alternative risk premia research.

Improving through research: learn from experience, adapt to new market dynamics and stay focused on objectives


During recent years, we have engaged in extensive research in order to make our LOIM Alternative Risk Premia (ARP) strategy more robust and less sensitive to market conditions.  Our ultimate objective remains, as always, to deliver true uncorrelated returns to investors. 

  • Our research was concentrated on rethinking the access to some premia (such as volatility and trend-following). We believe these premia have been the most impacted by harsh market rotations, which we have been observing more frequently during recent times.  We have also revisited our approach to Equity style factors, as these “popular” styles were significantly under pressure. 
  • Diversification being key, we developed new strategies, orthogonal to the existing ones. The main characteristics of this endeavor is to be focused on arbitrage opportunities (non-directional / no hidden Beta), to be uncorrelated to their primary markets, and to exhibit a limited decay and/or which are not already known and not crowded.
  • Latest leading and innovative technologies have been at the heart of our latest research efforts. We have engaged in AI / Machine Learning development to further strengthen our capabilities in order to find and generate new Alpha investment opportunities.

We have listed below our research that we have published as well as the enhancements we have carried out during recent times. Please click on the individual buttons below to read our research.

Please reach out to Clement Leturgie in Europe or Jessie Schenker in the US for any further information.

building the optimal approach to equity factor investing.

The recent period has been quite challenging for equity factor strategies. While some investors started to express doubts about their future, others decided to “sin” a little (i.e. factor timing). At LOIM, we have learnt two major lessons.

The first is that portfolio construction is a key requirement for prudent factor investing. Having done our homework, we present in Our Approach to Factor Investing, our views on the efficient implementation of factors. 

The second lesson can be summarized by the words of John Maynard Keynes “The market can stay irrational longer than you can stay solvent”. As patience is no longer an option, our current research is focused on new sources of alpha, through the application of machine learning (to financial and non-financial data including ESG). This will be the basis of our new equity strategies in 2020, with the objective, as always, to provide diversification and complementary sources of returns to traditional factors. 


 Read Our Approach to Factor Investing

extracting alpha from ESG.

Portfolio managers integrate ESG criteria for a number of different reasons. One of them is the desire to do “good,” which is a part of the global trend towards more responsible investment. Another reason for ESG integration is better risk management. ESG data can provide an alternative view on companies, which may help detect risks not evident from traditional fundamental analysis or market data. Lastly, more and more portfolio managers use ESG as an additional source of alpha hoping that ESG issues are not yet priced by the market.

We present in our research paper, ESG alpha: doing well while doing good, our main findings on the topic and how we intend to deploy it as new premium in our ARP offering.


 Read ESG alpha: doing well while doing good

using equity attributes and machine learning to forecast stocks returns.

Factor investing relies on only certain factors that have been discovered in the past, whose future as a source of excess performance is a matter of hot debate. At the same time, new factor premia are not easily accepted by the community on reasonable concerns around the risks of data mining.

We believe that this impasse can be overcome by making the process of factor-discovery truly systematic, and as much as possible free from human biases. Methods of machine learning are slowly but firmly penetrating the world of systematic equity, which may eventually add the missing piece of the puzzle to factor investing.

In our view, applying machine learning techniques to a wide set of stocks’ attributes including alternative data, such as ESG, will serve two main purposes. First, it will allow us to discover new sources of alpha either through exploring non-linear combinations between stocks’ attributes not captured by traditional factors or leveraging on new data sources. Second, the dynamic nature of machine learning will make the systematic process adaptive to changing market environments. This application of machine learning to systematic equity has been a strong focus of our research efforts. 

 Read Machine learning for the prediction of stock returns

developing new alternative risk premia.

We are constantly working to expand opportunities by developing new risk premia and/or refining existing ones. Over the recent period, we launched two new strategies on commodities, which we believe will allow us to increase diversification and return opportunities across our ARP offering. 

In 2018, we developed the LOIM Commodity Carry strategy which seeks to extract carry premium over the term structure as well as liquidity premium around the roll-over period. While being orthogonal to our other strategies (including our other commodities premia), this strategy brings interesting characteristics given its defensive profile, which tend to outperformed during global commodity risk-off scenarios.

In 2020, we launched an innovative way to exploit Value investing in commodities, which is not prevalent across the global ARP offering. This strategy seeks to extract premium from the supply and demand dynamic on the physical market and is driven by producers and consumers positioning. Price driven techniques are combined to support and diversify the global signal and positioning.

We are currently looking at event driven arbitrage using both traditional and unconventional data (Big data) in order to expand opportunities in equities.

building a diversified Alternative Risk Premia portfolio.

In the effort to build a robust diversified alternative risk premia portfolio, the Lombard Odier Investment Managers systematic team spends a considerable amount of time researching and developing its own premia. A frequent question we have been asked time and again is the process behind the decision to include a new premia in our portfolio. In our opinion, the key aspect in incorporating a new premia lies in its diversification benefits. One of our core beliefs is that a portfolio manager who can develop and effectively combine a robust set of orthogonal alternative risk premia, should be able to deliver the most attractive uncorrelated risk/adjusted returns for their investors. 

In this short analysis Building a diversified Alternative Risk Premia portfolio, we discuss the diversification benefits of our set of alternative risk premia, along with the proprietary combination process we have developed specifically for our portfolio.

 Read Building a diversified Alternative Risk Premia portfolio

rethinking access to volatility.

Volatility risk premium has always been part of our ARP offering. In 2017, we expanded our access to volatility to go beyond common implementations that tend to focus solely on short equity volatility premium. Our volatility strategy has been designed to harvest volatility premium across equities but also on fixed income. It also offers access to volatility arbitrage opportunities such as convexity and mean reversion.

In 2019, we revisited the access to the short equity volatility premium with the objective to be more defensive with regards to risky asset downturns as well as frequent back and forth movements. The main enhancement was implemented by redefining our option selection strips, to deliver smoother and uncorrelated returns over short and long term.

rethinking the use of trend-following.

In 2018, we enhanced our ARP Cross-Asset Trend strategy to integrate a “Risk-Overlay” with the objective of preventing our trend model from suffering in significant market downturn reversals. While we expect the ARP Cross-Asset Trend strategy to have low correlation to traditional asset classes and to contribute positively to the Fund’s performance over the medium to long term, this directional strategy can typically re-correlate to risky assets during risk-on scenarios. As a result, the Trend strategy can potentially add short-term equity risk into your portfolio. During sharp reversals, you may then suffer from a lack of short-term diversification. Therefore, we researched ways of preventing our Cross-Asset Trend premia to be significantly exposed to risky asset sensitivity. For this, we developed a ‘Dynamic Cross Asset Trend’ strategy with the aim to avoid participating in short term market reversals. We developed a risk overlay process for our Cross Asset Trend strategy. The risk overlay progressively diminishes the risk budget as its ex-ante correlation to equities increases. 


 Read trend following and volatility premia 2018 review

Extracting greater value from value investing.

Several consecutive years of underperformance have prompted an intensive debate on the future of value investing in equities. With Value being the most distinguished equity factor, this debate casts a doubt on factor investing as a discipline. This does not mean that we should sit and wait for the comeback of Value as it may not happen very soon. Any disappointing experience is an opportunity to revisit the strategy and that is what we did. 

In this note, Let’s make Value great again, we analyse what we know about Value, and propose a simple way of improving the performance of Value.


 Read Let’s make Value great again


important information.

For professional investor use only
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