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Exploring the future of quantitative finance

Beyond the “passive vs active” asset management paradigm

The passive vs active approach to asset management has been on our minds for a few years now. What are the benefits? Why is one better than the other? Why not do both with measure? Aymeric Kalife, Associate Professor at Paris Dauphine University, debunks some of the common misconceptions of both passive vs active approaches, and explores the ways digital technologies and new strategies could pave the way to more resilient and more successful business practices.

The asset management industry has been facing two main challenges since the 2008 financial crisis, regarding not only alpha generation but also risk mitigation:

         I.            Persistent low interest rates have created a dilemma for those planning for retirement, where traditional fixed annuities yield became unattractive due to their large bond historically low yields, while equities became inherently riskier and costlier given the multiple unexpected volatility spikes and drawdowns since 2015.

       II.            The deflationary environment and decreasing alpha have penalised active management in favour of low fees passive management based on algorithms leveraging on digital technologies, while some equity volatility spikes combined with increasingly connected markets have made some growing volatility control passive management algorithms sometimes inefficient since 2015.

As a result, neither active nor passive style seem to have significantly outperformed, as illustrated by the very poor performance in 2018. To help mitigate such a hardship, few innovations that go beyond the “passive vs. active” asset management paradigm may be appropriate.

Mixing active AND passive management styles to extract alpha and mitigate equity drawdowns

Contrary to conventional wisdom, less volatile stocks empirically tend to outperform over the long term, because of countercyclical behaviour in market slumps by losing significantly less during downturns and drawdowns, avoiding the performance swings. In contrast, volatile stocks have to work much harder to first restore the value lost during periods of decline and then to grow.

This historical performance of low-risk stocks defies the central paradigm of traditional finance theory which states that lower risk goes with lower returns (“negative mean-variance relationship”), stemming from (i) a lottery mentality driving most investors to consistently overpay for the small chances of winning big in riskier stocks, (ii) an inclination to avoid low-beta stocks, and (iii) the general use of log-Gaussian modelling assumption in returns distributions (characterised by left-skewed skewness distribution of returns, i.e. a long tail to the left of the returns distribution).

As a result, a sound mix of passive AND active asset management within the asset selection and asset allocation process, that combines “volatility control” mechanisms with high fundamental quality stocks selection (healthy and stable profitability, strong free cash flows, low debt and shareholder-friendly practices, above average dividend payout, low net equity issuance), can produce stronger performance. Such a mixed approach integrates more fundamental views with technicals (looking not only at the full distribution of the stocks returns but also at historically fundamental topics like management discussions, sentiment). This has proved to be a more efficient way since the 2008 crisis, not only to mitigate significant declines but also to generate significantly higher returns at similar levels of risks.

As a complement, using hedging in portfolio strategies for yield enhancement or performance protection also provides a way to mitigate drawdowns or boost returns within sideways markets, if they are chosen at appropriate punctual times, in order to remain at reasonable cost.

Digital technologies can be used for (i) merging and visualising data (data virtualisation and BI), (ii) forecasting returns/ risk and customising the utility function of the customers (artificial intelligence), (iii) transaction costs modelling (artificial intelligence technologies using “optimal control”).

Better risk management and business integration into asset management practices

Beyond the asset selection and asset allocation process, the design of the appropriate asset rebalancing frequency /trigger, tailored to the market regime and the risk appetite is also key in the asset management performance. For instance, the portfolio may drift from some committed target asset allocation, providing risk/return characteristics that may be inconsistent with an investor’s goals and preferences (e.g. 60/40 Equity / bonds target). A rebalancing strategy addresses this risk by formalising guidelines about how often the portfolio should be rebalanced, how far an asset allocation can deviate from its target before it’s rebalanced, and how much rebalancing should restore a portfolio to its target.

Developing a culture of stress tests and traffic lights governance systems to manage urgent and extreme situations is also required to enable manage unpredictable market shocks.

Those two examples emphasise the need to better integrate risk management and business in asset management practices. Unfortunately, most financial institutions take on complex risks that often tend to be managed in silos. Building processes and easily accessible tools that allow you to very cleanly attribute the performance of your strategies or behaviour of your products is key. There is still a lot of work to be done in integrating those risks into a comprehensive risk management and analytics systems that provide sufficient level of transparency to management and serve as decision making tools in real time.

In that respect, digital technologies can be used for devising the (i) suitable rebalancing strategy based on risk tolerance relative to a target allocation, net of transaction costs (Multi-steps automatised process using back testing and stress tests across a wide range of patterns and stress tests) (ii) cutting silos between the business and risk management departments through sharing and visualising data in a secure way (data virtualisation and BI) and enhancing common projects management.

Developing a bottom-up customer-centric approach by growing business integration between the insurance industry and the asset management industry to mitigate pressures on fees

To mitigate pressure on fees, whether in passive or active funds offer, develop innovative solutions that better fit customers’ needs, by leveraging coordination between insurance/banking product design expertise and asset management experience.

Today, most products are still based on a top-down approach, without caring enough about customers’ risk appetite and time horizons, which requires more tailored specific product features design, and impacts the portfolio structure and adequate rebalancing. Such a bottom-up approach should not only take advantage of big data accumulated over the past ten years on customers, but also of the new digital technologies that enable to merge, analyse and secure them (data virtualisation, artificial intelligence, blockchain).

Given the data and analytics asymmetry in different aspects of what asset managers/banks do compared to insurers about risk taking and analysis, collaboration between asset managers and insurance/banking can also be strengthened, notably by reducing political frictions and clarify governance, developing mobility and trainings / communication. Integrating the “customer to asset flow” can create additional risk transfer efficiencies, i.e. create a more efficient transaction flow from those who want to take a certain type of risk to those who want to avail themselves of that risk.

In that respect, digital technologies can be used for devising the (i) suitable investment strategies and product design in line with customers’ risk appetite (artificial intelligence technologies using “optimal control”) (ii) cutting silos between the asset manager and the insurance company through sharing and visualising data in a secure way (data virtualisation and BI) and enhancing common projects management.

Developing expertise in analysing interconnections and feedback loops between market shocks

There has been a growing concern that volatility risk managed funds with algorithmic passive strategies may all pile into the same assets at the same time, potentially creating more risk, or unanticipated for the writers of the guarantees and losses for the investors.

Actually, volatility risk managed funds ($500 billion of assets, two-thirds of which are traded by algorithms, while $275bn are held in variable annuity subaccounts) sell equities when volatility is rising and buy equities when volatility is falling, potentially translating into market “volatility feedback loops” that exacerbate both selloffs and rallies. It is one key driver of the repeated pattern of sharp selloffs followed by consistent rebounds as experienced since 2015.

As a result, it is key to develop more resilient volatility-control fund algorithmic mechanisms, so that they do not overreact following an initial market fall, but only consistently with sound economic concerns, for instance by introducing macro-economic and supply and demand indicators into the volatility control mechanism.

There, again, digital technologies can be used for merging huge quantity of data from different sources (using data virtualisation), for visualising and analysing them (BI technologies), or for helping asset management decisions (using artificial intelligence using non-linear market impact modelling and “optimal control”).

Join Aymeric Kalife at QuantMinds Americas this September to discuss active vs passive asset management in greater depth.

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Watch Iuliia Shpak, Quant Strategies Specialist at Sarasin & Partners, evaluate both active and passive asset management, and propose a different approach.