Innodata (INOD)


Market Price (11/10/2026): $37.1 | Market Cap: $1.3 BilSector: Information Technology | Industry: IT Consulting & Other Services

Innodata (INOD)


Market Price (11/10/2026): $37.1
Market Cap: $1.3 Bil
Sector: Information Technology
Industry: IT Consulting & Other Services

Investment Highlights Why It Matters Detailed financial logic regarding cash flow yields vs trend-riding momentum.

0

Cash is significant % of market cap
Net D/ENet Debt/Equity. Debt net of cash. Negative indicates net cash. Equity is taken as the Market Capitalization is -13%

Strong revenue growth
Rev Chg LTMRevenue Change % Last Twelve Months (LTM) is 47%

Attractive cash flow generation
CFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is 27%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is 24%

Valuation becoming less expensive
P/S 6M Chg %Price/Sales change over 6 months. Declining P/S indicates valuation has become less expensive. is -54%

Attractive yield
FCF Yield is 6.5%

Megatrend and thematic drivers
Megatrends include Artificial Intelligence. Themes include AI Software Platforms, AI Data Annotation & Curation, and Generative AI Data Services.

Meaningful short interest
Short Interest % of Basic SharesShort Interest % of Basic Shares = (Short Interest Quantity) / (Basic Shares Outstanding). A high fraction of short interest can indicate potential risk of a short squeeze. is 13%

High stock price volatility
Vol 12M is 122%

Key risks
INOD key risks include [1] an extreme client concentration, Show more.

0 Cash is significant % of market cap
Net D/ENet Debt/Equity. Debt net of cash. Negative indicates net cash. Equity is taken as the Market Capitalization is -13%
1 Strong revenue growth
Rev Chg LTMRevenue Change % Last Twelve Months (LTM) is 47%
2 Attractive cash flow generation
CFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is 27%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is 24%
3 Valuation becoming less expensive
P/S 6M Chg %Price/Sales change over 6 months. Declining P/S indicates valuation has become less expensive. is -54%
4 Attractive yield
FCF Yield is 6.5%
5 Megatrend and thematic drivers
Megatrends include Artificial Intelligence. Themes include AI Software Platforms, AI Data Annotation & Curation, and Generative AI Data Services.
6 Meaningful short interest
Short Interest % of Basic SharesShort Interest % of Basic Shares = (Short Interest Quantity) / (Basic Shares Outstanding). A high fraction of short interest can indicate potential risk of a short squeeze. is 13%
7 High stock price volatility
Vol 12M is 122%
8 Key risks
INOD key risks include [1] an extreme client concentration, Show more.

INOD in ETFs

Weight = INOD's share of each fund

VTI0.00%
ITOT0.00%
IWM0.04%
IWO0.09%
VTWO0.07%
SCHA0.04%
DFAS0.02%
DFAC0.00%
+3 more covered ETFs

Valuation & Metrics

Price Chart

Why The Stock Moved

Stock Movement Drivers

Fundamental Drivers

The -41.2% change in INOD stock from 7/31/2026 to 11/9/2026 was primarily driven by a -49.7% change in the company's P/E Multiple.
(LTM values as of)731202611092026Change
Stock Price ($)62.8336.96-41.2%
Change Contribution By: 
Total Revenues ($ Mil)28335023.6%
Net Income Margin (%)13.9%13.8%-0.2%
P/E Multiple52.226.2-49.7%
Shares Outstanding (Mil)3334-5.1%
Cumulative Contribution-41.2%

LTM = Last Twelve Months as of date shown

Market Drivers

7/31/2026 to 11/9/2026
ReturnCorrelation
INOD-41.2% 
Market (SPY)0.8%37.3%
Sector (XLK)-2.5%43.6%

Fundamental Drivers

The -12.5% change in INOD stock from 4/30/2026 to 11/9/2026 was primarily driven by a -37.9% change in the company's P/E Multiple.
(LTM values as of)430202611092026Change
Stock Price ($)42.2336.96-12.5%
Change Contribution By: 
Total Revenues ($ Mil)25235039.2%
Net Income Margin (%)12.8%13.8%8.2%
P/E Multiple42.226.2-37.9%
Shares Outstanding (Mil)3234-6.5%
Cumulative Contribution-12.5%

LTM = Last Twelve Months as of date shown

Market Drivers

4/30/2026 to 11/9/2026
ReturnCorrelation
INOD-12.5% 
Market (SPY)5.0%28.6%
Sector (XLK)7.3%37.0%

Fundamental Drivers

The -50.5% change in INOD stock from 10/31/2025 to 11/9/2026 was primarily driven by a -52.8% change in the company's P/E Multiple.
(LTM values as of)1031202511092026Change
Stock Price ($)74.6136.96-50.5%
Change Contribution By: 
Total Revenues ($ Mil)22835053.5%
Net Income Margin (%)18.7%13.8%-26.0%
P/E Multiple55.526.2-52.8%
Shares Outstanding (Mil)3234-7.6%
Cumulative Contribution-50.5%

LTM = Last Twelve Months as of date shown

Market Drivers

10/31/2025 to 11/9/2026
ReturnCorrelation
INOD-50.5% 
Market (SPY)11.3%35.1%
Sector (XLK)14.1%41.5%

