We don’t envy the challenges investment management firms face daily. 

Complex regulatory requirements. Data-intensive business models. Competitors introducing disruptive technologies. Legacy system incompatibility and sprawl. It’s enough to cause stressful days and sleepless nights for leaders tasked with driving innovation and solutions around these issues.

Many cloud-based technologies provide frameworks that allow innovation leaders, CIO’s, research directors, and portfolio managers to scale solutions for their business needs more effectively and efficiently than on-premise (“on-prem”) solutions can offer. But even with clear-cut advantages provided by cloud solutions, concerns can arise regarding security, uptime/operational resiliency, and how cloud technologies map to regulatory guidelines.

These tradeoffs need to be evaluated by a firm based on their operating model, with evaluations taking into account both off-the-shelf and bespoke software options (no wonder that the global software consulting has become a $200 billion industry!)¹

Unlocking the Power of Investment Research with Cloud Technology

Investment management companies know that the core driver for their firms’ performance is their investment research: sound research drives new investment ideas. When done well, research can help firms capture that elusive alpha. 

As a result, when narrowing our focus to evaluate most investment managers’ research management processes, the value a cloud-based approach can provide starts to emerge. Leveraging the power of cloud computing as part of a research management system (RMS) can: 

• Drive significant workflow efficiency improvements across research and investment teams
• Create a centralized, standardized platform for collaboration, regardless of team member locations
• Provide real-time visibility into investment theses for key internal stakeholders (eg, portfolio managers)
• Deliver a single, secure source of truth to guard IP and streamline compliance reporting preparation

In the evaluation process, we don’t want to downplay the importance of addressing any concerns that may result from a move to the cloud vs legacy on-prem strategies. When assessing RMS vendors who’ve transitioned to the cloud, due diligence should include understanding a vendor’s:

• Data protection and access controls, both while in transit and at rest
• Standards for high availability, especially for global organizations
• Information security and infrastructure designed around risk management best-practices
• Compliance with global security standards

Takeaways

Your research team likely developed an organic approach to getting their work done; we’ve seen it with many companies. But more often than not, these approaches have either relied on clunky internal systems or are patchworked across multiple, disjointed software programs that provide limited visibility across teams.

Cloud-based RMS technologies have emerged that can help with creating more efficient workflows, improved collaboration, and better visibility of research outputs for organizations. These tools can be an important, high-impact step in an investment manager’s digital transformation journey. Take time to do your due diligence and find your best fit, and you can unlock more of your research team’s valuable insights.

1. https://www.thebusinessresearchcompany.com/report/software-consulting-global-market-report

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This month’s release focuses on creating better integrated workflows while also helping customers get proprietary data off their desktops and into the Sentieo platform to unlock powerful insights.

In our latest release, we are introducing:

Sentieo Connect to Leverage the Power of Your Firm’s Data

Sentieo Connect is a powerful new feature that allows users to upload and integrate their proprietary and 3rd party data, including items like analyst-driven estimates, price targets, and other model outputs, as well as portfolio holdings driven out of portfolio accounting systems.

Once uploaded, analysts and PMs can use their data alongside Sentieo-sourced data to create better integrated workflows and more seamless monitoring of investment universes and portfolios using tools like Dashboard, Screener and Plotter.

Expanded Ticker Type Support for Watchlist Imports

We’ve enabled our Watchlist Manager with support for a broader list of ticker types that can be utilized in CSV uploads, allowing users a quicker path to building and using robust watchlists.      

Open Access Beta for New User Experience

As Sentieo continues to look for ways to create better user interactions with our system, we’ve undertaken a platform design initiative, with the goal of making the overall experience more seamless, the journey more contextual and the design more consistent.

Users now have the opportunity to try out the new experience for yourself, as we’ve opened up the beta environment (named Vega) to all users. Try it out today and let us know what you think!

If you are a current Sentieo user, log into Sentieo to read the April 2022 release notes. If you’d like to learn more about the Sentieo Platform, contact us today to meet with a solution executive.

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So far, we’ve explored how vast information landscapes have led to information overload and how AI is helping cut through the noise.

Today, we’re going to take a look at how a centralized research approach can help firms leverage technology to improve their workflows and transparency.

Power Up Research Workflows to Stay Ahead of the Pack

Today’s markets move on news at lighting speeds, so the ability to discover, digest, develop and deliver research insights can be the difference between generating alpha and dealing with performance lags.

