Algorithmic trading: evolutionary tourney?

The use of artificial intelligence (AI) in the form of machine learning techniques and algorithmic trading in the finance industry has in recent years become widespread. The preference for quantitative techniques is not a new development in the finance industry: a strong respect for quantitative methods has long been in place. However, the recent massive uptake in the use of AI through these algorithmic decision making tools for high frequency trading is a relatively new development.

Locally, these developments have contributed to improved industry productivity at a rate considerably greater than other industries in Australia. They have also resulted in increased profits to investment vehicles. However, they are not without pitfalls and cautions. The inability of machine learning models to predict “fat tail events” or regime change in the data generating process is well known. The dependence of these models on a consistent solution space over time is another weakness that should be acknowledged.

Failure to understand critical features of these models and algorithms may lead to substantive losses through naïve application. A subculture within the industry that breeds similarity between models may possibly make this a systemic failure at some point in the future.

While these models offer excellent improvements in wealth generation and productivity in this industry, (Qiao & Beling, 2016) they are not a substitute for human decision making, only an assistance to it. Failure to acknowledge this distinction may lead to adverse outcomes on a very wide scale.

Background

Algorithmic trading and use of decision analytics in financial market transactions is now very widespread, with many investment houses reducing the number of “stock pickers” they employ in favour of purely quantitative algorithmic approaches to trading (The Economist Intelligence Unit, 2017). The use of these algorithmic decision making tools is common in markets such as foreign exchange (Chaboud, et al., 2014) and commodity markets, as well as listed stock exchanges.

As part of an overall move towards digitization, these AI tools have resulted in increasing profits for investment houses in recent years (The Economist Intelligence Unit, 2017). Indeed, in part contributed to by these AI methodologies, the productivity growth of the finance industry in Australia alone is outpacing the aggregate productivity levels of the economy by a very considerable amount over the last fifteen years, shown in Figure 1 (Australian Bureau of Statistics, 2016).

 

Productivity chart

Figure 1: Productivity, Finance Industry and 12 Industry Aggregate Productivity. Data: ABS (2016), chart: Author

 

Algorithmic trading as an evolutionary tourney?

 

The methodology used to employ algorithmic trading is a combination of predictive modelling techniques and prescriptive decision frameworks (Qiao & Beling, 2016). The decision tools implemented by the automated algorithms consist of optimisation and simulation tools (Qiao & Beling, 2016).

Predictive methods vary, but include forecasting using machine learning methods such as neural nets, evolutionary algorithms and more traditional econometric modelling such as the AutoRegressive Conditional Heteroskedastic (ARCH) modelling frameworks. One example is Laird-Smith et al.’s use of regression techniques to estimate a systemic error capital asset price model (Laird-Smith, et al., 2016). Other techniques include using sentiment analysis on unstructured corpi generated by journalists to estimate the effects of feedback into the market and the interaction between market and sentiment (Yin, et al., 2016). Yin et al. (2016) note the strong correlation between news sentiment and market returns which can be exploited to improve investment outcomes.

The move towards algorithmic trading has been highly successful. Improved outcomes are observable both systemically, in the form of increased liquidity and price efficiency (Chaboud, et al., 2014) and at the firm level with improved profit margins (The Economist Intelligence Unit, 2017).

However, the complexity of machine learning models underpinning these systems make them difficult to compare and critically analyse, requiring novel techniques to do so such as Parnes (2015). Many of these algorithms are proprietary and closely guarded: it is not possible to outsiders to analyse them, except by observing trading behaviours ex post.

There are also some negative outcomes which bear consideration. Chaboud (2014) note that behaviour of algorithmic traders is highly correlated. While it is noted that this correlation does not appear to cause a degradation in market quality on average, these AI algorithms have in the past resulted in unexpected and costly mistakes, such as the automated selling program that initiated the ‘flash crash’ of 2010 (Kirilenko, et al., 2017).

In broader terms, it is now well known that machine learning algorithms are biased in unexpected ways when the training data from which they are generated is biased. For instance, an algorithm that decides on offers of bail to people awaiting trial in the U.S.A. was shown to disadvantage bail seekers of colour compared to those who were white despite the fact that race was not a feature selected for use in the algorithm (Angwin, et al., 2016). Algorithmically generated decision making can also lead to unforeseen and unwanted outcomes. Facebook was recently revealed to be selling advertisement space targeted at users categorised with anti-Semite topics (Angwin, et al., 2017). These algorithmically-generated targeting categories were unknown to the company until they were discovered by journalists.

The economic implications of employing algorithmic decision making methods are enormous, not only in private financial circles, but in government as well (Dilmegani, et al., 2014). It is clear that employing this form of AI will continue well beyond the finance industry, whether or not bias or negative unexpected outcomes is eradicable. However, being aware of and actively testing for the existence of these possibilities is critical going forward.

In the finance industry, each algorithmic trader has a similar fitness function by which it is judged, fine-tuned and updated: the ability to profitably trade in the market. In this sense, in a market dominated by algorithmic trading AI is effectively a tourney in which the fittest agents survive and the weakest are abandoned and replaced with better models. However, the resultant similarity after many trading generations, already noted in the popular press (Maley, 2017) may expose the system to crisis during an unpredictable event if a majority of algorithmic traders react similarly in unforeseen ways that have negative consequences.

The high speed at which these high frequency trading algorithms execute trades could lead to far greater damage in the future than the ‘flash crash’ documented by Kirilenko et al. (2017). While optimal performance of algorithmic trading agents will likely include a ‘Deadman’s switch’- a command which will halt further trading in the event of a predetermined event (such as a crash)- the efficacy of these measures have not been tested systemically during a financial crisis.

 

Conclusion

Algorithmic trading is a branch of artificial intelligence that has contributed to the generation of wealth and increased productivity in the finance industry, algorithmic decision making should be seen as an aid to human decision making and not a replacement for it.

While gains to be made from the technology are substantial and ongoing in fields beyond finance, the possibility of a lack of variability among algorithmic traders and the proprietary and hidden nature of these models which are difficult to explain and interpret may lead to adverse consequences if applied naively.

 

References

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Kirilenko, A., Kyle, A., Samadi, M. & Tuzun, T., 2017. The Flash Crash: High-Frequency Trading in an Electronic Market. The Journal of Finance, Volume 72, pp. 967-988.

Laird-Smith, J., Meyer, K. & Rajaratnam, K., 2016. A study of total beta specification through symmetric regression: the case of the Johannesburg Stock Exchange. Environment Systems and Decisions, 36(2), pp. 114-125.

Maley, K., 2017. Are algorithms under estimating the risk of a Pyongyang panic?. [Online]
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Parnes, D., 2015. Performance Measurements for Machine-Learning Trading Systems. Journal of Trading, 10(4), pp. 5-16.

Qiao, Q. & Beling, P. A., 2016. Decision analytics and machine learning in economic and financial systems. Environment Systems & Decisions, 36(2), pp. 109-113.

The Economist Intelligence Unit, N. I., 2017. Unshackled algorithms; Machine-learning in finance 2017. The Economist, 27 May.Volume 72.

Yin, S., Mo, K., Liu, A. & Yang, S. Y., 2016. News sentiment to market impact and its feedback effect. Environment Systems and Decisions, 36(2), pp. 158-166.