Fundamental Drivers

The 392.8% change in INOD stock from 10/31/2023 to 11/9/2026 was primarily driven by a 358.9% change in the company's Total Revenues ($ Mil).
(LTM values as of)1031202311092026Change
Stock Price ($)7.5036.96392.8%
Change Contribution By: 
Total Revenues ($ Mil)76350358.9%
P/S Multiple2.73.632.5%
Shares Outstanding (Mil)2834-19.0%
Cumulative Contribution392.8%

LTM = Last Twelve Months as of date shown

Market Drivers

10/31/2023 to 11/9/2026
ReturnCorrelation
INOD392.8% 
Market (SPY)86.2%36.2%
Sector (XLK)112.1%39.8%

Return vs. Risk

Price Returns Compared

 202120222023202420252026Total [1]
Returns
INOD Return12%-50%175%386%29%-27%597%
Peers Return-6%30%0%5%21%-49%-22%
S&P 500 Return27%-19%24%23%16%8%96%

Monthly Win Rates [3]
INOD Win Rate50%33%67%50%50%36% 
Peers Win Rate31%33%42%33%39%24% 
S&P 500 Win Rate75%42%67%75%67%36% 

Max Drawdowns [4]
INOD Max Drawdown-51%-68%-57%-54%-54%-70% 
Peers Max Drawdown-21%-13%-22%-16%-17%-53% 
S&P 500 Max Drawdown-5%-25%-10%-8%-19%-9% 


[1] Cumulative total returns since the beginning of 2021
[2] Peers: GIB, DCM, HLSQ, IBEX, OPNW.
[3] Win Rate = % of calendar months in which monthly returns were positive
[4] Max drawdown represents maximum peak-to-trough decline within a year
[5] 2026 data is for the year up to 11/9/2026 (YTD)

How Low Can It Go

EventINODS&P 500
2025 US Tariff Shock
  % Loss-50.1%-18.8%
  % Gain to Breakeven100.5%23.1%
  Time to Breakeven143 days79 days
Summer-Fall 2023 Five Percent Yield Shock
  % Loss-48.4%-9.5%
  % Gain to Breakeven93.9%10.5%
  Time to Breakeven177 days24 days
2022 Inflation Shock & Fed Tightening
  % Loss-50.9%-24.5%
  % Gain to Breakeven103.8%32.4%
  Time to Breakeven131 days427 days
2020 COVID-19 Crash
  % Loss-33.9%-33.7%
  % Gain to Breakeven51.3%50.9%
  Time to Breakeven49 days140 days
2016-2017 Trump Reflation Bond Selloff
  % Loss-36.0%-3.7%
  % Gain to Breakeven56.1%3.9%
  Time to Breakeven1190 days6 days
2015-2016 China Devaluation / Global Growth Scare
  % Loss-14.2%-12.2%
  % Gain to Breakeven16.5%13.9%
  Time to Breakeven39 days62 days

Compare to GIB, DCM, HLSQ, IBEX, OPNW

In The Past

Innodata's stock fell -50.1% during the 2025 US Tariff Shock. Such a loss loss requires a 100.5% gain to breakeven.

Preserve Wealth

Limiting losses and compounding gains is essential to preserving wealth.

Asset Allocation

Actively managed asset allocation strategies protect wealth. Learn more.

EventINODS&P 500
2025 US Tariff Shock
  % Loss-50.1%-18.8%
  % Gain to Breakeven100.5%23.1%
  Time to Breakeven143 days79 days
Summer-Fall 2023 Five Percent Yield Shock
  % Loss-48.4%-9.5%
  % Gain to Breakeven93.9%10.5%
  Time to Breakeven177 days24 days
2022 Inflation Shock & Fed Tightening
  % Loss-50.9%-24.5%
  % Gain to Breakeven103.8%32.4%
  Time to Breakeven131 days427 days
2020 COVID-19 Crash
  % Loss-33.9%-33.7%
  % Gain to Breakeven51.3%50.9%
  Time to Breakeven49 days140 days
2016-2017 Trump Reflation Bond Selloff
  % Loss-36.0%-3.7%
  % Gain to Breakeven56.1%3.9%
  Time to Breakeven1190 days6 days
2014-2016 Oil Price Collapse
  % Loss-29.7%-6.8%
  % Gain to Breakeven42.2%7.3%
  Time to Breakeven1810 days15 days
2013 Taper Tantrum
  % Loss-27.2%-0.2%
  % Gain to Breakeven37.3%0.2%
  Time to Breakeven102 days1 days
2010 Eurozone Sovereign Debt Crisis / Flash Crash
  % Loss-33.2%-15.4%
  % Gain to Breakeven49.8%18.2%
  Time to Breakeven546 days125 days
2008-2009 Global Financial Crisis
  % Loss-73.4%-53.4%
  % Gain to Breakeven276.6%114.4%
  Time to Breakeven265 days1085 days

Compare to GIB, DCM, HLSQ, IBEX, OPNW

In The Past

Innodata's stock fell -50.1% during the 2025 US Tariff Shock. Such a loss loss requires a 100.5% gain to breakeven.