The graphic below highlights some of the ways AI and other technologies are impacting and improving the research process.

Making the Case by Making a Plan

While AI tools may seem like the perfect solution to the information overload suffered by many analysts, poor implementation can add to the burden. As with any digital transformation project, firms must fully understand what problems they’re addressing and the capabilities of their people.

Additionally, having a sound change management strategy in place is important so analysts know why and how you’re introducing a new solution. A well-thought-out approach should also consider how to ease any analyst concerns about technology eventually replacing them.

If you’d like to learn more, we dive deeper about putting AI and technology to work in your research process in our blog post here or download the full briefing paper here.

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This time around, we’re exploring how technology is helping to manage this problem it helped create. 

AI, machine learning, and natural language processing have led to the development of tools analysts can use to overcome research process efficiency challenges and supercharge the hunt for alpha. While none of these tools replace humans, they augment the decision-making process and ultimately help to build more robust investment cases. 

Download full graphic.

Emotions like overconfidence or fear can lead to significant inefficiencies that provide a valuable alpha source. Sentiment analysis tracks what executives are saying about a company, which can be compared to competitor statements or other analysts’ commentary. 

Sentieo partnered with the Harvard Business Review (HBR) to publish a briefing paper highlighting how inefficient research processes can hinder a firm’s pursuit of alpha. It also explores the idea that while technology is part of the cause, intensifying the flow of information, it can also play a role in the solution.

If you liked what we covered today, feel free to check out the blog post with further insights on this topic or download the full briefing paper here.

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Sentieo partnered with the Harvard Business Review (HBR) to publish a briefing paper that highlights how inefficient research processes can hinder a firm’s pursuit of alpha and while technology is part of the cause, intensifying the flow of information, it can also play a role in the solution. 

One of the key foundational themes of the paper: the challenges that analysts face when researching investment opportunities. For emerging funds, these challenges are often heightened due to their impacts on smaller research teams.

Download the full infographic.

But a good investment team knows that information inefficiencies present opportunities for alpha. As a result, if you can create a more efficient method for your analysts to move through the process from idea generation to investment recommendation, your firm can establish a competitive advantage.

Today, the evolving landscape of AI, machine learning and natural language processing technologies opens new paths for emerging investment funds to level the playing field with larger competitors. We’ll explore this in our next insight piece.

In the meantime, feel free to check out the blog post on this research or download the full briefing paper here.

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Integration, flexibility, and control continue to be important criteria for institutional investors that are choosing the Sentieo platform as the central hub for analysts to do their work.

In our latest release, we are introducing:

Microsoft Teams Integration 

In our continued effort to integrate Sentieo with popular cloud storage and collaboration services, we are rolling out our Microsoft Teams integration. Users can leverage the power of Teams for communication and collaboration while making it easy to seamlessly search, share, and discuss Sentieo notes and documents directly in Teams. 

New Notebook Reports

Admin and compliance users can now generate reports on demand for notebook activity and research report readership data.

Saved Search Alert Digest Emails

Users can now increase the frequency of alerts for the searches most important to them without cluttering their Inbox with custom digests for saved search alerts.

If you are a current Sentieo user, log into Sentieo to read the November 2021 release notes. If you’d like to learn more about the Sentieo Platform, contact is today to meet with a solution executive.

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Following record revenues in 2020, global demand for our AI-driven financial research platform continues to grow. To support that momentum, today we’ve announced an expanded presence in the European market.  With more than 50 active customers in the region, including Schroders, Kames, S.W. Mitchell, Cape Capital, Amiral Gestion and Ownership Capita, we’ve doubled our team in the UK and have plans to double the sales team again in the next 12 months.

Our objective is to help customers digitize the financial research workflow and transform their investment research process into a competitive advantage. Featuring a cloud-based solution that incorporates innovative AI search and sentiment analysis, market and alternative data, modeling and analytics – all supported by an integrated research management system – we are empowering customers around the world to modernize their research workflows and drive analyst collaboration for more powerful investment analysis.

This commitment to technology innovation has also garnered two industry awards in the European market. In March, Sentieo was named Best AI Technology Provider in the 2021 Hedgeweek European Awards and in July we were recognized by HFM European Technology Awards as the Best Research Management Solution.

Sentieo is actively hiring across all departments and geographies. To view the available openings, visit sentieo.com/why-sentieo/careers/.

For more details on our EMEA expansion, check out our press release. To learn more about the Sentieo platform, visit https://sentieo.com/product/.

