Quant Fund Market size was valued at USD 16,008.69 Billion in 2024 and is projected to reach USD 31,365.94 Billion by 2032, at a CAGR of 10.09% from 2026 to 2032.The increasing adoption of data analytics, machine learning, and artificial intelligence (AI) across sectors has significantly driven market demand for the Global Quant Funds Market. Specifically, within the quant fund market, these technological advancements have revolutionized the investment strategies, risk management techniques, and operational efficiency, leading to increased investor interest and allocation of capital to quant funds. The ability of quant funds to maintain lower expense ratios compared to conventional actively managed funds is another significant driver behind the growing market demand for the global quant fund market. The factor contributing to the lower expense ratios of quant funds is the efficiency of their investment processes.

The global quant fund market is experiencing robust growth, driven by increasing adoption of quantitative investment strategies by institutional investors and the proliferation of sophisticated analytical tools and technologies. The market size in 2025 is estimated at $2.5 trillion, exhibiting a compound annual growth rate (CAGR) of 12% from 2025 to 2033. This substantial growth is fueled by several key factors. Firstly, the increasing complexity of financial markets necessitates the use of quantitative models to identify and exploit subtle market inefficiencies. Secondly, the availability of vast amounts of data, coupled with advancements in artificial intelligence (AI) and machine learning (ML), enables the development of more accurate and efficient trading algorithms. Furthermore, the demand for consistent, data-driven returns, particularly in volatile market conditions, makes quant funds attractive to investors seeking diversification and risk management. The market is segmented by strategy (Trend Following Funds, Countertrend Strategies, Statistical Arbitrage Funds, Convertible Arbitrage, Fixed Income Arbitrage, Commodity Spread Trades, and Others) and sales channel (Direct Sales and Indirect Sales), offering diverse investment options to cater to various risk appetites and investment horizons. The major players in the market are global firms including Bridgewater Associates, AQR Capital Management, and Renaissance Technologies, who are constantly innovating and expanding their offerings. Geographic growth is expected to be strong across North America, Europe, and Asia-Pacific, with emerging markets also contributing significantly to the overall market expansion. The continued growth of the quant fund market is projected to be supported by several factors. The integration of advanced technologies like big data analytics and blockchain will continue to enhance the accuracy and speed of quantitative models, leading to improved trading performance. The expanding universe of alternative data sources, including social media sentiment and satellite imagery, will also provide additional insights for quantitative strategies. However, regulatory changes and potential market volatility pose challenges. The increasing regulatory scrutiny of high-frequency trading and the potential for unexpected market shocks are factors that need to be considered when assessing future growth. Despite these challenges, the overall market outlook for quant funds remains positive, with consistent growth projected throughout the forecast period. The ongoing development and refinement of quantitative models, combined with the persistent demand for data-driven investment solutions, are poised to drive significant market expansion in the coming years.

Quant IP package 4 delivers patent information on a company level for private companies.

Quant IP Patent Data Packages allow investors to find signals in company-level innovation metrics. Beyond raw metrics, the packages include proprietary innovation metrics for green innovation, patent quality and more.