Preserve Wealth

Limiting losses and compounding gains is essential to preserving wealth.

Asset Allocation

Actively managed asset allocation strategies protect wealth. Learn more.

About Innodata (INOD)

Innodata Inc. is a global data engineering company primarily focused on leveraging artificial intelligence (AI) and machine learning (ML). Its Digital Data Solutions (DDS) segment provides AI-enabled software platforms and managed services crucial for training AI and ML algorithms. This includes comprehensive data engineering support like data annotation, transformation, curation, and master data management, alongside AI digital transformation solutions to help companies analyze documents and extract valuable insights.

Beyond its core AI data engineering, Innodata operates two specialized platforms. The Synodex segment offers an industry platform that digitizes and transforms medical records into usable data, utilizing its proprietary or client-specific data models. Separately, the Agility segment provides a platform for marketing communications and public relations professionals, enabling them to target and distribute content to journalists and social media influencers, as well as monitor and analyze global news and social media channels.

Innodata serves a diverse range of industries and professionals globally. Its primary customers span sectors such as banking, insurance, financial services, technology, digital retailing, and information/media. Through its specialized platforms, the company also caters to specific needs within the healthcare industry for medical record data and directly supports marketing communications and public relations professionals in managing their outreach and monitoring efforts.

AI Analysis | Feedback

1. It's like a specialized **Accenture** for AI, helping companies prepare, clean, and transform vast amounts of data to train AI and machine learning models.

2. For its Agility segment, it's like **Cision** or **Meltwater**, providing a platform for public relations and marketing professionals to manage media relations, distribute content, and monitor news.

AI Analysis | Feedback

  • AI Data Engineering and Transformation Services: Innodata provides AI-enabled software platforms and managed services for data annotation, transformation, curation, and master data management, essential for training AI/ML algorithms and digital transformation.
  • Synodex Platform: This industry platform specializes in transforming complex medical records into usable digital data using proprietary or client-defined data models.
  • Agility Platform: An industry platform designed for marketing communications and public relations professionals to target influencers, distribute content, and monitor global news and social media.

AI Analysis | Feedback

Major Customers of Innodata Inc. (INOD)

Innodata Inc. (INOD) primarily sells its AI-enabled software platforms, managed services, and industry-specific platforms to other companies (B2B) rather than individuals. The company serves clients across a range of major industries and sectors:

  • Banking sector
  • Insurance sector
  • Financial services sector
  • Technology sector
  • Digital retailing sector
  • Information/media sector

The provided background information describes the types of industries and sectors Innodata serves but does not disclose the specific names of its customer companies.

AI Analysis | Feedback

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AI Analysis | Feedback

Jack Abuhoff, Chief Executive Officer and Chairman of the Board

Jack Abuhoff is the co-founder of Innodata and has served as its President and Chief Executive Officer since September 1997, as well as a director since the company's founding in 1988. From 1995 to 1997, he was the Chief Operating Officer of Charles River Corporation, an international systems integration and outsourcing firm. He was also employed by Chadbourne & Parke and involved in Sino-American technology joint ventures with Goldman Sachs from 1992 to 1994. Earlier in his career, he practiced international corporate law with White & Case from 1986 to 1992. He holds an A.B. from Columbia College and a J.D. from Harvard Law School.

Marissa (Mariz) Espineli, Interim Chief Financial Officer and Principal Accounting Officer

Marissa Espineli was appointed Interim Chief Financial Officer on March 16, 2022. She has served as Innodata's Vice President of Finance since 2012, and previously as Corporate Controller. Before joining Innodata, she held significant finance roles at large U.S.-based companies in the consumer goods, food & beverages, and industrial products sectors. She began her career in public accounting as a Senior Auditor, specializing in taxation and financial audits. Ms. Espineli holds a Bachelor of Science in Business Administration with a focus on Finance and Accounting and is a Certified Public Accountant.

Rahul Singhal, President and Chief Revenue Officer

Rahul Singhal was promoted to President and Chief Revenue Officer in November 2025. Prior to this, he held the positions of Chief Product Officer (since January 2019) and Chief Revenue Officer (since January 2022) at Innodata, where he was responsible for defining the company's product strategy, roadmap, and go-to-market execution across its AI, data engineering, and digital transformation businesses. Before joining Innodata in 2019, Mr. Singhal spent over a decade at IBM in various leadership roles, culminating in his position as Program Director for IBM Watson Platform APIs. He also served as Chief Product Officer at Equals3.AI, an AI-powered knowledge management platform, and as an Adjunct Professor in New York City. He holds a Bachelor of Engineering in mechanical engineering and an MBA.

Amy Agress, General Counsel

As General Counsel, Amy Agress is responsible for overseeing Innodata's global legal functions, providing advice to the board and management, ensuring regulatory compliance, and contributing to the development of business strategy from a legal perspective. She possesses over thirty years of experience in general corporate legal matters. Before her tenure at Innodata, she worked as an associate at a general practice law firm in New York City. Ms. Agress earned a Bachelor of Arts in History from New York University and a Juris Doctor from Fordham University School of Law.