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Sentieo’s powerful and flexible Dashboards have grown dramatically in both client adoption and functionality since we introduced them two and a half years ago. Showcasing the depth of the research platform, the Dashboards bring together documents, document search results, research management/research monitoring, financial and alternative data, data visualizations, Tableaus, iFrames, and much more, through over 50 different widget types. 

Sentieo clients can build their own Dashboards, and have access to pre-built general as well as industry- and function-specific Dashboards. Over time, we have rolled out specialized Dashboards such as the:

• 39-widget “Fundamentals X-Ray” (video walk-through)
• Special Situations (video walk-through)
• Credit (video walk-through)
• ESG Topic Monitoring (video walk-through)
• Sustainalytics integration (video walk-through and joint webinar)

The new Risk Monitoring Dashboard (available to clients upon request) provides a comprehensive look at financial markets conditions across a variety of metrics, from macro, to credit, to equities, as well as aggregate NLP sentiment from transcripts, document trends, news, technical analysis, and more.

In the partial screenshot below, we display six out of the eighteen different widgets. Clients can add and delete widgets to customize further as well as share the dashboard internally with their team.   

For a detailed video walkthrough of every indicator included, please watch this video: Video: Sentieo Risk Monitoring Dashboard

If you are interested in building your own data visualization Dashboard in Sentieo, this is the “how to” video (including Sentieo’s data visualization engine Plotter, Tableaus, and iFrames): Video: Adding Data Visualization Widets in Sentieo Dashboards

Learn more about Sentieo’s data visualization capabilities in our Basic and Advanced Data Visualization webinars, or contact us to schedule a demo.

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In the first two blogs in this series on turning investment research into competitive advantage, based on a briefing paper we sponsored with the Harvard Business Review, we discussed some of the challenges analysts face when researching opportunities and how tools leveraging artificial intelligence (AI) can provide a solution.

In this final blog, we explore how AI can improve workflows and best practices for putting these tools into action.

A central source

Analysts are probably more interested in the potential applications of AI tools like semantic analysis, topic modeling, and named entity recognition rather than the science behind them. After all, the way they add value is through ease of use and by enhancing the research process.

Take an investment thesis, for example. Once an analyst completes a thesis, in many cases, it’s saved in a folder on the company server and then sent to a portfolio manager attached to an email. Tracking it down months later because a relevant opportunity has emerged is a waste of an analyst’s time. 

A centralized platform which uses AI to curate theses, notes, and presentations, is a much more efficient way of storing research output. Hosting the platform on the cloud means that analysts can upload or retrieve information from any device, while it also gives research directors a real-time overview of the team’s workflow. Incidentally, cloud computing has become an essential technology during the pandemic- spending is expected to increase by 30% this year across all industries, according to Forrester Research.  

A centralized platform also helps develop a more robust investment thesis. When a firm produces research on two companies within the same industry, variations in the approach taken by the analysts could lead to missed opportunities or, even worse, poor investment decisions. But when that information is presented in a standardized format, it’s more likely to highlight qualitative differences between the companies rather than inconsistencies in the way the analysts work. 

A regulatory paper trail

Staying on the right side of the regulatory authorities, especially in a sector as heavily regulated as finance, helps firms avoid hefty penalties issued for compliance breaches and prevents the inevitable damage to their reputation. A centralized platform effectively creates a paper trail, so if the regulator asks a firm to justify an investment decision or a response to a corporate action, the relevant information can be quickly and easily accessed.

Making the case

While AI tools may seem like the perfect solution to the information overload suffered by many analysts, poor implementation can add to the burden. As with any digital transformation project, firms must fully understand what problems they’re addressing and the capabilities of their people.

Having a change management strategy in place is important so analysts know why and how you’re introducing a new solution. It should also ease any concerns about technology eventually replacing them.

But don’t let these considerations allow your firm to delay making a decision- those that fail to embrace the latest innovations risk losing ground to their more tech-savvy competitors.

Download the full briefing paper here.

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Technology may have contributed to this overload, but it can also help to manage it. So in this post, we’re going to explore how advances in AI, machine learning, and natural language processing have led to the development of tools which analysts can use to overcome these challenges and supercharge the hunt for alpha. While none of these tools replace humans, they augment the decision-making process and ultimately help to build stronger investment cases. 

