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Equity research platforms in the digital age

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Satya Iluri

Vice President, Wealth Management Consulting

June 27, 2018 - If you are an asset management firm with a strong and stable asset base, it is imperative to understand the tectonic shift that digital technologies are causing in the field of research – and to take necessary steps to protect the franchise.

Like all other sectors in the economy, the digital age is disrupting equity research and transforming the foundation – mostly for the better. Recent developments and innovations have made processing data and analyzing it to glean insights more accessible and affordable than ever before. In the past, solo analysts were superstars steeped in industry lore, known for their stock-picking prowess, solid Rolodex of contacts, and skill crunching numbers in Excel (or in their minds). Not anymore.

Market data exclusivity ends

Unparalleled access to data has traditionally been the privilege of asset-rich financial firms, with data from vendors like Bloomberg, Standard & Poor's, and Reuters only available to those who could afford to spend a lot. The information gap is now a thing of the past as more and more vendors – with diverse commercial models – have entered the financial data space. Many of these vendors have the ability to serve the needs of data customers with on-demand APIs, along with the flexibility of providing pay-as-you-go solutions and less strict policies surrounding long-term subscriptions and cancellations. Technology is making data more available than ever before, leading to more competition – particularly from boutiques, independents, and individuals.

Information democratization

Once upon a time, predicting the future performance of a stock required knowing the right people in the right places to get the “intel." Today, boardroom leaks and backroom tips – once as insidious as they were pervasive – have been reined in, thanks to evolving standards, internal compliance, and regulations. (Of course, regulations have a dark side as well.) With a more level playing field and better access to information than ever before, success now depends less on who you know, which country clubs you belong to, or the circles you associate with. That's because the data is available for everyone to see. Now, how the data is used to glean insights and make the right forecasts is where an equity analyst can aspire to generate alpha.

Big data changed the research game

The ability to not only acquire and access multiple streams of data – both structured and unstructured – but also thoroughly normalize it, making it available for downstream analysis and recommendations, is a game changer. Internal data, social listening, news feeds, regulatory filings, social, cultural, and demographic trends – an amalgamation of disparate data combined with advanced visualization leads to the discovery of paths and patterns previously not possible.

Machine learning, Internet of Things (IoT), and AI

The mention of robotics and artificial intelligence conjures up a common image of robots taking over production or manufacturing jobs, but intelligent machines are beginning to have an impact on the Fintech space as well. The latest generation of intelligent agents can work as stock pickers, using advanced analytics to make decisions without the burden of human emotion or stress getting in the way. Lower costs for investors, without any change in the quality of the work performed, could lead more firms to adopt software and robotic models, replacing their human counterparts for good. Or perhaps co-existence, with human instinct supported by intelligent machines, will become the norm.

Using machine learning and other artificial intelligence models, the ability to not only analyze but parse and glean insights has accelerated the pace of change in equity research. Plus, incorporating new data sources like mainstream media news, social listening, and sentiment analysis, in addition to traditional sources like company information, adds to the value derived from ML and AI. Imagine if the obituary of a leading scientist could be used as one data point in a model that predicts the future fortune of a biotech stock. Or for that matter, the number of mentions – within the right context – of a brand among the digerati. If a product or a brand is blazing hot on social media, can the stock be far behind?

With satellite imagery and IoT, equity analysts can develop a new real-time source of intelligence. Images of a store parking lot in a mall in Kalamazoo, MI may send signals of the foot traffic to a store. For instance, it's possible to know many trucks are shipping inputs or carrying finished goods using satellite imagery and connected devices. Combine this with in-store beacons and the stream of data from other IoT devices and there is a treasure trove of location-based information, which will be a key input to equity research in the near future.

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Transforming the equity research function

Here are seven considerations to leapfrog your equity analysis and research management function into the digital age.

  1. Rethink the research function: How you hail a cab and consume your music has changed dramatically in recent years, so why should the new generation research function be a replica of the past? Design thinking can help you reimagine and reinvent the research function to accomplish the true value proposition – generating alpha or whatever is the true north star of the function.
  2. Unified data: Siloed systems and applications in the IT landscape do not allow the participants in the research management function to accomplish “data to decision" in one optimized flow. Hence, it is imperative to combine internal and external data, and structured and unstructured data, and normalize it into actionable intelligence for downstream systems and participants.
  3. A patchwork of systems: One of the challenges in the past has been the proliferation of a patchwork of equity analysis systems – many of them with underlying proprietary data. Terminals for data access, pricing feeds, spreadsheets (shadow IT) for analysis, email for communication with portfolio managers, multiple content management systems, and most often a standalone visualization software – all of these examples come to mind. Instead, asset managers should look at the entire “idea to recommend" value stream of research management as a holistic process and address the needs of each step that fosters an optimal user experience.
  4. Empowering the analysts: It is inevitable that artificial intelligence and machine learning will play an integral and increasingly important role in equity research and stock picking. (Or it is possible that all the competing intelligent agents, devoid of emotion and equipped with unprecedented data processing and analytical skills, could make the markets more efficient – and result in perfect pricing?) Adopting AI or ML into the equity research process does not necessarily mean pink slips to the equity analysts and portfolio managers. In fact, we believe that the native intelligence of the analysts and portfolio managers could benefit from the artificial intelligence as an overarching input – one that processes all the noise and highlights the right signals.
  5. Design as the differentiator: It is not just consumer front ends that require a different design ethos. B2B systems used by professionals can transform into powerful tools by adding a touch of design excellence. Imagine an equity management solution designed front to back with the right visualization, on-page information architecture to focus on key information sets, intuitive workflow, and seamless collaboration – all while being transparent to the underlying complexity of the data and systems architecture.
  6. Dynamic workflow with role-based views: As an example, let's say that a single stock might be a part of a dividend income strategy comprising a growth-at-a-reasonable-price strategy, a sector strategy, and a value strategy. Each portfolio manager may look for specific nuggets of information on which to base their decision to include or retain the stock in their respective portfolio. For example, the signals that matter to a quant will differ from the signals that matter to a technical analyst, which in turn will differ from the signals that matter to an analyst focused on traditional fundamental security analysis. A system that can provide a role-based view and accommodate a flexible workflow will not only help with decision making but make the process efficient and effective.
  7. Digital as the transformation catalyst: Digital technologies have become essential levers for attaining competitive advantage. For example:
    • Chatbots including conversational AI where analysts can ask questions in natural language
    • Natural language generation to aid in the process of compiling a research report. The intelligent machine can provide the raw material, and the analyst can craft the narrative and the tone
    • Robotic process automation (RPA) can play a role in compiling manual and laborious information, with rules and triggers to activate specific processes
    • Advanced visualization and data science to help identify patterns and paths
    • Computational linguistics, including the ability to understand the meaning and context of natural language and convert that into usable data

This blog is co authored by Satya Iluri, Vice President of Wealth Management Consulting and Prabod Sunkara, Assistant Vice President of Walth Management Consulting.