AK Mishra, Chief Operating Officer

AK Mishra is responsible for leading Innodata's high-performing scaled data operations across more than 20 global delivery locations. He has implemented innovative approaches, emphasizing hybrid human/machine workflows to achieve continuous improvement. Prior to joining Innodata, Mr. Mishra held senior-level positions at a prominent telecom manufacturer and various management roles in production, planning, processes, and quality. He holds a Bachelor of Technology in Mechanical Engineering and completed a condensed Master of Business Administration program.

AI Analysis | Feedback

The key risks to Innodata's business operations are primarily centered around its customer relationships, the highly competitive nature of the AI and data engineering market, and challenges related to maintaining profitability and scalability.

  1. Customer Concentration Risk: Innodata exhibits a significant reliance on a limited number of clients, particularly within its Digital Data Solutions (DDS) segment. In 2025, one customer alone accounted for approximately 58% of the company's total revenues, a figure that was even higher in some previous quarters. This heavy client concentration creates a substantial vulnerability, as the loss or reduction of business from such a major client could have a material adverse effect on Innodata's revenue stream, financial results, and overall stability. Many of these contracts are project-based and terminable on short notice, typically 30 to 90 days, which further exacerbates this risk.
  2. Intense Competition and Rapid Technological Change: Innodata operates in a highly dynamic and competitive market for AI and data engineering services. Numerous players, including large technology firms that may choose to bring data operations in-house, are vying for market share. This intense competition can lead to pricing pressures, reduced profit margins, and a continuous need for significant investment in technology and innovation to maintain a competitive edge. Furthermore, the rapid pace of technological advancements in artificial intelligence could potentially render Innodata's current offerings obsolete if the company fails to adapt quickly and effectively.
  3. Margin Pressure and Scalability Challenges: Despite experiencing impressive revenue growth, particularly driven by demand for its AI data services, Innodata faces challenges regarding margin expansion and the scalability of its operations. The company's business model, which is often service-based and human-intensive, can limit its ability to achieve significant economies of scale. Analysts have noted that profitability has not always scaled at the same rate as sales, and Innodata's gross margins are generally lower compared to the industry average. The necessity for ongoing, heavy investment to keep pace with industry demands and expand its client base could further pressure margins if anticipated AI demand or contract expansions do not materialize as planned.

AI Analysis | Feedback

The rapid advancements in generative artificial intelligence (AI) and large language models (LLMs) pose an emerging threat to Innodata's business model. These technologies are increasingly capable of automating core functions provided by Innodata's Digital Data Solutions (DDS) and Synodex segments, such as data annotation, transformation, curation, and the extraction of structured information from complex unstructured documents like medical records. As AI becomes more proficient and cost-effective in performing these tasks, the demand for human-assisted services and specialized platforms in these areas, which form a significant part of Innodata's offerings, could diminish. Furthermore, for the Agility segment, generative AI could automate aspects of content creation, personalized outreach, and advanced media monitoring, potentially reducing the reliance on Agility's platform for marketing communications and public relations professionals.

AI Analysis | Feedback

Innodata Inc. - Addressable Market Sizes

Digital Data Solutions (DDS) Segment

  • The global data annotation service market, which provides structured labeling, tagging, and classification of raw data for AI and machine learning models, was valued at approximately USD 4.73 billion in 2025 and is projected to grow to USD 7.37 billion by 2034.
  • The global AI training dataset market, closely related to data for training AI and ML algorithms, was valued at USD 3.59 billion in 2025 and is projected to reach USD 23.18 billion by 2034.
  • The global artificial intelligence (AI) in digital transformation market, encompassing solutions to help companies apply AI/ML for analyzing and deriving insights from documents, was valued at USD 424.75 billion in 2025 and is projected to reach USD 1,677.29 billion by 2030.

Synodex Segment

  • The global electronic medical records (EMR) market, which involves transforming medical records into usable digital data, was valued at USD 34.5 billion in 2025 and is estimated to grow to USD 46.34 billion by 2031.
  • The global healthcare data technology market, which includes data integration, interoperability, and AI-driven transformation within healthcare, was estimated at USD 3.10 billion in 2024 and is projected to reach USD 9.5 billion by 2033.

Agility Segment

  • The global public relations (PR) tools market, which assists professionals in targeting and distributing content, was valued at USD 12.7 billion in 2024 and is estimated to reach USD 28.9 billion by 2033.
  • The global media monitoring tools market, providing solutions to monitor and analyze global news channels and social media, was valued at USD 5.7 billion in 2025 and is estimated to reach USD 13.8 billion by 2034.

AI Analysis | Feedback

Innodata Inc. (INOD) is strategically positioned for future revenue growth over the next 2-3 years, primarily driven by its robust involvement in the evolving artificial intelligence (AI) ecosystem and expanding customer engagements.