The US hedge fund market, a cornerstone of alternative investments, is projected to reach a substantial size, exhibiting robust growth over the forecast period (2025-2033). The market's 2025 value of $2.77 billion reflects a significant accumulation of assets under management by prominent firms such as Bridgewater Associates, Renaissance Technologies, and BlackRock. A compound annual growth rate (CAGR) of 6.52% indicates consistent expansion, driven by several key factors. Increased investor interest in alternative investment strategies seeking higher returns than traditional markets, coupled with the sophisticated risk management techniques employed by hedge funds, fuels this growth. Technological advancements, particularly in areas like artificial intelligence and big data analytics, are enhancing investment strategies, contributing to improved performance and attracting further investment. However, regulatory scrutiny and evolving investor preferences pose potential constraints. The industry’s evolution is characterized by a shift towards more specialized strategies and the increasing adoption of sustainable and ESG (Environmental, Social, and Governance) investing principles. This suggests a move beyond traditional long/short equity strategies into niche areas like quantitative trading, private equity, and global macro strategies. The competitive landscape remains intensely competitive, with established giants vying for market share against nimble, emerging players employing innovative techniques. The segmentation of the US hedge fund market likely encompasses various investment strategies (e.g., long/short equity, global macro, distressed debt, event-driven), fund sizes (e.g., mega-funds, mid-sized funds, smaller funds), and investor types (e.g., institutional investors, high-net-worth individuals). Regional variations within the US market might also exist, reflecting economic activity and investor concentration in certain areas. The forecast anticipates continued growth, although the rate may fluctuate based on macroeconomic conditions, geopolitical events, and evolving regulatory frameworks. The dominance of established players is likely to persist, though disruptive innovations and the emergence of new, successful firms could reshape the competitive landscape in the coming years. Recent developments include: January 2024: The Palm Beach Hedge Fund Association (PBHFA), the premier trade association for investors and financial professionals in South Florida, and Entoro, a leading boutique finance and investment banking group, announced a strategic partnership to improve deal distribution for hedge funds., October 2022: Divya Nettimi, a former Viking Global Investors portfolio manager who oversaw over USD 4 billion at the Greenwich, Connecticut-based hedge fund firm, became the first woman to launch a hedge fund that has committed more than USD 1 billion.. Key drivers for this market are: Positive Trends in Equity Market is Driving the Market. Potential restraints include: Positive Trends in Equity Market is Driving the Market. Notable trends are: Rise of the Crypto Hedge Funds in United States.

The algorithmic trading market, currently valued at $11.88 billion in 2025, is projected to experience steady growth, driven by a Compound Annual Growth Rate (CAGR) of 3.7% from 2025 to 2033. This expansion is fueled by several key factors. Increased adoption of sophisticated trading strategies by investment banks and hedge funds seeking improved execution speed and efficiency is a significant driver. The ongoing transition towards cloud-based algorithmic trading platforms offers enhanced scalability, reduced infrastructure costs, and improved accessibility, further accelerating market growth. Furthermore, the rising popularity of algorithmic trading among personal investors, facilitated by the proliferation of user-friendly platforms and increased access to market data, contributes to market expansion. However, regulatory scrutiny surrounding algorithmic trading practices and concerns related to market manipulation and flash crashes pose potential restraints. The market is segmented by application (Investment Banks, Funds, Personal Investors, Others) and type (On-Premise, Cloud-Based). While North America currently dominates the market share due to the high concentration of major players and advanced technological infrastructure, regions like Asia-Pacific are poised for significant growth fueled by increasing financial market sophistication and technological advancements. The competitive landscape is marked by a mix of established players like Virtu Financial and DRW Trading, and emerging technology firms specializing in developing innovative trading algorithms. The competitive dynamics are characterized by continuous innovation and a race to develop superior algorithms and trading strategies. The forecast for the algorithmic trading market indicates consistent growth throughout the projection period (2025-2033), with the cloud-based segment expected to outpace on-premise solutions due to its inherent advantages. The increasing demand for high-frequency trading (HFT) solutions and sophisticated risk management tools will also fuel this growth. The market will likely witness strategic partnerships and mergers and acquisitions as firms strive for greater market share and technological advancements. Geographic expansion, particularly in developing economies with burgeoning financial markets, represents another significant growth opportunity. However, the market will need to navigate the challenges posed by regulatory changes and technological risks to maintain its projected growth trajectory. This requires a continued focus on responsible algorithm development, robust risk management practices, and transparent regulatory compliance.

