The world is moving towards industry 4.0, and with the introduction of approaches like Ensemble Active Management, the investment management industry doesn’t seem to have been left behind. Ensemble Active Management or EAM is the result of a technology-backed investment management process powered by Artificial Intelligence (AI) and Machine Learning (ML).
It combines one of the core components of machine learning - ensemble methods with financial data to develop EAM portfolios. While there might be some skepticism about the implementation of AI and ML in the investment management industry, a whitepaper by EAM Research Consortium tells the opposite.
The research took into consideration close to 30,000 EAM portfolios distributed over a period of time to statistically compare them with the traditional models of investment decision-making amongst mutual funds and the S&P 500 index.
The result? The EAM portfolios had an upper hand over traditional actively managed funds.
Quoting one of the key findings of the whitepaper, “EAM Portfolios delivered a risk-adjusted-return (as measured by Sharpe Ratios) that was 17% greater than the S&P 500 for rolling 1-year periods, and 22% greater for rolling 3-year periods.”
Machine learning-based investment management approaches like EAM take into consideration the insights and data management capabilities of the algorithms to get closer to making accurate predictions. Thanks to how the technology fundamentally works, machine learning learns and adapts to the patterns it recognizes along the way, making it a far better companion for investment decisions than solely human-coded software.
But how is the investment management industry adapting to the Machine Learning revolution? Are we getting closer to making it a part of our daily operations? Or are we still skeptical about the power it holds?
Let’s take a closer look at some of the real-life examples of machine learning adaptation in the investment management industry and decide for ourselves. These are the companies who are using Machine Learning to support their investment management processes.
MAN AHL
Uses ML to enhance trading strategy and investment management.
Man AHL is one of the most active investment management firms that trade various hedge funds and long-only investment strategies. It has used ML in the execution process that is similar to all the MAN AHL strategies. It develops a definitive route to track trade through intelligent algorithm adaptation.
Till now, the firm has gained success through ML in two major categories:
Developing trading strategies - Basically, algorithms that generate trade and provide guidance in when to sell a trade.
Enhancing execution efficiency of such trades - It includes delivery and completion of the process in the financial market.
One key point here is that ML does not work on undirected search, rather it helps modify features to extend both alpha and diversification. It is true that manually directing and analyzing trade predictions becomes doubtful, which is why MAN AHL is using algorithms to provide accurate and portfolio-based directions in trading. They have levelled up and sought more growth to optimize future cash equities.
New York Life Investments
Uses ML to generate signals for quant models.
The company is known for managing assets for the New York Life Insurance company. People from finance and economics backgrounds are managing multi-asset solutions for global players, thus the job comes with great responsibility. Every decision process requires a blend of quantitative and fundamental inputs. 4 processes include cycle, value, momentum, and sentiment where predictive analysis is used through ML techniques to serve reliable and faster work-flow systems.
Through adapting to ML technologies, the company can now:
Track extensive global player’s investment portfolio lucratively.
Shifts through large data flow occurring in the market.
Effectively affects how they work with data and strengthen the development process.
ML has enabled them to manage large volumes of data while improving prediction accuracy and generating value signals to resolve the monthly outlook of the firm.
Goldman Sachs
Uses ML to leverage data analysis in Sell-Side Research
Known as the leading investment management firm, it is dedicated to strengthening the research data strategy team to bring efficiency to work. It works with equity and macro research analytics from around the globe to serve them with qualitative analytics and quantitative skill-sets.
Through a GS aggregate tracker system, the company is achieving various sell-side objectives for an investor such as:
Discovering their domain expertise.
Accessing all the relevant information with traditional and alternative data sources.
Tapping relevant insights through advanced analysis techniques.
The mechanism is helping the strategy team to solve the first step in the investment process that is the research use case. Also, the use of ML has helped their teamwork more confidently and saves time. Overall, it is aimed at improving the investment strategies through meaningful insights.
R.E. Lee Capital
Uses ML to maneuver investment portfolios
R.E. Lee Capital provides discretionary portfolio management services for high net worth clients who are looking for a unique approach to investment management.
Using Artificial Intelligence, R.E. Lee Capital’s data-intensive and quantitative investment decision process helps to take the stress out of investing by making every decision easy and automatic with no bias or emotional attachment.
R.E. Lee Capital deserves a proud spot on this list as a Smart Solutions’ client. In a recent webinar, Bao Vu, Investment Director of R.E. Lee Capital shares his insights on how they’ve leveraged RoboInvestor and integrated machine learning into their investment process for reliable decision making and strategizing.
Bao has considered the ‘Beta’ model for Machine Learning to maneuver investment portfolios and the results are impressive.
American Century Investments
Uses ML for sentiment analysis in conversations.
It is almost a 50-year old company and manages assets for various financial stakeholders like investors, institutional clients, and financial intermediaries. They used ML as a key component in developing sentiment model analysis for languages during the conference call. It majorly works on omission, spin, obfuscation, and blame. This way, they recognize the unique style of a given management team member.
The spectacular use of machine learning in developing such a model which is backed by psychology is a tremendous innovation. It has helped in:
Adding a lot to identify investment decisions and track the probability of the investor decision.
Directing and harnessing team managers to seek the best results in their strategy.
With technologies such as machine learning in place, investment teams can amplify productivity while making the most of their strategic roles.
Acing Financial Technology, Where We Are?
The above-mentioned companies use ML to better their processes, team management, work efficiency along with investment strategies.
As a company dedicated to helping investment managers leverage machine learning for smart decision making, Smart Solutions has come up with RoboInvestor - a platform designed to simplify and trim financial complexities, and extend diverse tailored solutions with Machine Learning algorithms. RoboInvestor helps investors in managing, expanding, and diversifying investment plans, and thus helps you think beyond human errors when it comes to investing.
Thanks to the groundbreaking technology that backs it, Roboinvestor comes with some incredible features that are more advanced and reliable for any investor, trader, and fund analyst in the market today.
The players in the investment industry, like the ones above, are moving towards the adoption of AI and Machine Learning. It’s definitely a bright-looking future for the Machine Learning and Finance Industry to build ML-based investment management models for decision making.
For more, explore our services or book an appointment with our experts to gain clarity.