Tag Archives: Equity Research AI

The research for equity is thus more than simply a recommendation to “buy” or “sell”. It is the basis for capital allocation, fund strategy, and corporate growth planning. In a world shaped by market volatility, regulatory changes, and technological disruption, research enables investors to seize opportunities while managing risks.

The global equity market has evolved to be bigger and wider than ever in 2025. A company recorded that the total market capitalization for global equities reached $125.7 trillion in 2024, representing a 15.5% increase on a y-o-y basis. While this increase speaks volumes, it did so against the backdrop of a drying up of new listings. For context, 1,133 IPOs were raised in 2024, the lowest in five years. The stark contrast makes it very clear why equity research is so critical, really. While the market is growing, opportunities are being funneled into fewer but more dynamic pockets.

Equity Research Market Size & Momentum

Yet, despite the IPO slowdown, with only 1,133 IPOs worldwide in 2024 compared to 1,425 in 2023. Analysts still consider the global equity market bigger and much stronger in times of crisis. Total global equity market capitalization reached a whopping $125.7 trillion by December 2024, which accounted for a 15.5% increase on a year-on-year basis.

The global equity research market is anticipated to witness tremendous growth in the coming years. Said development has been an emergence of various driving forces over the past few years. Valued at around $9.5 billion in 2023, it is likely to reach $15.6 billion by 2030, thereby recording a CAGR of 7.2%. The growing demand for market analysis, data-driven insights, and investment decisions characterizes a fast-changing financial environment with a set course of action.

Active management is making a comeback. Less than 54% of active managers underperformed the S&P 500 in the first half of 2025, whereas this figure stood at 65 in 2024

Small-cap stocks thus delivered a fine performance in August 2025, outperforming their large-cap counterparts by a wide margin. The Russell 2000, which represents smaller publicly traded companies, posted an impressive 7% gains for the month. Comparatively, the S&P 500 index, which is mostly composed of large-cap stocks, has gone only up by 2%. This added outperformance of small caps came at a time when they continued at a significant valuation discount of approximately 26% to large-cap stocks. Cheap valuation combined with a renewed investor interest in economically sensitive sectors probably spurred the sharp rally of small-cap stocks.

Current Trends in Equity Research

Some of the recent trends observed in Equity Research are:

Equity Research AI

70% of global asset managers are already using AI for earnings forecasts, sentiment analysis, and screening, and the rate of adoption is expected to hit 90% by 2027.

Intangible Assets

In 2024, intangible assets of the S&P 500-other than software, IP, brands, and data contributed 90% to the market value of the companies, i.e., worth above $25 trillion.

Private Credit Expansion

Positioned to rise from $1 trillion in 2020-2024 to after $2.1 trillion by 2028-$2.8 trillion, an increase in the need for equity-linked debt valuation models.

IPO Activity

India led Asia Pacific IPOs in 2024, raising $18.4 billion (149% YoY growth), while U.S. IPO proceeds declined by 25% during the same period.

Data Insights & Analytical Techniques

Earnings Forecast Accuracy

Analyst consensus earnings estimate for 2024 U.S. equities were accurate within ±8% compared with ±15% a decade ago, with the intervention of AI models.

Sentiment Analysis

Nowadays around 65% of global hedge funds employ NLP to analyze earnings calls with the aim of predicting market reaction.

Investor Flows

EPFR data shows net inflows of $340 billion in 2024 into global equity funds, with 60 percent flowing into ETFs, thus forcing active managers to differentiate through research.

Industry-Specific Applications

Some of the industry-specific applications of Equity Research are:

Industry-Specific Applications in Equity Research

Industry-Specific Applications in Equity Research

Technology & Startups

Equity research is vital in fundraising. It performs the vital function of validating the business model and building a reasonable case for market growth. Example: Global AI startups raised over $50B in 2024.

Private Equity & Hedge Funds

For transaction purposes, these banks work primarily with valuations: DCFs, LBOs, and comparable company valuation methods.

Corporate Finance & M&A

Global M&A volumes hit $2.9T in 2024, marking the period where equity valuation in due diligence became even more of a pressing need.

FinTech & Digital Assets

Equity research identifies risk and growth for 5,000-plus FinTech startups operating in India and for tokenized funds worldwide (~$10B AUM in 2024).

Why Consulting Firms Add Value

A consulting partner such as Magistral Consulting will lend equity research effectiveness:

Specialized Expertise

Analysts in the respective sectors, having advanced valuation skills.

Data-Backed Customization

Research reports customized to the portfolio of the client rather than the generic reports.

Speed & Efficiency

Offshore deliveries ensure cost savings of 30 to 50 percent, all while maintaining quality.