Profit and margin are two key pieces of information when researching a company. But how many other terms can be used to describe the same thing? Profit has 45 synonyms according to thesaurus.com, and margin has 30. Different meanings typically cause programmes designed to do investment research to return a lot of useless information from scanned documents. However, semantic analysis algorithms understand sentences by breaking down their structure and recognizing combinations of words, making sure analysts only receive the most relevant results. 

Analysts can use a process called topic modelling to uncover major themes within a document or a set of documents. Algorithms can be trained to scan pages of text and detect patterns or recurring words to reveal the topics that come up most frequently.  

AI tools can crunch numerical data as well as textual data. They automate the tasks of identifying trends hidden in the numbers and comparing a company’s results with previous financial years or their competitors. Eliminating the need to copy and paste figures from one spreadsheet to another and having to ask a colleague to double-check their work for accuracy, means analysts can focus their efforts on processing the data and uncovering investment opportunities they might have otherwise missed.  

Certain employees are critical to a company’s success. While the passing of Steve Jobs was widely mourned, the departure of Apple’s Chief Design Officer Jony Ives didn’t make as many headlines, even though he played an intricate role in the design of the iPhone (not to mention the company’s other products). Analysts can now keep track of executives and employees as they move from one job to another using named entity recognition, which takes into account variations such as middle initials or misspellings.   

Market sentiment has a major influence on share prices. Emotions like overconfidence or fear lead to inefficiencies which provide a valuable source of alpha. That’s why many analysts are keen to capture the mood of investors. Sentiment analysis tracks what executives are saying about a company, which can then be compared with statements from competitors or commentary by other analysts in the financial media or on social media.  

In the third and final blog post in this series, we’ll explore how AI can improve workflows and some best practices for putting these tools into action. Alternatively, you can download the full briefing paper here. 

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Sentieo recently partnered with the Harvard Business Review (HBR) to publish a briefing paper explaining how research powered by artificial intelligence (AI) can provide a source of competitive advantage for investment firms. The paper highlighted how inefficient research processes can hinder a firm’s pursuit of alpha and while technology is part of the cause, intensifying the flow of information, it can also play a role in the solution.

In this first of a series of three blog posts summarising the paper’s findings, we explore some of the challenges that analysts face when researching investment opportunities.

Information overload

Companies across all industries struggle with the sheer volume of information they must process. Research by HBR in 2019 showed that 82% of respondents struggled with analyzing unstructured data and 77% relied on manual methods. The burden is even greater in the investment industry due to the regulatory demands of listed companies. The Securities and Exchange Commission alone processes around 3,000 filings each day.

Many analysts also need to research industry trends to build a credible investment thesis. That means combing through additional layers of information. For instance, one database of North American manufacturers covers half a million suppliers and has published 300,000 articles.

All this information comes in different formats, which makes it hard to collate. Analysts typically monitor a range of sources when researching a company. A news story on the Wall Street Journal may prompt an idea which requires further investigation through search engines, data terminals and reports. That’s why the information is known as unstructured- it comes as spreadsheets, PDFs, videos and even social media posts.

In the absence of a central platform, most analysts build their own system to track developments, manage the information they gather and share it with colleagues. How much of it gets lost in the process is anyone’s guess.

To uncover insights, analysts must spend countless hours sifting through the information manually. But that’s hardly an effective use of their time. Minor distractions, such as searching for an email, on their own may not cause much disruption, but they add up. Valuable nuggets of information could also be hidden in instant messages, Slack channels and transcripts from virtual meetings. These distractions eventually affect an employee’s happiness and productivity, especially when that employee is a highly qualified analyst.

You didn’t hire these well-paid individuals to organize information, you hired them to analyze it and come up with actionable ideas which will help your firm deliver benchmarking-beating returns. After all, information inefficiencies lead to alpha which sets active managers apart from passive investments like exchange-traded funds and index trackers. This added value has become particularly important as money flows out of actively managed funds and into passive funds.

Analysts need access to tools to do their job more efficiently, but the investment industry has traditionally been slow to adopt new technologies. So in the next blog in this series, we’ll explore how AI, machine learning and natural language processing can help to ease the burden of information overload. Alternatively, you can download the full briefing paper here.

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The State of ESG Research Management

With public, investor, and regulatory pressures rising to integrate environmental, social, and governance (ESG) principles and transparency into investment research and decisions, institutional investors are struggling to find, analyze, and centralize the deluge of information provided by companies and ratings organizations. 

