Active managers continue to face strong headwinds for a couple of reasons. There is a strong market which is driven by liquidity on steroids. Then there are passive funds with dramatically low management charges, and the less than the ordinary performance by a large segment of fund managers haven’t helped either. However the final nail on the coffin has been the cavalier, but well-directed actions by regulators. These headwinds have exposed the weakness and inertia of many industry participants.
Investment management industry has the presence of many large investment institutions, some of them in existence for over a century and some more recently. These institutions have established investment practices which is considered as a holy grail for successful investing. However, these Investment research methodologies have not seen any innovation for many decades. Technology has made it possible for the perfect implementation of such research methodologies, to add to that information and data is now freely available and easy to access. Even retail investors have the same access to information and data as most institutions.
Active managers have to innovate to find their way out of this mess. They have found their path to redemption by implementing emerging new approaches to management using input and insight made possible by harvesting the ever-increasing flow of digital footprint of most aspects of various businesses and industries. This involves collecting and organising these big-data sets, and setting up advanced analytical techniques.
There are different definitions of Big-data floating about, however, one definition that brings out the true essence describes it as unstructured sets of data that are so large that traditional data handling tools are not equipped enough to process or analyse it. Three aspects that are key to understanding big data are: volume, variety and velocity of data.
Some asset managers have started to aggregate, a multitude of such data sets. These are data sets containing credit card transactions, satellite imagery, and others that give hints of emerging trends about different aspects about a company or industry prospects.
These large sets of data, if analysed properly, can provide patterns and trends that give valuable insights. In order to get these insights, advanced analytical tools such as machine learning, natural language processing, and robotic process automation are deployed.
Asset managers have so far been able to make a difference on investments either through unique insight or through unique information (which may or may not be publicly available). This situation has undergone tremendous change due to technology and in some respect strong actions taken by the regulators. Asset managers while continue to follow various fundamental, technical and quantitative approaches, they are now seeking a unique understanding of underlying trends and sentiments relating to various key factors that affect company performances and valuations.
Machine learning makes available a plethora of possibilities for asset managers to process information and data in the right way and hence obtain these valuable insights.
Natural Language Processing, like machine learning, is a subset of artificial intelligence, capable of understanding human language as it is spoken. Human language and communication are tricky as it is often not literal. NLP enables the machines to understand human conversations like another human does. NLP can help gauge investor emotions in the market based on their interaction amongst themselves.
There are algorithms that have been developed to identify the common themes that maybe more relevant for a particular quarter for companies in similar industry lines. These can be done by textual processing of earning call transcripts.
An NLP search can sift through market conversations and look for either positive or negative words. These words can then be attributed to a positive or a negative sentiment in the market and decisions can be taken based on that. On the back of such capability, asset managers are able to identify specific sentiments in the management commentary in their statutory filings.
An algorithm can then evaluate the best momentum signal (derived from market performance over recent time periods) for predicting future market performance or to try to predict how much the market will move if there is a sudden spike in inflation.
Similarly an algorithm could find that, at a certain point in time, the market is being driven by various factors, such as momentum factor, energy prices, level of US dollar, and liquidity.
Another application of machine learning is in the area of identifying relevant news along with specific hint of sentiment in such news articles.
Big data and advanced analytics are powerful tools that can catapult a firm to greater heights and present great possibilities, but the implementation of big data strategies and employing advanced analytical tools for day-to-day operations pose a few challenges.
IBM estimates that 90% of the world’s data was created in the past two years. In this age where infinite amounts of data are being generated every moment, it’s important to know where to access the most potent and useful datasets from. Asset managers, while accessing alternative data generated by clients should be careful that the data is unique and is being accessed without any breach of privacy and that the source of data is reliable.
Majority of strategies and models in the asset management space are based on market signals. After some time, the decay effect of signals starts taking over as competitors adjust and respond to the same market signals. Hence for the continuation of efficient operations, it is essential to review and refine big data strategies from time to time.
Much has been talked about big data and advanced analytics but there are still a lot of blanks that need to be filled on the practical, application-based knowledge front. A recent survey conducted by industry service providers showed that, over 70% of the respondents believed big data investments as ‘Very Important’ or ‘Somewhat Important’. However, about 50% of the respondents did not have adequate big data capabilities or were in the initial stages of building their big data capabilities. As an evolving field, the big data industry suffers from a lack of manpower adequately equipped with the skills to navigate the space of big data and advanced analytics and get desired results.
An asset management firm’s current pool of employees are highly experienced analysts and fund managers who have been working using various resources that have been available for many years. These employees now need to adapt to the new environment. This will give them the ability to add insights from new sources of data, while applying their own in-depth understanding of business models and the industry. Achieving this transition can prove to be the most challenging task.
To sum it all, the asset management is in a nascent stage of big data and advanced analytics adoption. While some of these challenges seem daunting however, they are certainly not insurmountable. The dynamics of the asset management industry is ripe for a big dose of innovative change. And these will be facilitated by numerous ancillary technology companies that will play an important role in this evolution.