Tearsheet

Innodata (INOD)


Market Price (5/12/2026): $104.41 | Market Cap: $3.4 Bil
Sector: Information Technology | Industry: IT Consulting & Other Services

Innodata (INOD)


Market Price (5/12/2026): $104.41
Market Cap: $3.4 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

Strong revenue growth
Rev Chg LTMRevenue Change % Last Twelve Months (LTM) is 40%

Attractive cash flow generation
CFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is 26%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is 22%

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 16%

Stock price has recently run up significantly
12M Rtn12 month market price return is 145%

Valuation getting more expensive
P/S 6M Chg %Price/Sales change over 6 months. Declining P/S indicates valuation has become less expensive. is 50%

Yield minus risk free rate is negative
ERPEquity Risk Premium (ERP) = Total Yield - Risk Free Rate, Reflects the premium above risk free assets offered by the investment. is -2.8%

High stock price volatility
Vol 12M is 120%

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

0 Strong revenue growth
Rev Chg LTMRevenue Change % Last Twelve Months (LTM) is 40%
1 Attractive cash flow generation
CFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is 26%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is 22%
2 Megatrend and thematic drivers
Megatrends include Artificial Intelligence. Themes include AI Software Platforms, AI Data Annotation & Curation, and Generative AI Data Services.
3 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 16%
4 Stock price has recently run up significantly
12M Rtn12 month market price return is 145%
5 Valuation getting more expensive
P/S 6M Chg %Price/Sales change over 6 months. Declining P/S indicates valuation has become less expensive. is 50%
6 Yield minus risk free rate is negative
ERPEquity Risk Premium (ERP) = Total Yield - Risk Free Rate, Reflects the premium above risk free assets offered by the investment. is -2.8%
7 High stock price volatility
Vol 12M is 120%
8 Key risks
INOD key risks include [1] an extreme client concentration, Show more.

Valuation, Metrics & Events

Price Chart

Why The Stock Moved

Qualitative Assessment

AI Analysis | Feedback

Innodata (INOD) stock has gained about 85% since 1/31/2026 because of the following key factors:

1. Innodata reported exceptionally strong First Quarter 2026 financial results, significantly surpassing analyst expectations. The company's revenue surged 54% year-over-year to $90.1 million, exceeding the consensus estimate by 18%. Additionally, adjusted EBITDA reached $25.0 million, beating consensus by 139%, and diluted earnings per share (EPS) of $0.42 crushed analyst expectations of $0.08 by $0.34.

2. The company raised its full-year 2026 revenue growth guidance and secured significant new engagements with a major technology customer. Innodata increased its full-year 2026 revenue growth forecast to approximately 40% or more, up from previous guidance of approximately 35% or more. This was bolstered by new engagements with a "Big Tech company" expected to generate approximately $51 million in revenue during 2026, positioning this client to become Innodata's second-largest customer.

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Stock Movement Drivers

Fundamental Drivers

The 87.3% change in INOD stock from 1/31/2026 to 5/11/2026 was primarily driven by a 64.3% change in the company's P/E Multiple.
(LTM values as of)13120265112026Change
Stock Price ($)55.44103.8387.3%
Change Contribution By: 
Total Revenues ($ Mil)23828318.8%
Net Income Margin (%)14.1%13.9%-1.7%
P/E Multiple52.586.264.3%
Shares Outstanding (Mil)3233-2.4%
Cumulative Contribution87.3%

LTM = Last Twelve Months as of date shown

Market Drivers

1/31/2026 to 5/11/2026
ReturnCorrelation
INOD87.3% 
Market (SPY)3.6%66.0%
Sector (XLK)23.8%48.6%

Fundamental Drivers

The 39.2% change in INOD stock from 10/31/2025 to 5/11/2026 was primarily driven by a 55.2% change in the company's P/E Multiple.
(LTM values as of)103120255112026Change
Stock Price ($)74.61103.8339.2%
Change Contribution By: 
Total Revenues ($ Mil)22828324.2%
Net Income Margin (%)18.7%13.9%-25.9%
P/E Multiple55.586.255.2%
Shares Outstanding (Mil)3233-2.6%
Cumulative Contribution39.2%

