How do you manage risk when you can’t be certain about uncertainties?
A new catalogue of risks has surfaced as we march into the new year, posing new challenges to the management of investments.
The first risk is uncertainty. We don’t know what we don’t know, as the cliché goes. So we cannot be certain what those uncertainties are. When and how uncertainties - political, social, economic, technological disruptions - play out is beyond prediction. The odds of success or failure in investment decisions based on these uncertainties depend as much on judgment as luck. The best thing we can do is to systemise the decision-making process, to incorporate as many diversified variables as possible to construct a more reliable framework, which might complement the limited human cognition.
The second risk arises from how uncertainties have upended conventional relationships. Inflation and unemployment, for instance, no longer moves along the classic Phillips Curve, while the correlations among stocks, bonds and currencies are showing unprecedented new patterns. Economic and investment cycles have been redrawn by mysterious dark forces. There are so many dilemmas, trilemmas and paradoxes that many of the generally accepted and applied economic models and theories have entirely or at least partially lost their credibility. We need urgently to develop new theories and to construct new models to capture and deal with these unprecedented phenomena. Unlike natural science, economics and finance are supposed to be highly adaptive to reality. In the meantime, we need to carefully study the newly emerged investment styles including smart beta, and index-linked exchange traded funds (ETFs). We need new risk management tools to better cope with the new paradigms.
The third risk is posed by technological disruption. Wall Street and the City of London are crammed with Ivy League MBAs and economic majors, because investment decisions of the past mainly revolved around financial considerations.
Nowadays, new technology has reshaped almost every single industry. That means investment is now more about a specific technology, and less about financial parameters. The result is more and more science majors filling the seats at investment banks and venture capital firms, giving rise to a new, de rigueur “T-shaped” investment capability. That combines horizontal knowledge and understanding of diverse financial markets with vertical expertise and mastery of a specific industry or segment.
Investing in the new economy is never a me-too or used-to-be process; you thrive if you get it right, or die if you get it wrong. The financial industry is poised at such a thrive-or-die precipice, where real life applications can be reshaped or transformed by technology, or fintech.
Positive changes brought by fintech ought to be laudable, but we must prepare for their negative side. Here are some samples:
- Will human traders or investment managers be pushed aside by artificial intelligence (AI)?
- Will machine learning and reasoning outsmart investment professionals?
- Will hackers lay to waste entire networks of devices and applications by exploiting the vulnerabilities in the internet of things (IoT) technology?
Nobody knows for sure how this will play out, but disruptions have become commonplace. Regulatory technology, or regtech, is not the simple antidote to the negative effects of fintech, for the obvious paradox that regulators cannot gain access to petabytes of market data without interfering in the market.
Sound and appropriate regulations should target the new issues and their new patterns and then set the rules accordingly, rather than getting involved with raw data in the first place. The right regulatory mindset should be to identify new patterns, generalise new standards, and then apply new rules and upgrade new mechanisms.
It’s neither possible to avoid risks entirely, nor cost-effective to hedge 100 per cent of them. More realistically, risks should be dealt with by embedding them in the investment process itself, starting with identifying, recognising, understanding them to managing them systematically. Risk management should also be data-driven, AI-enabled and technology-based. Investment needs machine learning, reinforcement learning and machine reasoning, so does investment risk management.
Here are some risk management issues we should pay close attention to in 2018:
- The surge of passive investments and its effects on market parameters;
- The high equity valuation, particularly in the U.S. and the possibility of the bubble bursting;
- The shift to factor investments like smart beta, and the interaction with existing investment styles;
- The increases in interest rates and the normalisation of the balance sheet by major central banks, and their impact on the world economy, especially on emerging markets;
- The sovereign defaults in Latin America and the spillover effects;
- Cyberattacks and cybersecurity breaches;
- The complacency brought by the outperformance of investments, and the misjudgments on geopolitical issues.
We may still not be fully prepared, but the bottom line is that we know we ought to be prepared.
Liu Jun is executive vice-president of the China Investment Corporation. This column represents his opinion.