Risk & Compliance

Anticipating regulatory risks as they relate to disclosures about ESG and the intangibles’ issues.

ROI-Driven Results

Constitute better investment decisions resulting in better funds being raised and better market positioning.

In the $125T global equity space, equity research has gone from being “nice-to-have” to a strategic imperative. Whether uncovering undervalued stocks in emerging markets, analyzing India’s IPO wave, or integrating ESG, data-driven research is the only way forward. Specializing in particular verticals by blending deep expertise with delivery capabilities. It has enabled Magistral Consulting to help clients leverage data for decision-making, alpha, and growth.

Services Offered by Magistral Consulting for Equity Research

At Magistral Consulting, we offered customized research services for sell-side and buy-side clients through a modular engine catering to the challenges of today:

Fundamental Analysis

Magistral delivers customized financial models, quarterly earnings reviews, detailed earnings call transcript analysis, and equity- and industry-themed research to facilitate investment decision-making.

Quantitative Analysis

Data management includes cleansing, mining, and classification, whereas, advanced data analysis manages statistical analysis, correlation, and regression. Tracking and evaluating commodities are also included.

Credit Analysis

We offer country- and company-level risk assessments to support a risk-aware investment approach.

Content Marketing

Magistral supports outreach with industry-focused research encompassing industrial reports, index tracking, and analysis of events and news developments.

 

About Magistral Consulting

Magistral Consulting has helped multiple funds and companies in outsourcing operations activities. It has service offerings for Private Equity, Venture Capital, Family Offices, Investment Banks, Asset Managers, Hedge Funds, Financial Consultants, Real Estate, REITs, RE funds, Corporates, and Portfolio companies. Its functional expertise is around Deal origination, Deal Execution, Due Diligence, Financial Modelling, Portfolio Management, and Equity Research

For setting up an appointment with a Magistral representative visit www.magistralconsulting.com/contact

About the Author

Nitin is a Partner and Co-Founder at Magistral Consulting. He is a Stanford Seed MBA (Marketing) and electronics engineer with 19 + years at S&P Global and Evalueserve, leading research, analytics, and inside‑sales teams. An investment‑ and financial‑research specialist, he has delivered due‑diligence, fund‑administration, and market‑entry projects for clients worldwide. He now shapes Magistral Consulting’s strategic direction, oversees global operations, and drives business‑development support.

 

FAQs

What does “equity research in the financial services industry” mean in 2025?

Researching companies for valuation and calls remains the essence, now with data integrations, GenAI tooling, and evolving payment rules that let one often access the best coverage.

Is passive growth somehow killing the research?

No. With ETFs sitting at $17.3T AUM and flows surging, price moves usually cluster; differentiated research helps active managers to time exposures, to hedge, and to take advantage of dispersion.

Regulatory changes are to fix small-cap coverage, yes?

Not overnight, but reforms in the UK and EU go a long way in reducing frictions and making budget pools a little more flexible and should aid SMEs over the longer term-if managers take advantage of the new optionality.

Does research on stocks still matter with passive investing holding sway over the market?

Absolutely! Despite their ever-growing size, ETFs and index funds still have active managers who rely on research to find mispriced stocks and act accordingly upon it, going in or exiting, and on their risk mitigation strategy for the portfolios. This research is the edge needed to outperform the benchmarks and makes the case for paying active fees.

How is technology changing equity research in the coming years?

Technology, especially GenAI, automation, and alt-data integration, will reshape the workflows by reducing the time-to-publish.

 

The equity research AI world is transforming fundamentally. While once reliant on manual data handling and analysis, rigid models, and labour-intensive processes, firms are incorporating artificial intelligence (AI) to generate sharper insights, quicker execution, and wise investment decisions. The amount of structured and unstructured data available to market participants has exploded in the financial ecosystem — from earnings results to satellite data to social media buzz — traditional approaches are simply unable to keep up. AI is disrupting the current status quo by simplifying complexity into clarity.

From real-time sentiment analysis to predictive modeling, alternative data integration, and automated research generation, AI is fundamentally changing equity analyst workflows. While technically this is mostly about efficiency, it is intertwining the richness, accuracy, and agility of delivering important equity research AI contributions in the rapidly changing market landscape. Large firms have embraced AI, and mid-sized firms are not far behind, while global institutions are in the process of following an accelerated path of digital transformation. This suggests that the competitive advantage will continue to shift towards those who can use intelligent systems to inform, adapt, and act.

 

AI in Finance

AI in finance is accelerating. The global market for AI in finance is projected to be $38.36 billion in 2024 (up from $29.80 billion in 2020), with estimates of between $450 billion to $2 trillion being discussed by 2030 at a CAGR of between 25% – 35%. Change is underway with AI playing a significant role. AI is transforming the service delivery of financial services by replacing manual tasks with automated processing, increasing efficiencies, and making data-driven decisions possible.