LTM = Last Twelve Months as of date shown

Market Drivers

10/31/2025 to 5/11/2026
ReturnCorrelation
INOD39.2% 
Market (SPY)5.5%53.0%
Sector (XLK)18.6%46.4%

Fundamental Drivers

The 174.5% change in INOD stock from 4/30/2025 to 5/11/2026 was primarily driven by a 117.5% change in the company's P/E Multiple.
(LTM values as of)43020255112026Change
Stock Price ($)37.82103.83174.5%
Change Contribution By: 
Total Revenues ($ Mil)17028366.3%
Net Income Margin (%)16.8%13.9%-17.5%
P/E Multiple39.686.2117.5%
Shares Outstanding (Mil)3033-7.9%
Cumulative Contribution174.5%

LTM = Last Twelve Months as of date shown

Market Drivers

4/30/2025 to 5/11/2026
ReturnCorrelation
INOD174.5% 
Market (SPY)30.4%47.9%
Sector (XLK)70.4%45.6%

Fundamental Drivers

The 1468.4% change in INOD stock from 4/30/2023 to 5/11/2026 was primarily driven by a 420.7% change in the company's P/S Multiple.
(LTM values as of)43020235112026Change
Stock Price ($)6.62103.831468.4%
Change Contribution By: 
Total Revenues ($ Mil)79283258.7%
P/S Multiple2.312.0420.7%
Shares Outstanding (Mil)2733-16.0%
Cumulative Contribution1468.4%

LTM = Last Twelve Months as of date shown

Market Drivers

4/30/2023 to 5/11/2026
ReturnCorrelation
INOD1468.4% 
Market (SPY)78.7%38.4%
Sector (XLK)140.8%40.1%

Return vs. Risk

Price Returns Compared

 202120222023202420252026Total [1]
Returns
INOD Return12%-50%175%386%29%67%1502%
Peers Return-1%-34%19%32%6%-19%-11%
S&P 500 Return27%-19%24%23%16%8%97%

Monthly Win Rates [3]
INOD Win Rate50%33%67%50%50%60% 
Peers Win Rate50%37%58%63%52%36% 
S&P 500 Win Rate75%42%67%75%67%60% 

Max Drawdowns [4]
INOD Max Drawdown-5%-52%0%-30%-25%-32% 
Peers Max Drawdown-25%-43%-11%-14%-17%-28% 
S&P 500 Max Drawdown-1%-25%-1%-2%-15%-7% 


[1] Cumulative total returns since the beginning of 2021
[2] Peers: IBM, ACN, ZM, CTSH, TOST.
[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 5/11/2026 (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 IBM, ACN, ZM, CTSH, TOST

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 IBM, ACN, ZM, CTSH, TOST

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. operates as a global data engineering company in the United States, the United Kingdom, the Netherlands, Canada, and internationally. The company operates through three segments: Digital Data Solutions (DDS), Synodex, and Agility. The DDS segment offers AI-enabled software platforms and managed services to companies that require data for training AI and machine learning (ML) algorithms, and AI digital transformation solutions to help companies apply AI/ML for problems relating to analyzing and deriving insights from documents. This segment provides a range of data engineering support services, including data annotation, data transformation, data transformation, data curation, data hygiene, data consolidation, data compliance, and master data management. The Synodex segment offers an industry platform that transforms medical records into useable digital data with its proprietary data models or client data models. The Agility segment provides an industry platform that provides marketing communications and public relations professionals to target and distribute content to journalists and social media influencers; and to monitor and analyze global news channels, such as print, web, radio, and TV, as well as social media channels. It serves banking, insurance, financial services, technology, digital retailing, and information/media sectors through its professional staff, senior management, and direct sales personnel. The company was formerly known as Innodata Isogen, Inc. and changed its name to Innodata Inc. in June 2012. Innodata Inc. was incorporated in 1988 and is headquartered in Ridgefield Park, New Jersey.

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.