Here are the key expected drivers of future revenue growth:

  1. Accelerated Demand for AI-Focused Data Solutions (DDS Segment): The Digital Data Solutions (DDS) segment is the primary growth engine for Innodata. The company anticipates continued strong demand for its AI-enabled software platforms and managed services, particularly for training generative AI, advanced machine learning (ML) algorithms, agentic AI, and physical AI. This segment's expansion reflects higher demand for Innodata's data engineering capabilities.
  2. Expansion with Major Technology and Enterprise Customers: Innodata is focused on deepening its relationships with existing large technology clients, including contracts with five of the "Magnificent Seven" tech giants, while also securing new enterprise customers. The company expects increased spending from its largest customers and anticipates that aggregate growth from its broader customer base will meaningfully contribute to customer diversification. This strategy, often described as "land and expand," is validated by strong project wins and a robust pipeline.
  3. Innovation in "Smart Data," Trust & Safety, and Hybrid AI Solutions: Innodata is pivoting towards a "smart data" strategy that involves analyzing model deficiencies and prescribing precise datasets to enhance factuality, safety, and reasoning. The company is also developing capabilities around trust and safety for large language model (LLM) evaluation and is investing in a hybrid AI model that combines human expertise with automation and proprietary evaluation platforms to improve operating leverage and deliver measurable improvements in model outcomes. This includes new data engineering solutions for AI model training, autonomous agents, and robotics.
  4. Growth in the Agility and Federal Segments: The Agility segment, which provides marketing communications and public relations platforms, is expected to continue its growth trajectory, with aspirations for 15-20% annual growth, driven by higher subscription volumes and the integration of AI features. Additionally, Innodata is making inroads into the federal sector, including a notable $25 million contract, and expects it to become a longer-term contributor to revenue as federal investments in technology increase.

AI Analysis | Feedback

Capital Allocation Decisions (2021-2025)

Share Issuance

  • Innodata's diluted weighted average shares outstanding increased from 32.177 million in 2024 to 35.025 million in 2025.

Capital Expenditures

  • Capital expenditures for the year ended December 31, 2025, were $11.104 million, an increase from $7.741 million in 2024.
  • For the three months ended March 31, 2025, capital expenditures were $2.4 million, primarily allocated to the Digital Data Solutions ($1.5 million), Agility ($0.6 million), and Synodex ($0.3 million) segments.
  • These expenditures were principally for the purchase of technology equipment, including servers, network infrastructure, workstations, and capitalized developed software.
  • For the 12 months following March 31, 2025, anticipated capital expenditures for capitalized developed software and technology, equipment, and infrastructure upgrades are projected to be approximately $11.0 million.

Better Bets vs. Innodata (INOD)

Peer Outperformance in IT Consulting & Other Services

INOD has outperformed 93% of its 29 IT Consulting & Other Services peers over 5Y. IT Consulting & Other Services ranks 9th of 12 industries in Information Technology by median 5Y return. The sector’s gains have largely come from elsewhere.
Share of IT Consulting & Other Services constituents that INOD has outperformed, by holding period. Peers without a full price history over a given window are excluded from that window.
1Y
30%
of 37 industry peers · -43.2% return
3Y
94%
of 31 industry peers · 342.6% return
5Y
93%
of 29 industry peers · 343.2% return
No IT Consulting & Other Services peer with a comparable growth profile has beaten INOD by more than 20pp over 5Y.
Median price return by industry across the Information Technology sector, ranked by 5Y. IT Consulting & Other Services is INOD's own industry. Use this to see which corners of the sector have carried the returns.
Industry Names 1Y med3Y med5Y med Top 3 by 5Y
Electronic Manufacturing Services 10 15.6%110.8%187.8% FLEX 499% · TTMI 428% · JBL 303%
Technology Distributors 9 50.7%59.2%59.9% AVT 146% · SNX 118% · CNXN 77%
Semiconductor Materials & Equipment 28 39.5%48.2%43.1% AXTI 370% · KLAC 281% · LRCX 274%
Communications Equipment 36 10.5%99.8%38.1% AAOI 806% · LITE 546% · ANET 461%
Electronic Components 24 20.6%78.5%33.8% CLS 1838% · BELFA 1082% · APH 310%
Electronic Equipment & Instruments 27 -5.1%32.8%-0.4% PI 129% · OSIS 112% · MIR 70%
Semiconductors 55 12.1%44.0%-5.3% MU 893% · AVGO 526% · NVDA 485%
Technology Hardware, Storage & Peripherals 21 13.0%88.7%-8.8% WDC 592% · DELL 581% · SMCI 538%
IT Consulting & Other Services ← 30 -17.7%-7.7%-25.2% APLD 293% · IBM 142% · GIB -10%
Internet Services & Infrastructure 6 -9.8%38.8%-29.7% GDDY 56% · VRSN 33% · AKAM -27%
Systems Software 70 -7.3%17.7%-52.9% ZETA 227% · CRWD 212% · CVLT 145%
Application Software 126 -11.5%-11.7%-55.7% PLTR 598% · FROG 180% · AGYS 141%
Median of constituents with a full price history over each window. Top names are limited to companies above $2B market cap.