Equity Research AI in Finance

Equity Research AI in Finance

Consider AI as it relates to asset management. In equity research AI tools are now being used t analyze vast and varied data sources, identify patterns, optimize strategies, and ultimately use the information to generate a more informed investment decision. According to NVIDIA’s (2024) Financial Services Industry Survey, nearly 75% of organizations reported that they received efficiency gains from AI, and nearly 60% of organizations that reported efficiency gains reported cost savings of at least 30%. Also of note, 75% of organizations reported they were able to improve customer satisfaction. Just under 80% of financial service organizations reported they were likely to increase their investment in AI in the next two years, reinforcing AI as a strategic investment opportunity.

The following are the benefits of equity research AI:

AI-Driven Data Analytics

Data analytics is a challenge for asset managers, as it requires them to consolidate inputs from many sources and quickly find meaningful signals before the market reacts. Equity research AI helps address this issue by:

Ingesting both structured and unstructured data from diverse sources, including company filings, social media, earnings calls, and alternative data sets

Utilizing Natural Language Processing (NLP) and machine learning (ML) to provide real-time sentiment and impact analysis

Allowing analysts to process much larger volumes of information – up to 100 times more volume than utilizing more traditional research methods

AI-driven analytics will provide a more complete and accurate view of the market, enabling investors to find “hidden” market signals while being able to act with more speed and conviction.

AI-Powered Financial Modelling

In the fast-moving market today, relying on prior spreadsheets and fixed assumptions can be limiting. AI, on the other hand, makes financial modelling more dynamic and adaptive through:

Creating customized valuation models that align with different fund strategies and market conditions

Automated scenario analysis, stress testing, and probabilistic forecasting

Significantly decreasing model-development time by up to 50% and updating cycles by up to 80%, enabling quick responses

AI does not just allow for automation, it increases the quality of decision making by providing comprehensive and integrated valuation techniques, including a DCF, comparables, and real options approach, all integrated into a single, intelligent model.

AI-Augmented Research

With a myriad of economic, political, and regulatory factors always shifting, research teams must operate opportunistically. AI shortens the research process by:

Using large language models (LLMs) and generative AI to produce investment theses, earnings previews, and summaries quickly.

Automatically capturing earnings calls summaries, extracting key points from SEC filings, and sourcing competitive intelligence.

Delivering real-time alerts and dashboards that highlight actionable intelligence from the markets.

With these capabilities, AI shortens the length of time to initiate research by as much as 40%, This creates the capacity for analysts to focus on strategic, high-value work.

AI-Driven Portfolio Management

To maximize Alpha, timely signals ahead of shifts in the market are key. AI empowers portfolio managers to take advantage of opportunities by:

Monitoring portfolios in real time, automatically rebalancing portfolios based on changes in risk & return

Access to predictive modelling for sector momentum, macro trends, and performance anomalies.

Embedding AI-based insights and strategies within quantitative and qualitative risk frameworks.

Research (including research performed by the University of Hamburg) shows AI-based models can provide returns up to 1.5% annually. At Sutherland, firms using AI-based tools produce returns exceeding market expectations over 60% of the time, creating more improved and consistent returns. 

Analyst Productivity and AI

A study indicated that more than 80% (81.12%) of finance professionals report utilizing AI-powered tools in their equity research AI process. Only 18.88% reported they were not using AI-powered tools at all. Regarding frequency, 60.22% of those surveyed reported using AI either “occasionally” (30.11%) or “frequently” (30.11%). Fewer employ AI “always” (15.05%), while some rarely (10.75%) or never (9.68%) use AI-powered tools at all.

Analyst Productivity and AI

Analyst Productivity and AI

Overall, the adoption of AI tools is driven largely by significant benefits such as improved efficiencies, better speeds of data on-boarding, increased job satisfaction (86.52%), which are developed through handling menial, habitual and repetitive tasks such as data collection and reporting and related deliverables, allowing the analyst to apply more towards higher-valued outputs.

AI Adoption by Size of Firm

Usage also differed by the size of the firm to a degree; however, there was little differentiation between firms, with mid-sized firms ahead (91.18%), followed by small firms (85.71%) and larger firms (83.33%). Boutique firms (1 – 50 employees) reported the lowest number of AI users (75%). Global firms (5001+ employees) reported relatively lower than other firms (71.43%), which may reflect organizational or legacy challenges within global firms.
>This may suggest that mid-sized and larger firms may be better able to adopt AI into the research workflow than global firms, which may still be going through the motions of large-scale membership in a digital transformation effort.