The global quant fund management fee market is experiencing robust growth, with a market size of $4,042 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 4.8% from 2025 to 2033. This expansion is fueled by several key factors. Increased adoption of quantitative strategies by institutional investors seeking consistent returns and sophisticated risk management is a major driver. The rise of alternative data sources, advanced analytical tools, and machine learning algorithms is enhancing the capabilities of quant funds, leading to improved performance and attracting more investment. Furthermore, the growing complexity of financial markets and the need for sophisticated investment strategies contribute to the market's expansion. The market is segmented by fund type (Trend Following Funds, Countertrend Strategies, Statistical Arbitrage Funds, Convertible Arbitrage, Fixed Income Arbitrage, Commodity Spread Trades, and Others) and application (Direct Sales and Indirect Sales). Leading players like Bridgewater Associates, AQR Capital Management, and Renaissance Technologies are shaping the competitive landscape through innovation and technological advancements. Geographic expansion, particularly in emerging markets with growing investment pools, is another significant growth contributor. The market's growth, however, is not without its challenges. Regulatory scrutiny and increased compliance costs are potential restraints. Furthermore, market volatility and unforeseen economic events can impact investor confidence and negatively affect the performance of quant funds. The ongoing evolution of algorithmic trading strategies and the competitive pressures among firms also pose challenges for growth. Despite these constraints, the long-term outlook for the quant fund management fee market remains positive, driven by the continued demand for sophisticated investment solutions and the ongoing technological advancements within the industry. The projected growth trajectory indicates significant potential for market participants, particularly those who adapt quickly to changing market dynamics and technological innovations.

Dataset containing over 5000 data metrics (including raw data and BQ calculated scores & metrics) for over 4000 public companies (~95% of the Russell 3000). Includes financials (from SEC filings) as well as data that is not reported to the SEC, including monthly headcount, detailed employee benefits data, credit events related to contributions to benefits plans. Also includes BQ scores, industry and macro statistics that provide a comprehensive view of the sector & industry.

BQ's Public Companies dataset is applicable to both quantitative investment managers as well as fundamentals public equity investors, who wish to use alternative (non-financial) data to enhance their investment analysis and investment decisions.

The High-Frequency Trading (HFT) Model Systems market, valued at $7.147 billion in 2025, is projected to experience robust growth, driven by a Compound Annual Growth Rate (CAGR) of 11.8% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing reliance on algorithmic trading strategies within the finance industry necessitates sophisticated HFT model systems for achieving optimal execution speeds and minimizing latency. Secondly, advancements in computing power, particularly the development of specialized hardware like FPGAs and ASICs, are enabling the creation of ever more complex and efficient trading algorithms. Furthermore, the rise of quantitative research and the growing adoption of machine learning techniques are contributing to the development of more predictive and profitable HFT models. The demand for these systems is particularly high in regions with well-developed financial markets like North America and Europe, where established players like Two Sigma Investments, Virtu Financial, and XTX Markets are driving innovation and market share. However, the market also faces certain restraints. Regulatory scrutiny of HFT practices, aimed at curbing market manipulation and ensuring fair trading, presents a significant challenge. The complexity and high cost associated with developing, implementing, and maintaining HFT model systems can also limit adoption among smaller firms. Despite these constraints, the market’s growth trajectory is anticipated to remain strong due to the enduring competitive advantage conferred by speed and efficiency in today's fast-paced financial markets. Segmentation analysis reveals a strong concentration in the Finance Industry application segment and the Software Program type, yet the Others segments in both application and type hold substantial untapped potential. This suggests opportunities for innovative players to offer niche solutions and customized HFT models catered to specific industry needs and technological preferences. Geographic expansion, particularly in developing markets with burgeoning financial sectors, represents another avenue for future growth.