In fact, research conducted by McKinsey in 2019 “highlighted that while 90 percent of companies report on sustainability, only 15 percent of investors can successfully integrate this information into their investment decisions.”

The lack of ability to effectively and efficiently integrate ESG factors into investment research workflows leads to a significant amount of time wasted by expensive ESG and fundamental analysts, missed insights that could deliver better results, and a lack of confidence in ESG-driven investment decisions.

ESG Investing: Mainstream & Delivering Results

ESG investing has come a long way since Who Cares Wins, the report that made the case for integrating ESG factors into capital markets, was published in 2005.

Long considered a niche investment strategy, ESG investing has become mainstream, with assets under management using sustainable investment strategies on the rise across the globe:

The US SIF Foundation’s 2020 biennial Report on US Sustainable and Impact Investing Trends found that sustainable investing assets account for $17.1 trillion or 33% of the total U.S. assets under professional management–a 42% increase over 2018.

PwC Luxembourg’s late 2020 report, The growth opportunity of the century, called ESG a “paradigm shift” with European ESG assets forecast to make up “41% to 57% of total fund assets in the region, compared to the 15% of assets ESG accounted for at the end of 2019.”

In 2019, research from The Economist Intelligence Unit found that 76% of Gen X and Millennials in the UK say “it’s increasingly important to consider ESG factors when investing.”

And from Morningstar to Morgan Stanley, JUST Capital, RBC Global Investment Management, and Goldman Sachs, research shows that ESG investing pays off in terms of performance and returns.

ESG Research Obstacles

Despite strong returns and rising interest in ESG investing, institutional investors continue to struggle with the best way to confidently integrate ESG factors and research outputs into investment decision-making processes.

Proliferation of documents and data

The growing amount of financial and non-financial data points and documents that need to be consumed by ESG analysts and the typically small teams doing primary research is overwhelming, requiring complex fact-finding exercises and use of multiple tools and channels for sourcing from:

• Publicly-traded companies
• ESG ratings organizations (such as Sustainalytics, JUST Capital, and MSCI)
• Sell-side research
• Industry oversight groups

Lack of transparency and standardization

There are no standards for corporate sustainability reporting and disclosures as there are for financial disclosures, making it difficult to validate claims and reconcile this information to make comparisons between companies.The same thing applies to the growing body of third-party ratings, all of which have their own methodologies for evaluating a company’s track record on ESG factors.

Fractured research workflows

With disjointed processes and systems used to conduct and track research across teams of ESG and fundamental analysts, capturing and auditing all of the source material and engagement interactions–internally across teams and externally with company management–is time consuming and resource intensive at best and nearly impossible at worst.

Regulation on the Rise

When it comes to transparency and standardization of methodologies and reporting to support ESG investment strategies, legislators and regulators are starting to step up, particularly in Europe.

Europe’s SFDR Deadline Approaching

The first major milestone in the European Commission’s Action Plan on sustainable finance is swiftly approaching, with a March 10, 2021 deadline for compliance with SFDR, the Sustainable Finance Disclosure Regulation. SFDR imposes transparency and disclosure requirements on institutional investors and asset managers, including:

• Written policies on the integration of sustainability risks in the investment decision-making process
• Information on the methodologies used to assess, evaluate, and monitor the effectiveness of sustainable investments
• Periodic reports on the impacts of sustainable investments

UNPRI Signatories Growing

Some 2,600 global asset owners and managers, representing $103 trillion in assets, have committed to incorporating ESG issues into their investment analysis and decision-making investment processes by signing the United Nations Principles for Responsible Investment (UNPRI). Compliance with UNPRI mandates includes:

• Incorporation of ESG factors into 50% of total AUM
• Centralized capture and aggregate reporting of both internal and external engagement on ESG factors

SEC Exploring ESG Disclosure Standards

With Europe moving full speed ahead on regulating ESG disclosures, the Securities and Exchange Commission (SEC) is looking to catch up with the agency’s investment committee recommending with creation in May 2020 of a framework for ESG disclosure.

A New Approach to ESG Research Integration

Positive performance and returns combined with growing public, investor, and regulatory pressures to integrate ESG factors into investment strategies is driving greater interest in a new, integrated approach to ESG research management that incorporates content aggregation, NLP-powered search, notes standardization, and compliance in a single platform.

Learn more in the guide we put together to help institutional investors explore a new, integrated approach to ESG research management that can overcome the challenges and maximize the opportunities of ESG investing.

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