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Recent Active Movers

Peer Comparisons

Peers to compare with:

Financials

INODIBMACNZMCTSHTOSTMedian
NameInnodata Internat.AccentureZoom Com.Cognizan.Toast  
Mkt Price103.83223.55172.35107.1249.2524.19105.47
Mkt Cap3.4209.8106.331.823.514.227.6
Rev LTM28368,91272,1104,86921,4066,44613,926
Op Inc LTM4912,94611,3231,1243,3793642,251
FCF LTM6212,25812,4971,9242,4706542,197
FCF 3Y Avg3312,26910,3471,7352,0013961,868
CFO LTM7313,99213,0801,9892,7577142,373
CFO 3Y Avg4113,98810,9291,8442,2944482,069

Growth & Margins

INODIBMACNZMCTSHTOSTMedian
NameInnodata Internat.AccentureZoom Com.Cognizan.Toast  
Rev Chg LTM40.1%9.7%7.3%4.4%6.5%23.4%8.5%
Rev Chg 3Y Avg59.2%4.5%4.5%3.5%3.4%29.0%4.5%
Rev Chg Q54.4%9.5%8.3%5.3%5.8%21.9%8.9%
QoQ Delta Rev Chg LTM12.6%2.0%2.0%1.3%1.4%4.8%2.0%
Op Inc Chg LTM55.1%26.0%9.3%38.2%9.0%191.2%32.1%
Op Inc Chg 3Y Avg329.7%15.6%5.5%69.0%4.8%131.6%42.3%
Op Mgn LTM17.1%18.8%15.7%23.1%15.8%5.6%16.4%
Op Mgn 3Y Avg12.1%16.8%15.5%17.4%15.5%1.0%15.5%
QoQ Delta Op Mgn LTM1.3%0.3%0.0%0.2%0.0%0.7%0.3%
CFO/Rev LTM25.8%20.3%18.1%40.9%12.9%11.1%19.2%
CFO/Rev 3Y Avg18.9%21.7%16.0%39.3%11.3%8.0%17.4%
FCF/Rev LTM21.9%17.8%17.3%39.5%11.5%10.1%17.6%
FCF/Rev 3Y Avg14.3%19.0%15.2%36.9%9.8%7.0%14.7%

Valuation

INODIBMACNZMCTSHTOSTMedian
NameInnodata Internat.AccentureZoom Com.Cognizan.Toast  
Mkt Cap3.4209.8106.331.823.514.227.6
P/S12.03.01.56.51.12.22.6
P/Op Inc69.816.29.428.37.039.022.2
P/EBIT69.816.89.928.36.739.022.5
P/E86.219.513.916.710.534.518.1
P/CFO46.315.08.116.08.519.915.5
Total Yield1.2%8.1%10.8%6.0%10.1%2.9%7.1%
Dividend Yield0.0%3.0%3.6%0.0%0.6%0.0%0.3%
FCF Yield 3Y Avg3.5%5.9%5.3%7.1%6.0%2.4%5.6%
D/E0.00.30.10.00.00.00.0
Net D/E-0.00.3-0.0-0.2-0.0-0.1-0.0

Returns

INODIBMACNZMCTSHTOSTMedian
NameInnodata Internat.AccentureZoom Com.Cognizan.Toast  
1M Rtn191.8%-2.4%-4.0%35.2%-15.0%-4.8%-3.2%
3M Rtn113.6%-23.6%-26.6%12.3%-33.6%-15.6%-19.6%
6M Rtn51.5%-26.7%-28.5%28.8%-31.8%-36.6%-27.6%
12M Rtn199.9%-8.0%-42.6%31.7%-36.7%-40.8%-22.3%
3Y Rtn1,017.7%100.5%-34.1%68.2%-16.0%24.1%46.2%
1M Excs Rtn183.1%-11.2%-12.7%26.4%-23.7%-13.5%-11.9%
3M Excs Rtn107.2%-30.0%-33.0%5.9%-40.1%-22.0%-26.0%
6M Excs Rtn60.2%-37.4%-37.9%20.0%-42.0%-43.8%-37.6%
12M Excs Rtn121.7%-40.1%-73.6%2.6%-67.6%-64.9%-52.5%
3Y Excs Rtn1,604.6%21.1%-114.0%-10.4%-100.2%-45.1%-27.7%

Comparison Analyses

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Financials

Segment Financials

Assets by Segment
$ Mil20252024202320222021
Digital data solutions (DDS)9237264028
Agility1719191729
Synodex53320
Total11359485957