Latest Trefis Analyses

Recent Active Movers

Peer Comparisons

Peers to compare with:

Financials

INODGIBDCMHLSQIBEXOPNWMedian
NameInnodata CGI DeFi Cap.Tessera .IBEX OpenWorld 
Mkt Price37.1083.003.160.1350.270.5920.13
Mkt Cap1.317.2--0.7-1.3
Rev LTM35016,4411-668-509
Op Inc LTM522,697-5-60-56
FCF LTM832,325-1-32-58
FCF 3Y Avg452,058-0-29-37
CFO LTM942,591-1-60-77
CFO 3Y Avg542,332-0-49-52

Growth & Margins

INODGIBDCMHLSQIBEXOPNWMedian
NameInnodata CGI DeFi Cap.Tessera .IBEX OpenWorld 
Rev Chg LTM46.9%5.7%-95.0%-15.2%-10.5%
Rev Chg 3Y Avg64.0%5.4%-30.8%-9.0%-7.2%
Rev Chg Q52.9%2.5%-97.1%-15.9%-9.2%
QoQ Delta Rev Chg LTM10.4%0.6%-76.6%-3.7%-2.2%
Op Inc Chg LTM30.2%6.0%-1,912.1%-16.3%-11.1%
Op Inc Chg 3Y Avg248.3%6.0%-661.5%-13.8%-9.9%
Op Mgn LTM14.9%16.4%-429.1%-8.9%-11.9%
Op Mgn 3Y Avg14.3%16.4%-144.9%-8.5%-11.4%
QoQ Delta Op Mgn LTM-2.5%-0.0%-333.1%-0.4%--1.3%
CFO/Rev LTM26.7%15.8%-107.9%-8.9%-12.3%
CFO/Rev 3Y Avg20.3%15.0%-35.7%-8.3%-11.7%
FCF/Rev LTM23.7%14.1%-111.9%-4.8%-9.5%
FCF/Rev 3Y Avg16.2%13.3%-37.0%-5.0%-9.1%

Valuation

INODGIBDCMHLSQIBEXOPNWMedian
NameInnodata CGI DeFi Cap.Tessera .IBEX OpenWorld 
Mkt Cap1.317.2--0.7-1.3
P/S3.61.0--1.0-1.0
P/Op Inc24.46.4--11.2-11.2
P/EBIT24.46.9--11.2-11.2
P/E26.39.9--13.6-13.6
P/CFO13.66.7--11.2-11.2
Total Yield3.8%10.9%--7.3%-7.3%
Dividend Yield0.0%0.8%--0.0%-0.0%
FCF Yield 3Y Avg2.5%11.2%--6.2%-6.2%
D/E0.00.3--0.1-0.1
Net D/E-0.10.2--0.0-0.0

Returns

INODGIBDCMHLSQIBEXOPNWMedian
NameInnodata CGI DeFi Cap.Tessera .IBEX OpenWorld 
1M Rtn-40.2%18.4%-89.3%-54.7%12.9%-86.8%-47.5%
3M Rtn-40.4%10.5%-89.3%-89.8%35.8%-89.1%-64.7%
6M Rtn-64.3%27.3%-89.3%-89.8%54.3%-89.1%-76.7%
12M Rtn-43.0%-3.0%-89.3%-89.8%36.4%-89.1%-66.1%
3Y Rtn344.3%-16.0%-89.3%-89.8%184.7%-89.1%-52.5%
1M Excs Rtn-34.6%24.0%-84.0%-48.4%18.5%-81.2%-41.5%
3M Excs Rtn-36.3%15.6%-84.9%-84.7%41.6%-84.5%-60.4%
6M Excs Rtn-63.8%27.7%-88.8%-89.3%54.8%-88.6%-76.2%
12M Excs Rtn-48.9%-11.3%-98.6%-100.8%26.6%-98.9%-73.7%
3Y Excs Rtn288.6%-87.1%-156.7%-169.6%129.8%-160.4%-121.9%

Comparison Analyses

null

Financials

Segment Financials

Revenue by Segment
$ Mil20252024202320222021
Digital data solutions (DDS)221141625753
Agility2421181513
Synodex78874
Total252170877970


Operating Income by Segment
$ Mil20252024202320042003
Digital data solutions (DDS)39211  
Synodex120  
Agility-01-1  
Content Services   16-0
Professional Services   51
Total40240211


Assets by Segment
$ Mil20252024202320222021
Digital data solutions (DDS)14992372638
Agility1417191919
Synodex55332
Total169113594859


Price Behavior

Price Behavior
Market Price$36.96 
Market Cap ($ Bil)1.3 
First Trading Date08/11/1993 
Distance from 52W High-69.6% 
   50 Days200 Days
DMA Price$56.57$60.79
DMA Trenddownindeterminate
Distance from DMA-34.7%-39.2%
 3M1YR
Volatility87.6%122.3%
Downside Capture325.90384.45
Upside Capture100.27288.07
Correlation (S&P 500)36.3%35.7%
INOD Betas & Captures as of 10/31/2026

 1M2M3M6M1Y3Y
Beta2.312.092.553.483.322.77
Up Beta-23.11-0.142.962.793.701.82
Down Beta1.744.693.602.162.262.49
Up Capture-0%56%111%628%724%69076%
Bmk +ve Days12182862130427
Stock +ve Days8203158109364
Down Capture506%225%278%254%184%114%
Bmk -ve Days10253664121325
Stock -ve Days14233368141384

