Innodata (INOD)
Market Price (5/12/2026): $104.41 | Market Cap: $3.4 BilSector: Information Technology | Industry: IT Consulting & Other Services
Innodata (INOD)
Market Price (5/12/2026): $104.41Market Cap: $3.4 BilSector: Information TechnologyIndustry: IT Consulting & Other Services
Investment Highlights Why It Matters Detailed financial logic regarding cash flow yields vs trend-riding momentum.
Strong revenue growthRev Chg LTMRevenue Change % Last Twelve Months (LTM) is 40% Attractive cash flow generationCFO/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 driversMegatrends include Artificial Intelligence. Themes include AI Software Platforms, AI Data Annotation & Curation, and Generative AI Data Services. | Meaningful short interestShort 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 significantly12M Rtn12 month market price return is 145% Valuation getting more expensiveP/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 negativeERPEquity 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 volatilityVol 12M is 120% Key risksINOD key risks include [1] an extreme client concentration, Show more. |
| Strong revenue growthRev Chg LTMRevenue Change % Last Twelve Months (LTM) is 40% |
| Attractive cash flow generationCFO/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 driversMegatrends include Artificial Intelligence. Themes include AI Software Platforms, AI Data Annotation & Curation, and Generative AI Data Services. |
| Meaningful short interestShort 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 significantly12M Rtn12 month market price return is 145% |
| Valuation getting more expensiveP/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 negativeERPEquity 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 volatilityVol 12M is 120% |
| Key risksINOD key risks include [1] an extreme client concentration, Show more. |
Qualitative Assessment
AI Analysis | Feedback
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 $51 million in revenue during 2026, positioning this client to become Innodata's second-largest customer.
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Stock Movement Drivers
Fundamental Drivers
The 87.3% change in INOD stock from 1/31/2026 to 5/11/2026 was primarily driven by a 64.3% change in the company's P/E Multiple.| (LTM values as of) | 1312026 | 5112026 | Change |
|---|---|---|---|
| Stock Price ($) | 55.44 | 103.83 | 87.3% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 238 | 283 | 18.8% |
| Net Income Margin (%) | 14.1% | 13.9% | -1.7% |
| P/E Multiple | 52.5 | 86.2 | 64.3% |
| Shares Outstanding (Mil) | 32 | 33 | -2.4% |
| Cumulative Contribution | 87.3% |
Market Drivers
1/31/2026 to 5/11/2026| Return | Correlation | |
|---|---|---|
| INOD | 87.3% | |
| 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) | 10312025 | 5112026 | Change |
|---|---|---|---|
| Stock Price ($) | 74.61 | 103.83 | 39.2% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 228 | 283 | 24.2% |
| Net Income Margin (%) | 18.7% | 13.9% | -25.9% |
| P/E Multiple | 55.5 | 86.2 | 55.2% |
| Shares Outstanding (Mil) | 32 | 33 | -2.6% |
| Cumulative Contribution | 39.2% |
Market Drivers
10/31/2025 to 5/11/2026| Return | Correlation | |
|---|---|---|
| INOD | 39.2% | |
| 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) | 4302025 | 5112026 | Change |
|---|---|---|---|
| Stock Price ($) | 37.82 | 103.83 | 174.5% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 170 | 283 | 66.3% |
| Net Income Margin (%) | 16.8% | 13.9% | -17.5% |
| P/E Multiple | 39.6 | 86.2 | 117.5% |
| Shares Outstanding (Mil) | 30 | 33 | -7.9% |
| Cumulative Contribution | 174.5% |
Market Drivers
4/30/2025 to 5/11/2026| Return | Correlation | |
|---|---|---|
| INOD | 174.5% | |
| 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) | 4302023 | 5112026 | Change |
|---|---|---|---|
| Stock Price ($) | 6.62 | 103.83 | 1468.4% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 79 | 283 | 258.7% |
| P/S Multiple | 2.3 | 12.0 | 420.7% |
| Shares Outstanding (Mil) | 27 | 33 | -16.0% |
| Cumulative Contribution | 1468.4% |
Market Drivers
4/30/2023 to 5/11/2026| Return | Correlation | |
|---|---|---|
| INOD | 1468.4% | |
| Market (SPY) | 78.7% | 38.4% |
| Sector (XLK) | 140.8% | 40.1% |
Price Returns Compared
| 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | Total [1] | |
|---|---|---|---|---|---|---|---|
| Returns | |||||||
| INOD Return | 12% | -50% | 175% | 386% | 29% | 67% | 1502% |
| Peers Return | -1% | -34% | 19% | 32% | 6% | -19% | -11% |
| S&P 500 Return | 27% | -19% | 24% | 23% | 16% | 8% | 97% |
Monthly Win Rates [3] | |||||||
| INOD Win Rate | 50% | 33% | 67% | 50% | 50% | 60% | |
| Peers Win Rate | 50% | 37% | 58% | 63% | 52% | 36% | |
| S&P 500 Win Rate | 75% | 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
| Event | INOD | S&P 500 |
|---|---|---|
| 2025 US Tariff Shock | ||
| % Loss | -50.1% | -18.8% |
| % Gain to Breakeven | 100.5% | 23.1% |
| Time to Breakeven | 143 days | 79 days |
| Summer-Fall 2023 Five Percent Yield Shock | ||
| % Loss | -48.4% | -9.5% |
| % Gain to Breakeven | 93.9% | 10.5% |
| Time to Breakeven | 177 days | 24 days |
| 2022 Inflation Shock & Fed Tightening | ||
| % Loss | -50.9% | -24.5% |
| % Gain to Breakeven | 103.8% | 32.4% |
| Time to Breakeven | 131 days | 427 days |
| 2020 COVID-19 Crash | ||
| % Loss | -33.9% | -33.7% |
| % Gain to Breakeven | 51.3% | 50.9% |
| Time to Breakeven | 49 days | 140 days |
| 2016-2017 Trump Reflation Bond Selloff | ||
| % Loss | -36.0% | -3.7% |
| % Gain to Breakeven | 56.1% | 3.9% |
| Time to Breakeven | 1190 days | 6 days |
| 2015-2016 China Devaluation / Global Growth Scare | ||
| % Loss | -14.2% | -12.2% |
| % Gain to Breakeven | 16.5% | 13.9% |
| Time to Breakeven | 39 days | 62 days |
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.