The quantitative investment market is experiencing robust growth, driven by the increasing adoption of advanced analytical techniques and algorithmic trading strategies. The market's sophistication is reflected in its segmentation, encompassing various investment types (stocks, bonds, futures, options) and employing diverse strategies (trend judgment, volatility judgment). The substantial market size, estimated at $500 billion in 2025, demonstrates the significant capital allocated to these strategies. A Compound Annual Growth Rate (CAGR) of 12% is projected from 2025 to 2033, suggesting a market value exceeding $1.5 trillion by 2033. This growth is fueled by several factors: the availability of vast datasets, advancements in machine learning and artificial intelligence, and a growing need for efficient portfolio management in increasingly complex financial markets. Furthermore, the rise of fintech and the proliferation of high-frequency trading further accelerate market expansion. However, the quantitative investment market is not without challenges. Regulatory scrutiny, particularly regarding algorithmic trading's potential for market manipulation and systemic risk, poses a significant restraint. The high initial investment costs associated with developing and maintaining sophisticated quantitative models also present a barrier to entry for smaller firms. Despite these challenges, the long-term outlook for quantitative investment remains positive, driven by ongoing technological innovation and the inherent demand for superior risk-adjusted returns in the financial industry. The competitive landscape is dominated by established giants like Millennium Management and Bridgewater Associates alongside emerging players in Asia, indicating a globally distributed and dynamic market.

Browse LSEG's StarMine Quantitative Models, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivaled data and delivery mechanisms.

The algorithmic trading market, valued at $14.99 billion in 2025, is poised for significant growth. Driven by increasing demand for high-frequency trading, advanced analytics, and the need for faster execution speeds, the market is expected to experience substantial expansion throughout the forecast period (2025-2033). The adoption of cloud-based solutions is a major trend, offering scalability, flexibility, and cost-effectiveness compared to on-premise systems. Investment banks remain the dominant segment, leveraging algorithmic trading for sophisticated strategies and large-scale transactions. However, the rise of personal investors and the increasing availability of user-friendly algorithmic trading platforms are expanding market accessibility. Geographical expansion is also a key driver, with North America currently holding a significant market share, followed by Europe and Asia Pacific. Regulatory scrutiny and cybersecurity concerns represent potential restraints, necessitating robust compliance frameworks and robust security measures within the industry. The competitive landscape is highly concentrated, with major players like Virtu Financial, DRW Trading, and Optiver leading the market. However, the emergence of smaller, specialized firms focused on niche strategies indicates a growing diversification within the sector. Continued innovation in artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) will further shape the market landscape, potentially leading to the development of more sophisticated and autonomous trading systems. The increasing adoption of blockchain technology for secure and transparent transactions could also influence market dynamics. Looking ahead, the Algorithmic Trading market's growth trajectory depends on factors such as global economic stability, technological advancements, and regulatory developments. A conservative estimate suggests a compound annual growth rate (CAGR) of 8-10% over the forecast period.

The global quant fund management fee market is projected to reach USD XX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The growth of the market is attributed to various factors, including the rising demand for quantitative investment strategies, the increasing sophistication of quantitative models, and the growing adoption of artificial intelligence (AI) and machine learning (ML) techniques in the fund management industry. The market is expected to witness significant growth in the coming years as more investors seek to capitalize on the potential benefits of quant-driven investment strategies. The market is highly competitive, with a number of established players and several new entrants. Some of the key players in the market include Bridgewater Associates, AQR Capital Management, Millennium Management, Citadel, and Soros Fund Management. These companies offer a wide range of quant-based investment strategies, catering to the needs of different types of investors. The market is also witnessing the emergence of new players, particularly from the Asia-Pacific region. These new entrants are leveraging advancements in AI and ML to develop innovative quant-based investment strategies. The competitive landscape is expected to remain fragmented over the forecast period, with new entrants challenging the dominance of established players.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Quant-IX-Software.

Annual reports Assessment Dataset

This dataset will help investors, merchant bankers, credit rating agencies, and the community of equity research analysts explore annual reports in a more automated way, saving them time.

Following Sub Dataset(s) are there :

a) pdf and corresponding OCR text of 100 Indian annual reports These 100 annual reports are for the 100 largest companies listed on the Bombay Stock Exchange. The total number of words in OCRed text is 12.25 million.

b) A Few Examples of Sentences with Corresponding Classes The author defined 16 widely used topics used in the investment community as classes like:

Accounting Standards

Accounting for Revenue Recognition

Corporate Social Responsbility

Credit Ratings

Diversity Equity and Inclusion

Electronic Voting

Environment and Sustainability

Hedging Strategy

Intellectual Property Infringement Risk

Litigation Risk

Order Book

Related Party Transaction

Remuneration

Research and Development

Talent Management

Whistle Blower Policy

These classes should help generate ideas and investment decisions, as well as identify red flags and early warning signs of trouble when everything appears to be proceeding smoothly.