[1] Upside and downside betas calculated using positive and negative benchmark daily returns respectively
Based On 1-Year Data
Annualized
Return
Annualized
Volatility
Sharpe
Ratio
Correlation
with INOD
INOD-39.4%122.3%0.06-
Sector ETF (XLK)18.7%28.5%0.5941.1%
Equity (SPY)13.3%13.7%0.6534.6%
Gold (GLD)3.7%28.8%0.1218.6%
Commodities (DBC)38.2%21.3%1.422.0%
Real Estate (VNQ)13.7%14.3%0.654.4%
Bitcoin (BTCUSD)-25.3%43.6%-0.5526.5%

Smart multi-asset allocation framework can stack odds in your favor. Learn How
Based On 5-Year Data
Annualized
Return
Annualized
Volatility
Sharpe
Ratio
Correlation
with INOD
INOD26.0%108.4%0.67-
Sector ETF (XLK)17.1%26.5%0.5836.1%
Equity (SPY)11.8%17.3%0.5133.3%
Gold (GLD)17.8%19.1%0.759.2%
Commodities (DBC)8.0%19.6%0.296.5%
Real Estate (VNQ)1.5%18.9%-0.0319.1%
Bitcoin (BTCUSD)4.4%51.6%0.2716.7%

Smart multi-asset allocation framework can stack odds in your favor. Learn How
Based On 10-Year Data
Annualized
Return
Annualized
Volatility
Sharpe
Ratio
Correlation
with INOD
INOD30.4%90.6%0.68-
Sector ETF (XLK)23.1%25.3%0.8327.0%
Equity (SPY)15.3%18.0%0.7224.1%
Gold (GLD)12.1%16.5%0.606.5%
Commodities (DBC)7.6%18.1%0.336.8%
Real Estate (VNQ)6.1%20.7%0.2514.1%
Bitcoin (BTCUSD)60.0%66.3%1.008.0%

Smart multi-asset allocation framework can stack odds in your favor. Learn How

Short Interest

Short Interest: As Of Date10152026
Short Interest: Shares Quantity4.5 Mil
Short Interest: % Change Since 9302026-3.1%
Average Daily Volume1.2 Mil
Days-to-Cover Short Interest3.6 days
Basic Shares Quantity34.4 Mil
Short % of Basic Shares13.1%

Earnings Returns History

Updated 11/10/2026
Expand for More
 Forward Returns
Earnings Date1D Returns5D Returns21D Returns
11/5/2026-4.1%  
8/6/2026-4.8%-5.1%-15.0%
6/17/2026-6.8%-28.6%-39.4%
2/26/2026-7.2%-6.7%-22.0%
11/6/20256.9%-6.7%-8.4%
7/31/2025-18.1%-22.0%-30.8%
5/8/2025-15.8%-13.9%18.4%
2/20/202513.5%-3.6%-23.4%
...
SUMMARY STATS   
# Positive17129
# Negative81215
Median Positive9.7%5.9%15.0%
Median Negative-7.0%-6.7%-15.3%
Max Positive75.8%63.8%124.9%
Max Negative-24.9%-37.8%-48.6%
Collapse to Preview
 Forward Returns
Earnings Date1D Returns5D Returns21D Returns
11/5/2026-4.1%  
8/6/2026-4.8%-5.1%-15.0%
6/17/2026-6.8%-28.6%-39.4%
2/26/2026-7.2%-6.7%-22.0%
11/6/20256.9%-6.7%-8.4%
7/31/2025-18.1%-22.0%-30.8%
5/8/2025-15.8%-13.9%18.4%
2/20/202513.5%-3.6%-23.4%
11/7/202475.8%60.9%79.3%
8/8/202413.3%4.5%-12.9%
5/7/202455.3%63.8%124.9%
2/27/20246.6%3.0%-15.3%
11/2/20239.7%5.1%-10.9%
8/10/202324.6%21.9%-13.6%
5/12/20234.6%6.7%10.3%
2/23/202311.8%7.3%15.0%
11/10/202211.6%0.0%-2.0%
8/11/2022-24.9%-37.8%-48.6%
5/13/2022-5.1%-18.4%-40.9%
3/18/20226.1%4.2%1.4%
11/4/20211.2%-4.9%-25.4%
8/5/20216.4%1.0%1.8%
5/6/20213.2%-3.8%13.8%
3/11/20216.3%-5.2%-11.3%
11/12/202022.7%25.4%73.6%
SUMMARY STATS   
# Positive17129
# Negative81215
Median Positive9.7%5.9%15.0%
Median Negative-7.0%-6.7%-15.3%
Max Positive75.8%63.8%124.9%
Max Negative-24.9%-37.8%-48.6%