Price Behavior

Price Behavior
Market Price$103.83 
Market Cap ($ Bil)3.3 
First Trading Date08/11/1993 
Distance from 52W High0.0% 
   50 Days200 Days
DMA Price$44.77$54.51
DMA Trendindeterminatedown
Distance from DMA131.9%90.5%
 3M1YR
Volatility189.3%120.9%
Downside Capture-0.801.18
Upside Capture227.08298.49
Correlation (SPY)21.6%32.6%
INOD Betas & Captures as of 4/30/2026

 1M2M3M6M1Y3Y
Beta3.883.013.243.173.212.79
Up Beta4.133.894.074.282.801.74
Down Beta3.372.012.162.493.192.69
Up Capture247%262%292%250%823%82862%
Bmk +ve Days15223166141428
Stock +ve Days12213050120363
Down Capture835%295%307%247%202%113%
Bmk -ve Days4183056108321
Stock -ve Days10223474130385

[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
INOD138.3%121.7%1.18-
Sector ETF (XLK)64.5%20.8%2.2946.1%
Equity (SPY)28.1%12.5%1.7848.7%
Gold (GLD)42.9%26.9%1.309.3%
Commodities (DBC)48.6%18.0%2.142.7%
Real Estate (VNQ)13.6%13.5%0.709.8%
Bitcoin (BTCUSD)-22.4%41.7%-0.5024.9%

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Based On 5-Year Data
Annualized
Return
Annualized
Volatility
Sharpe
Ratio
Correlation
with INOD
INOD72.0%105.8%0.95-
Sector ETF (XLK)22.0%24.8%0.7835.3%
Equity (SPY)12.9%17.1%0.5934.4%
Gold (GLD)21.2%17.9%0.966.8%
Commodities (DBC)13.5%19.1%0.587.9%
Real Estate (VNQ)3.6%18.8%0.0920.4%
Bitcoin (BTCUSD)8.5%56.0%0.3614.1%

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Based On 10-Year Data
Annualized
Return
Annualized
Volatility
Sharpe
Ratio
Correlation
with INOD
INOD46.1%88.7%0.80-
Sector ETF (XLK)25.1%24.4%0.9325.8%
Equity (SPY)15.0%17.9%0.7224.2%
Gold (GLD)13.4%15.9%0.704.8%
Commodities (DBC)9.5%17.7%0.457.1%
Real Estate (VNQ)5.6%20.7%0.2414.8%
Bitcoin (BTCUSD)68.1%66.9%1.077.5%

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

Short Interest

Short Interest: As Of Date4302026
Short Interest: Shares Quantity5.3 Mil
Short Interest: % Change Since 4152026-0.6%
Average Daily Volume0.8 Mil
Days-to-Cover Short Interest6.2 days
Basic Shares Quantity32.6 Mil
Short % of Basic Shares16.1%

Earnings Returns History

Expand for More
 Forward Returns
Earnings Date1D Returns5D Returns21D Returns
5/7/202686.0%  
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%
...
SUMMARY STATS   
# Positive17129
# Negative5912
Median Positive11.6%7.0%18.4%
Median Negative-16.6%-6.7%-17.8%
Max Positive86.0%63.8%124.9%
Max Negative-24.9%-37.8%-48.6%

SEC Filings

Expand for More
Report DateFiling DateFiling
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

Recent Forward Guidance [BETA]

Latest: Q1 2026 Earnings Reported 5/7/2026

Forward GuidanceGuidance Change
MetricLowMidHigh% Chg% DeltaChangePrior
2026 Revenue Growth 40.0% 14.3%5.0%RaisedGuidance: 35.0% for 2026
2026 Revenue 51.00 Mil   Higher New

Prior: Q4 2025 Earnings Reported 2/26/2026

Forward GuidanceGuidance Change
MetricLowMidHigh% Chg% DeltaChangePrior
2026 Revenue Growth 35.0% -22.2%-10.0%LoweredGuidance: 45.0% for 2025

Insider Activity

Expand for More
#OwnerTitleHoldingActionFiling DatePriceSharesTransacted
Value
Value of
Held Shares
Form
1Mishra, AshokEVP and COODirectSell1208202560.1623,6541,423,0253,609,600Form
2Forlenza, Louise CDirectSell1112202565.008,278538,070256,295Form
3Mishra, AshokEVP and COODirectSell1112202561.39200,00012,278,9393,683,682Form
4Mishra, AshokEVP and COODirectSell1112202562.5228,0941,756,4373,751,200Form