At the same time, it seems to have been very much the market ecology itself, with low liquidity conditions and concentrated investor positions, that drove the jump in market volatility since mid-December.
While many market levels aren’t much different from a month ago, much has happened since then. In the weeks before Christmas, equity markets corrected strongly downward and thereafter rebounded from oversold levels, partially helped by last week’s supportive comments from Fed Chair Jerome Powell. Along the way, the VIX volatility index level jumped above 35, approaching its highest level for last year. Other indexes made dramatic daily moves, with the US S&P 500 gaining 116.6 points, or 6.44 standard deviations, on 26 December.
To some extent these developments should not be surprising. The regular occurrence of unexpected events in financial markets offers a clue to the underlying nature of the system. Financial markets can be risky, irrational and unpredictable, as they are full of irregularity, feedback loops and friction. Markets consist of participants who are constantly exploring, learning and adapting, but who are also subject to behavioural biases, have different objectives, skills and rationality, and have time-varying influence on market behaviour.
As a result, markets are complex adaptive systems that frequently show this type of behaviour, but not in a regular or predictable manner. Moreover, their complexity is not decreasing but is rather increasing further, given new technology, accelerating information flows and a constantly changing composition of market participants.
More and more money managers are therefore focusing on the opportunities of new technology and are exploring AI and Big Data to enhance their understanding of the complex market environment and improve their investment decisions.
For any investment decision, some basic steps are needed: analysis of the relevant information, a prediction of what will (most probably) happen in the future and a purchase of securities that will benefit. This is obviously easier said than done, especially with the amount of potentially relevant information exponentially growing.
Over the past two centuries, the use of “machines” to systematically capture data and extract unbiased insights from it has significantly increased. Until a few years ago, most of the information that was harvested that way was mostly economic data and/or financial market pricing and volume data.
With the eruption of the digital revolution in recent years, the type of information that can be captured through the new Big Data channels has expanded greatly.
In addition, we have gained much more insight into how we think and feel; what opinions we have (and share) through digital channels, social media or digital news platforms; what sentiments we express regarding corporate activity or management decisions; and how much trust we have in governments or policy-makers. We leave traces of these opinions and emotions across all the digital channels we use, and the patterns they create can help us capture the mood of investors, identify the behavioural forces that drive markets up or down and sharpen our assessment of near-term risks around our investment exposures.
This is not only for the sake of improving forecasting, but also to create a continuous process of forecasting improvements – just as our brains learn from the mistakes we make (at least on occasion). Furthermore, Big Data and Artificial Intelligence can further empower risk management. They help to identify patterns, connections and dependencies among risk factors that are difficult to identify with single-factor (volatility) risk tools. To adapt fully, therefore, investors must explore ways to move towards more multi-dimensional risk diagnostics.
Man and machine need to play together more than ever before. Think of this as “tech-enabled investors”, where Artificial Intelligence and Big Data augment the strengths and creativity of humans to analyse the rare, the different, the new or the unprecedented. Diversity of thought strengthens reasoning and decision-making of human teams, and diversity of skill does the same for tech-enabled investment teams. Making these types of teams play well together requires a different mindset and culture, and we will all have to adapt to get there. But those who will move forward on this in 2019 are much more likely to win in the complex future ahead of us.
Have a great year!
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