ABOUT DATA ::

"scrips.json" is a json with name of companies "SC_CODE" is BSE Scrip Id "SC_NAME" is Listed Companies Name "NET_TURNOV" is Turnover on the day of consideration

"source_pdf" is folder containing both PDF and OCR Output from Tesseract "raw_pdf.zip" contains raw PDF and it can be used to try another OCR. "ocr.zip" contains json file (annual_report_content.json) containing OCR text for each pdf. "annual_report_content.json" is an array of 100 elements and each element is having two keys "file_name" and "content"

"classif_data_rank_freezed.json" is used for evaluation of results contains "sentence" and corresponding "class"

The global Financial Industry Quantitative Evaluation Service market is experiencing robust growth, driven by increasing demand for sophisticated risk management and algorithmic trading solutions. The market, estimated at $15 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value of approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, the increasing complexity of financial markets necessitates advanced quantitative techniques for accurate risk assessment and portfolio optimization. Secondly, regulatory scrutiny and compliance requirements are pushing firms to adopt more robust and transparent evaluation methods. Thirdly, the proliferation of big data and advanced analytics provides the raw material for more sophisticated quantitative models, enabling firms to gain a competitive edge. Leading players like Bloomberg, AQR Capital Management, and Renaissance Technologies are driving innovation and setting industry standards, while smaller firms are focusing on niche areas and specialized services. However, the market also faces some challenges. The high cost of implementing and maintaining quantitative evaluation systems can be a barrier to entry for smaller firms. Furthermore, the need for specialized expertise and skilled professionals creates a talent shortage within the industry, potentially limiting growth. Despite these restraints, the overall outlook for the Financial Industry Quantitative Evaluation Service market remains positive, with continued innovation and adoption expected across various financial sectors. The market is segmented geographically, with North America and Europe currently holding the largest market share. However, regions like Asia-Pacific are experiencing rapid growth, driven by increasing financial activity and technological advancements. The long-term prospects are further enhanced by the ongoing development of artificial intelligence and machine learning techniques within quantitative finance, promising even more accurate and efficient evaluation services in the future.

Lucror Analytics: Proprietary Company Financial Data for Credit Quality & Bond Valuation

At Lucror Analytics, we provide cutting-edge corporate data solutions tailored to fixed income professionals and organizations in the financial sector. Our datasets encompass issuer and issue-level credit quality, bond fair value metrics, and proprietary scores designed to offer nuanced, actionable insights into global bond markets that help you stay ahead of the curve. Covering over 3,300 global issuers and over 80,000 bonds, we empower our clients to make data-driven decisions with confidence and precision.

By leveraging our proprietary C-Score, V-Score , and V-Score I models, which utilize CDS and OAS data, we provide unparalleled granularity in credit analysis and valuation. Whether you are a portfolio manager, credit analyst, or institutional investor, Lucror’s data solutions deliver actionable insights to enhance strategies, identify mispricing opportunities, and assess market trends.

What Makes Lucror’s Company Financial Data Unique?

Proprietary Credit and Valuation Models Our proprietary C-Score, V-Score, and V-Score I are designed to provide a deeper understanding of credit quality and bond valuation:

C-Score: A composite score (0-100) reflecting an issuer's credit quality based on market pricing signals such as CDS spreads. Responsive to near-real-time market changes, the C-Score offers granular differentiation within and across credit rating categories, helping investors identify mispricing opportunities.

V-Score: Measures the deviation of an issue’s option-adjusted spread (OAS) from the market fair value, indicating whether a bond is overvalued or undervalued relative to the market.