SEC Filings

Expand for More
Report DateFiling DateFiling
09/30/202611/05/202610-Q
06/30/202608/06/202610-Q
03/31/202605/07/202610-Q
12/31/202502/26/202610-K
09/30/202511/06/202510-Q
06/30/202507/31/202510-Q
03/31/202505/09/202510-Q
12/31/202402/24/202510-K
09/30/202411/07/202410-Q
06/30/202408/09/202410-Q
03/31/202405/08/202410-Q
12/31/202303/04/202410-K
09/30/202311/03/202310-Q
06/30/202308/11/202310-Q
03/31/202305/12/202310-Q
12/31/202202/24/202310-K
Collapse to Preview
Report DateFiling DateFiling
09/30/202611/05/202610-Q
06/30/202608/06/202610-Q
03/31/202605/07/202610-Q
12/31/202502/26/202610-K
09/30/202511/06/202510-Q
06/30/202507/31/202510-Q
03/31/202505/09/202510-Q
12/31/202402/24/202510-K
09/30/202411/07/202410-Q
06/30/202408/09/202410-Q
03/31/202405/08/202410-Q
12/31/202303/04/202410-K
09/30/202311/03/202310-Q
06/30/202308/11/202310-Q
03/31/202305/12/202310-Q
12/31/202202/24/202310-K
09/30/202211/10/202210-Q
06/30/202208/12/202210-Q
03/31/202205/13/202210-Q
12/31/202103/24/202210-K
09/30/202111/04/202110-Q
06/30/202108/05/202110-Q
03/31/202105/06/202110-Q
12/31/202003/15/202110-K
09/30/202011/12/202010-Q
06/30/202008/06/202010-Q
03/31/202005/14/202010-Q
12/31/201903/16/202010-K

Recent Forward Guidance

Updated 11/6/2026

Latest: Q3 2026 Earnings Reported 11/5/2026

Forward GuidanceGuidance Change
MetricSourceLowMidHigh% Chg% DeltaChangePrior
2026 Revenue GrowthReported42.0%42.0%  2.0%RaisedGuidance: 40.0% for 2026
2027 Hyperscaler Customer RevenueReported20.00 Mil20.00 Mil    
2027 Robotics Pilot RevenueReported 7.00 Mil    


Prior: Q2 2026 Earnings Reported 8/6/2026

Forward GuidanceGuidance Change
MetricSourceLowMidHigh% Chg% DeltaChangePrior
2026 Revenue GrowthReported 40.0%  0.0%AffirmedGuidance: 40.0% for 2026

Q2 2026 Earnings Reported 6/17/2026

Forward GuidanceGuidance Change
MetricSourceLowMidHigh% Chg% DeltaChangePrior
2026 Revenue GrowthReported 40.0%  5.0%RaisedGuidance: 35.0% for 2026

Q4 2025 Earnings Reported 2/26/2026

Forward GuidanceGuidance Change
MetricSourceLowMidHigh% Chg% DeltaChangePrior
2026 Revenue GrowthReported 35.0%  -10.0%Lower NewGuidance: 45.0% for 2025

Insider Activity

Updated 10/13/2026
Expand for More
#OwnerTitleHoldingActionFiling DatePriceSharesTransacted
Value
Value of
Held Shares
Form
1Forlenza, Louise CDirectSell915202653.5410,000535,400290,401Form
2Forlenza, Louise CDirectSell904202655.0110,000550,100298,374Form
3Abuhoff, JackCEODirectSell6162026110.20200,00022,040,948147,724,607Form
4Abuhoff, JackCEODirectSell6162026105.7094,0599,942,343141,690,570Form
5Mishra, AshokEVP and COODirectSell6032026114.3438,6664,421,1079,510,766Form
Collapse to Preview
#OwnerTitleHoldingActionFiling DatePriceSharesTransacted
Value
Value of
Held Shares
Form
1Forlenza, Louise CDirectSell915202653.5410,000535,400290,401Form
2Forlenza, Louise CDirectSell904202655.0110,000550,100298,374Form
3Abuhoff, JackCEODirectSell6162026110.20200,00022,040,948147,724,607Form
4Abuhoff, JackCEODirectSell6162026105.7094,0599,942,343141,690,570Form
5Mishra, AshokEVP and COODirectSell6032026114.3438,6664,421,1079,510,766Form
6Espineli, Marissa BInterim CFODirectSell6012026105.8819,6672,082,4362,966,256Form
7Abuhoff, JackCEODirectSell6012026103.50105,58610,928,426138,740,694Form
8Abuhoff, JackCEODirectSell5262026100.3938,0563,820,591134,573,622Form
9Forlenza, Louise CDirectSell520202692.3315,0001,384,902364,045Form
10Abuhoff, JackCEODirectSell520202693.46150,00014,019,050125,279,467Form
11Forlenza, Louise CDirectSell520202686.105,000430,500339,492Form
12Abuhoff, JackCEODirectSell520202693.032,800260,484124,702,622Form
13Abuhoff, JackCEODirectSell518202694.39150,00014,157,845126,519,788Form
14Abuhoff, JackCEODirectSell518202695.78100,0009,577,910128,387,666Form
15Massey, Stewart RSEP IRASell518202696.0010,000960,0001,056,000Form
16Mishra, AshokEVP and COODirectSell513202690.15242,90121,897,4588,580,360Form
17Forlenza, Louise CDirectSell513202688.8630,0002,665,783350,373Form
18Mishra, AshokEVP and COODirectSell1208202560.1623,6541,423,0253,609,600Form
19Forlenza, Louise CDirectSell1112202565.008,278538,070256,295Form
20Mishra, AshokEVP and COODirectSell1112202561.39200,00012,278,9393,683,682Form
21Mishra, AshokEVP and COODirectSell1112202562.5228,0941,756,4373,751,200Form
Core Cache Last Updated: 11/9/2026