Research Projects 2020

| Thought Leadership

Outlined below is a brief summary of the research we carried out in 2020. Some of our research findings are publicly available on our website Others formed part of bespoke research that we undertook at the request of individual clients. Please get in touch if you would like to find out more about these projects, or to discuss your own research requirements.

1. Enhancing our ESG approach

A new version of our ESG scoring

In the equity space, we developed a new version of our ESG scoring. The score is built with a focus on how companies manage ESG-related issues at a sub-industry level. We measure the proportion of a company’s exposure to ESG risks that are effectively managed and take into account the vulnerabilities of the industry and the competence of management. We also accommodate the impact of fast-evolving controversies as well as their severity by penalising the overall ESG score.

Methodology to determine portfolio alignment with the Paris agreement

At the company level, we have developed a methodology to determine the portfolio transition pathway in terms of apportioned GHG emissions with respect to different alignment trajectories while understanding the impact on different sectorial allocations and determining the gap in terms of GHG budget. This allows us to identify where the portfolio is located with respect to the Paris agreement trajectory.

Monitoring ESG factors in private equity

Within private equity, as part of our fund monitoring process, we have put in place a specialised ESG questionnaire which is shared with our investee companies and GPs. This allows us to monitor the evolution of important ESG factors within our investments.

Enhancing ESG integration

In the multi-asset space, we have enhanced our ESG integration by expanding the effort to long/short equity portfolios, implementing a methodology for investing in Green bonds within the sovereign space, and following the responsible guidance of the London Bullion Market Association (LBMA). We have also complemented our external manager selection by ESG considerations as one of the determinants of the process while in parallel engaging with managers and industry bodies.

ESG Investing in minimum variance portfolios

We have published a paper in which our research on ESG investing in minimum variance portfolios found that incorporating both bottom-up and top-down ESG guidelines does not materially affect portfolio efficiency and can lead to improved risk-adjusted performance and downside protection. This means that investors can address their ESG preferences while still achieving their investment objectives.

Climate scenario analysis

We have also developed a model which allows us to evaluate the climate factor exposures and their impact on the portfolio while determining the resilience of the portfolio for the future through Climate Value at Risk.

2. Strengthening our quantitative models

Newscasters versus Nowcasters

One major piece of work involved the evolution of Nowcasters. We have been using this proprietary indicator for many years for asset allocation across traditional and alternative risk premia. We have been interested for some time as to whether tracking newspaper articles and blogs focused on macro news could be an additional leading indicator. We found that while Nowcasters and Newscasters track each other over the long term, in the short term there are variations with the Newscasters reacting quicker to economic conditions, particularly at turning points. Newscasters combined with Nowcasters have provided much better risk adjusted returns than Newscasters alone over the past 20 years. The value of Newscasters has been further validated during the COVID-19 crisis given they reacted ahead of the Nowcasters.

Machine learning filters for regional funds

We continued to implement in our equity strategies some machine learning filters in the different regional funds we manage. We started this process in 2019 with the implementation of such algorithms on US stocks and continued in 2020 with the European, emerging market, Japanese and global regions. The goal of our machine learning algorithm in our risk-managed equity is to better exclude stocks with large tail risks in the portfolio construction process results in superior out-of-sample properties after accounting for transaction costs.

Developing an alpha model for benchmark-aware equity strategies

We have also developed an alpha engine using our machine learning model on expected returns. This alpha model allows us to design benchmark-aware equity strategies as a complement to our risk-managed portfolio. Theis new range of strategies use the same tools and models as the risk-managed strategy but explicitly target a superior information ratio.

A quantitative approach to evaluating private companies

Private equity investors have traditionally relied on professional judgement and, to a much lesser extent, on quantitative techniques. Existing research has concentrated mainly on fund-level data analytics but in 2019 we developed an innovative machine learning-based scoring tool to predict the success of PE fund investments based on the information available to prospective investors.

In this study, we take into account all existing lines of research and extend their scope to deliver more accurate and practical estimates of private companies’ future value using our proprietary database of more than 1,000 LBO deals from almost 140 PE funds, covering the period from 2000 to 2017 and incorporating information such as deal valuations, financial performance and final investment performance. We complement company-level data with the attributes of the PE fund related to each deal, and our proprietary macroeconomic indicators.

Our new models bring us closer to the era of technology-powered PE investing and already show robust results. We will continue to refine them and improve their predictive power. In our view, data and data-driven insights will create more transparency in private markets and help PE investors make better investment decisions.