V-Score I: Similar to the V-Score but benchmarked against industry-specific fair value OAS, offering insights into relative valuation within an industry context.

Comprehensive Global Coverage Our datasets cover over 3,300 issuers and 80,000 bonds across global markets, ensuring 90%+ overlap with prominent IG and HY benchmark indices. This extensive coverage provides valuable insights into issuers across sectors and geographies, enabling users to analyze issuer and market dynamics comprehensively.

Data Customization and Flexibility We recognize that different users have unique requirements. Lucror Analytics offers tailored datasets delivered in customizable formats, frequencies, and levels of granularity, ensuring that our data integrates seamlessly into your workflows.

High-Frequency, High-Quality Data Our C-Score, V-Score, and V-Score I models and metrics are updated daily using end-of-day (EOD) data from S&P. This ensures that users have access to current and accurate information, empowering timely and informed decision-making.

How Is the Company Financial Data Sourced? Lucror Analytics employs a rigorous methodology to source, structure, transform and process data, ensuring reliability and actionable insights:

Proprietary Models: Our scores are derived from proprietary quant algorithms based on CDS spreads, OAS, and other issuer and bond data.

Global Data Partnerships: Our collaborations with S&P and other reputable data providers ensure comprehensive and accurate datasets.

Data Cleaning and Structuring: Advanced processes ensure data integrity, transforming raw inputs into actionable insights.

Primary Use Cases

1. Portfolio Construction & Rebalancing Lucror’s C-Score provides a granular view of issuer credit quality, allowing portfolio managers to evaluate risks and identify mispricing opportunities. With CDS-driven insights and daily updates, clients can incorporate near-real-time issuer/bond movements into their credit assessments.

2. Portfolio Optimization The V-Score and V-Score I allow portfolio managers to identify undervalued or overvalued bonds, supporting strategies that optimize returns relative to credit risk. By benchmarking valuations against market and industry standards, users can uncover potential mean-reversion opportunities and enhance portfolio performance.

3. Risk Management With data updated daily, Lucror’s models provide dynamic insights into market risks. Organizations can use this data to monitor shifts in credit quality, assess valuation anomalies, and adjust exposure proactively.

4. Strategic Decision-Making Our comprehensive datasets enable financial institutions to make informed strategic decisions. Whether it’s assessing the fair value of bonds, analyzing industry-specific credit spreads, or understanding broader market trends, Lucror’s data delivers the depth and accuracy required for success.

Why Choose Lucror Analytics? Lucror Analytics is committed to providing high-quality, actionable data solutions tailored to the evolving needs of the financial sector. Our unique combination of proprietary models, rigorous sourcing of high-quality data, and customizable delivery ensures that users have the insights they need to make sm...

MSCI Barra Models are leading risk models backed by over 40 years of factor data and now leverage Systematic Equity Strategy factors. Get access via LSEG.

Comprehensive alternative data platform providing real-time market intelligence from social media, web traffic, consumer behavior, and search trends for institutional investors and hedge funds

When there is a vast variety of metrics and tools available to gain market insight, Insider trading offers valuable clues to investors related to future share performance. We at Smart Insider provide global insider trading data and analysis on share transactions made by directors & senior staff in the shares of their own companies.

Monitoring all the insider trading activity is a huge task, we identify 'Smart Insiders' through specialist desktop and quantitative feeds that enable our clients to generate alpha.

Our experienced analyst team use quantitative and qualitative methods to identify the stocks most likely to outperform based on deep analysis of insider trades, and the insiders themselves. Using our easy-to-read derived data we help our clients better understand insider transactions activity to make informed investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as XML, XLSX or API via SFTP or Snowflake.

Sample dataset for Desktop Service has been provided with some proprietary fields concealed. Upon request, we can provide a detailed Quant sample.

Tags: Stock Market Data, Equity Market Data, Insider Transactions Data, Insider Trading Intelligence, Trading, Investment Management, Alternative Investment, Asset Management, Equity Research, Market Analysis, United Kingdom, Europe