This is a sponsored article from RAM Active Investments.
The application of artificial intelligence is opening up extraordinary new frontiers in asset and wealth management, as machine learning increases the speed and efficiency of critical data processing and optimisation exercises.
Meanwhile, another powerful theme is reshaping the investment universe: sustainable, socially responsible investing and the overarching need to build portfolios on strong ESG (environmental, social, and governance) foundations.
As a pioneer in this field, quant specialist RAM Active Investments is harnessing the convergence of these two themes by leveraging the power of machine learning to tap increasing growth to take a more informed approach to ESG-based investing.
The global shift towards sustainability investment has been remarkably swift. In 2011, fewer than 20% of S&P 500 companies disclosed ESG data. Five years later, the number of companies issuing sustainability reports or chapters on sustainability in their reports had risen to more than 80%.
Recent geopolitical implications and growing awareness of climate issues have only strengthened the imperative for sustainable and responsible investment, according to Thomas de Saint-Seine, CEO, founder, and senior fund manager at RAM.
“Investors, particularly those of the next generation, are not just concerned with the actual performance. They also care about how you achieve that performance,” de Saint-Seine said. “What kind of companies is the strategy investing in? Are they sustainable? Are they being responsible?”
These considerations were thrown into sharper focus following the US’s withdrawal from the Paris Agreement on climate change, placing President Trump out of step not only with political allies but a growing congregation of companies and investors worldwide, particularly the younger generation on the receiving end of the intergenerational wealth transfer.
“Typhoon Mangkhut, the strongest signal 10 incident Hong Kong has ever had to endure, is a stern reminder that climate risk is very real, and closer to us that we like to think. As a manager, if you don’t offer a strategy that truly takes such risks into consideration, you are not necessarily speaking to the needs of your clients and nor considering the long-term viability of the investment,” argued Benjamin Li, head of business development and head of SRI and CSR at RAM.
In discussions with de Saint-Seine, executives at RAM Active Investments concluded investing responsibly and in a sustainable manner, does not have to come at a cost to performance. In fact, there is increasingly strong evidence that companies with strong fundamentals and a strong ESG profile perform better than those with weak ESG credentials.
According to RAM’s extensive research, companies that embrace good governance tend to offer higher and more sustainable payouts, while financial and non-financial transparency favours sustainable financing, and restricting carbon emissions leads to sustainable earnings. Simply put: businesses that do good, tend to perform well.
“Our philosophy is not to integrate ESG criteria just to satisfy the need for ESG elements,” de Saint-Seine explained. “We only integrate something if it improves the quality of the portfolio and the overall risk/return profile of the strategy. We understand it has to be a win-win scenario in order for investors to truly appreciate this extra initiative’s value.”
He added that while some market offerings trade concessionary returns for added impact, this would not be RAM’s approach.
“As a specialist, people place their trust in us because of our superior performance and top-tier rating. We must continue to deliver and keep it our priority,” he explained.
The asset manager is able to do just that thanks to its quantitative research team, which has fashioned four pillars to underpin its systematic ESG investment process: governance, transparency, climate, and diversity. According to the team’s extensive research, companies that operate in accordance with the pillars, tend to perform well in terms of fundamentals and stock performance.
However, verifying the data to confirm a company’s ESG credentials is itself a key challenge. Just a few years ago, ESG data was scarce, but now there is an avalanche of information drawn from a multitude of sources and compiled using a wide range of methodologies. Further, ESG performance is not reported in a universal format, and consequently often lacks the consistency, reliability, and timeliness necessary to make informed investment decisions.
RAM tackles this by focussing on low-level data that is consistent across both time series and platforms, basing metrics on simple, repeatable, and transparent methodologies, and adopting conservative data availability assumptions to avoid look-ahead bias.
Evaluating huge quantities of data and identifying companies with strong ESG profiles and correspondingly strong investment potential is a scientific and highly involved process – one in which the artificial intelligence of machine learning has a key role to play.
RAM is ideally placed to make optimal use of such technology because of the vast stores of data it has amassed and analysed over the past 12 years, according to founding partner and senior equity fund manager Emmanuel Hauptmann.
“Machine learning is driven by data. The more data, the better the algorithm,” Hauptmann said. “We have processed this wide array of data sources to build hundreds of factors capturing a variety of different market inefficiencies. This research on the inefficiencies provides us with many inputs and dimensions we can now embed in our machine learning effort, making us well positioned to benefit from it.”
RAM’s machine learning infrastructure helps it select long/short equity strategies and evaluate stocks using 10 times more factors and qualities than it had previously been able to do.
“Until now, in one strategy, we would typically combine five-to-ten factors,” said Hauptmann. “Now, we are able to combine more than 50 and up to hundreds of factors to make the right investment decision on one stock.”
“It is compelling because it helps us identify high-alpha stock profiles that we missed before because of the biases we had when combining information. Machine learning has improved our work as systematic investors. We now have a more complete picture of all information, and we can leverage all our data sources.”
For investors, this means better, more diversified access to all the inefficiencies captured by RAM, which, according to Hauptmann, has an additional alpha engine in its portfolios that is able to “capture a new set of attractive opportunities”.
“When we first introduced this type of technology in our process, the exercise was precisely to see whether we were missing names that were potentially high alpha,” he explained. “Now we can identify and add to our portfolios between 80 and 100 of these high-alpha names, depending on the region. This diversifies our risk exposures, increases the alpha of the book, and it is actually also reducing the turnover in the portfolios.”
However spellbinding the technological capabilities, machine learning will not lead to the removal of the human element or the replacement of portfolio managers with banks of computers.
“We are using machines and technology to enhance our ability to uncover alpha and uncover opportunities – but it still starts and finishes with the human,” de Saint-Seine stressed.
“It’s not, as some think, purely statistical and purely based on maths. We are just trying to use the machines and technology to make our work more efficient and to leverage the power to look at a broader set of opportunities. There are still a lot of parameters and a lot of design based on the discretion of the human being, our managers.”
Rather, in a fast-changing global environment where responsible investing makes financial as well as moral sense, machine learning provides an unprecedented opportunity for man and machine to work together towards a common good and a more sustainable future.
“Our objective is to keep searching for new data sources with the aim of further improving our capture of market inefficiencies,” Hauptmann added. “Looking ahead, our objective is to increase the size of our research team to keep up with the current exponential growth of data, leveraging our machine learning platform even better.”
This is a sponsored article from RAM Active Investments.