Time Savings from AI Tools

Time savings realized from AI adoption also correlate with firm size. Professionals at global and large firms report the most significant time savings:

45.96% of respondents at global firms’ report saving 10+ hours per week

37.32% of respondents at large firms save 6–10 hours per week

On the other hand, boutique firm professionals reported the least time savings, with 50.45% of them saving just 0–2 hours per week. This disparity suggests that larger firms may have more advanced AI infrastructures or better integration, enabling greater operational efficiencies.

Key Trends Shaping Equity Research AI

AI is revolutionizing equity research by enabling analysts to process vast datasets, uncover hidden patterns, and respond to market changes with real-time sentiment analysis. As ESG data becomes central to investment decisions, AI is accelerating compliance tracking and sustainability analysis. There’s a clear shift toward quantitative analysis, especially in emerging markets, where AI helps interpret complex financial structures. The growing use of alternative data—like social media, satellite imagery, and transaction records—further expands research depth. As AI handles routine tasks, analysts are evolving into strategic, tech-savvy partners with a deeper focus on ESG and continuous learning.

 

Magistral’s Services for Equity Research AI

Magistral offers the following services for equity research-

AI-Powered Financial Modeling and Forecasting

For equity research AI Magistral utilizes AI and machine learning to create predictive models. They increase accuracy in earnings forecasts, stock price predictions, and financial ratios. These models limit manual error and allow for a stream of real-time analysis based on market events.

Data Collection and Processing Automation

Magistral uses AI-based tools for automating the collection of financial statements, news articles, regulatory filings, and alternative data. They also process those documents/ files to minimize the time taken in data collection. They also reduce the overall time needed to produce an equity research report using equity research AI.

Sentiment Analysis and News Tracking

Magistral leverages natural language processing (NLP) models to evaluate news stories, social media, and earnings call transcripts, enabling political, economic, and other factors to track market sentiment for these various financial instruments, along with uncovering signals that can influence investor decisions.

Incorporating Alternative Data

For equity research AI Magistral also utilizes alternative data such as web traffic analytics, various satellite images, and credit card transactions to supplement research models using AI, which dives deeper into the perspective of standard financials.

Support for Quantitative and Technical Analysis

In context of equity research AI Magistral develops and adopts AI-based quantitative research. It is done so that it can utilize historical market data, technical indicators, and algorithms. They can find patterns, look for anomalies, and exploit trading opportunities.

Customized Dashboards and Visualization Tools

For equity research AI Magistral creates AI-enhanced dashboards that visualize key metrics, sentiment scores, and forecast data. This leads to enabling faster decision-making for buy-side and sell-side analysts.

Back-Testing and Model Validation

Equity research AI models undergo extensive back-testing to assess performance throughout market cycles. This can take many months. Magistral uses model tuning, validation, and model interpretability analysis to ensure models are compliant and reliable.

Outsourced Research Operations with AI Augmentation

For equity research AI Magistral leverages its offshore teams to provide economic research outsourcing services. We combine the use of human labor and AI-based research productivity tools to improve workflow and research coverage.

 

About Magistral Consulting

Magistral Consulting has helped multiple funds and companies in outsourcing operations activities. It has service offerings for Private Equity, Venture Capital, Family Offices, Investment Banks, Asset Managers, Hedge Funds, Financial Consultants, Real Estate, REITs, RE funds, Corporates, and Portfolio companies. Its functional expertise is around Deal origination, Deal Execution, Due Diligence, Financial Modelling, Portfolio Management, and Equity Research

For setting up an appointment with a Magistral representative visit www.magistralconsulting.com/contact

About the Author

The article is authored by the Marketing Department of Magistral Consulting. For any business inquiries, you can reach out to prabhash.choudhary@magistralconsulting.com

Key benefits include:

  • Real-time sentiment analysis and predictive modeling
  • Automated generation of financial models and investment research
  • Integration of alternative data for sharper insights
  • Enhanced portfolio management and real-time rebalancing
  • Improved analyst productivity and decision-making agility

AI systems use Natural Language Processing (NLP) and Machine Learning (ML) to ingest and analyze unstructured data from earnings calls, company filings, social media, and more. This allows analysts to uncover hidden signals and act swiftly.

Large language models (LLMs) and generative AI create earnings previews, investment theses, and market summaries efficiently. AI also extracts key points from earnings calls and filings, significantly shortening the research cycle by up to 40%.

Surprisingly, yes. Around 91.18% of mid-sized firms report AI usage in equity research, compared to 83.33% of large firms and 75% of boutique firms. Global firms lag slightly due to legacy systems and the complexity of large-scale digital transformation.