Machine learning in fund selection

Artificial intelligence is increasingly being adopted in financial markets, transforming the way investible instruments are analysed and selected. Building on earlier Unigestion research on machine learning in private equity fund selection, we have found that integrating machine learning techniques in the quantitative due diligence process improves hedge fund selection in several ways. First and foremost, it helps address the elephant in the room: financial market returns are not normally distributed and linear techniques are often ill-suited to explain their interactions. Broadening the scope of the analysis in a systematic fashion increases its quality, thereby impacting portfolio returns positively. In particular, identifying a predictive pattern within a strategy represents a strong appeal for portfolio construction. Applying machine learning techniques to liquid alternative funds is a natural step as they are well suited to identify non-linear relationships. However, training the model is only possible if large amounts of historical data and the experience to choose the right factors are available.

3. Designing new approaches for portfolio construction

Risk-managed equities: Riding the macro cycles

We have been running risk-managed equity portfolios for over two decades; harvesting the low volatility anomaly using minimum variance optimisation and strong active risk management to deliver consistent performance for our clients, on both an absolute and risk-adjusted basis. However, we have also navigated several market cycles and experienced periods when the “low volatility” investment style lagged significantly behind the market-weighted benchmark – a lag that was particularly pronounced in “risk-on” markets when the macro picture was improving or very accommodative.

Our research focused on finding a way to dynamically balance between our absolute risk management objective and the need to control for active risk at times when absolute risk management is a lesser concern. In this paper, we present how we developed a macro-based dynamic signal to arbitrage between absolute risk management and active risk management within our portfolio construction process. We also show how we use this signal to control our portfolios’ exposure to other risk premia beyond low volatility.

Active v passive low risk investing

While the foundations for low volatility investing were laid almost 70 years ago and are well understood, investors are increasingly focusing on issues of practical implementation. A key concern is how to incorporate ESG and liquidity preferences into low risk portfolios while managing transaction costs. Negative oil prices during the COVID-19 crisis and their effect on passive oil ETFs have raised additional questions about passive investment vehicles: managers had to actively intervene to override passive rebalancing rules to mitigate the risk of the vehicle’s failure.

In this paper, we examine and highlight the strengths of active low risk investing compared to passive low risk investing. We find that passive low risk indices have significantly larger illiquid holdings than active low risk portfolios and we demonstrate the adverse effects of such concentration risk in stress situations. Moreover, an examination of ESG characteristics shows that existing passive indices provide suboptimal carbon and ESG characteristics compared to what active management can offer. We made six recommendations to low risk strategy investors including that low risk portfolios with suboptimal ESG ratings due to the inclusion of stocks with, for example, higher carbon footprints can be poor at protecting on the downside. Conversely, actively-managed, low risk portfolios demonstrate greater resilience and are better at matching investor ESG preferences.

Building a better strategic allocation

We also did some valuable work to establish a new approach to asset allocation that solves the structural problems with the three most widely used but outdated approaches – the classic 60/40 portfolio, the endowment model and the risk parity portfolio. These models are overly concentrated in growth assets and, by being more focused on traditional assets, can less easily adapt to today’s environment of low yields and stretched valuations.

Our new approach to portfolio construction takes some of the key principles behind each of these models and evolves them – expanding the use of liquid and transparent alternative investments, incorporating economic sensitivity into risk allocation decisions, and developing a risk measure more closely aligned to investors’ goals and that accounts for the multiple dimensions of risk.

This macro risk-based approach provides diversification of risk across asset classes as well as macroeconomic environments over the long term. The enhanced diversification is not intended to lead to outperformance in all economic and market conditions. Rather, it aims to ensure that the macro risk diversified allocation performs reasonably well in all environments. As a result of this consistent performance, it should outperform other strategic allocation approaches over the long term.

The challenge of portfolio hedging

Portfolio hedging is in strong demand as the last 20 years have highlighted a real risk of large drawdowns, keeping equity portfolios underwater for prolonged periods of time.

Unfortunately, traditional hedging solutions using outright put options are so expensive that it is not sustainable over the long run. To permanently protect against downturns, a systematic hedging strategy must find the right balance between cost of carry and reactivity. To reduce the cost of carry while keeping the hedging effective, investors can design a downside-hedging strategy that does not aim to protect the first leg of a down move, and allocate to a diversified set of cost-efficient strategies. To reduce the risk that the strategy doesn’t work in a given environment, one should use a set of strategies with different characteristics.

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Document issued January 2021.