1067-1099.