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Asset Allocation
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| Event | INOD | S&P 500 |
|---|---|---|
| 2025 US Tariff Shock | ||
| % Loss | -50.1% | -18.8% |
| % Gain to Breakeven | 100.5% | 23.1% |
| Time to Breakeven | 143 days | 79 days |
| Summer-Fall 2023 Five Percent Yield Shock | ||
| % Loss | -48.4% | -9.5% |
| % Gain to Breakeven | 93.9% | 10.5% |
| Time to Breakeven | 177 days | 24 days |
| 2022 Inflation Shock & Fed Tightening | ||
| % Loss | -50.9% | -24.5% |
| % Gain to Breakeven | 103.8% | 32.4% |
| Time to Breakeven | 131 days | 427 days |
| 2020 COVID-19 Crash | ||
| % Loss | -33.9% | -33.7% |
| % Gain to Breakeven | 51.3% | 50.9% |
| Time to Breakeven | 49 days | 140 days |
| 2016-2017 Trump Reflation Bond Selloff | ||
| % Loss | -36.0% | -3.7% |
| % Gain to Breakeven | 56.1% | 3.9% |
| Time to Breakeven | 1190 days | 6 days |
| 2014-2016 Oil Price Collapse | ||
| % Loss | -29.7% | -6.8% |
| % Gain to Breakeven | 42.2% | 7.3% |
| Time to Breakeven | 1810 days | 15 days |
| 2013 Taper Tantrum | ||
| % Loss | -27.2% | -0.2% |
| % Gain to Breakeven | 37.3% | 0.2% |
| Time to Breakeven | 102 days | 1 days |
| 2010 Eurozone Sovereign Debt Crisis / Flash Crash | ||
| % Loss | -33.2% | -15.4% |
| % Gain to Breakeven | 49.8% | 18.2% |
| Time to Breakeven | 546 days | 125 days |
| 2008-2009 Global Financial Crisis | ||
| % Loss | -73.4% | -53.4% |
| % Gain to Breakeven | 276.6% | 114.4% |
| Time to Breakeven | 265 days | 1085 days |
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)
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
nullAI 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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.
- 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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| 04302026 | PLTR | Palantir Technologies | Monopoly | MY | Getting CheaperMonopoly-Like with P/S DeclineLarge cap with monopoly-like margins or cash flow generation and getting cheaper based on P/S multiple | 0.0% | 0.0% | 0.0% |
| 04102026 | ADSK | Autodesk | Dip Buy | DB | FCFY OPMDip Buy with High FCF Yield and High MarginBuying dips for companies with high FCF yield and meaningfully high operating margin | 8.5% | 8.5% | 0.0% |
| 04102026 | BSY | Bentley Systems | Dip Buy | DB | FCFY OPMDip Buy with High FCF Yield and High MarginBuying dips for companies with high FCF yield and meaningfully high operating margin | 4.2% | 4.2% | 0.0% |
| 04102026 | ENPH | Enphase Energy | Dip Buy | DB | P/E OPMDip Buy with Low PE and High MarginBuying dips for companies with tame PE and meaningfully high operating margin | 5.7% | 5.7% | 0.0% |
| 04102026 | BL | BlackLine | Dip Buy | DB | CFO/Rev | Low D/EDip Buy with High Cash Flow MarginsBuying dips for companies with significant cash flows from operations and reasonable debt / market cap | 3.2% | 3.2% | -3.0% |
Research & Analysis
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Peer Comparisons
| Peers to compare with: |
Financials
| Median | |
|---|---|
| Name | |
| Mkt Price | 105.47 |
| Mkt Cap | 27.6 |
| Rev LTM | 13,926 |
| Op Inc LTM | 2,251 |
| FCF LTM | 2,197 |
| FCF 3Y Avg | 1,868 |
| CFO LTM | 2,373 |
| CFO 3Y Avg | 2,069 |
Growth & Margins
| Median | |
|---|---|
| Name | |
| Rev Chg LTM | 8.5% |
| Rev Chg 3Y Avg | 4.5% |
| Rev Chg Q | 8.9% |
| QoQ Delta Rev Chg LTM | 2.0% |
| Op Inc Chg LTM | 32.1% |
| Op Inc Chg 3Y Avg | 42.3% |
| Op Mgn LTM | 16.4% |
| Op Mgn 3Y Avg | 15.5% |
| QoQ Delta Op Mgn LTM | 0.3% |
| CFO/Rev LTM | 19.2% |
| CFO/Rev 3Y Avg | 17.4% |
| FCF/Rev LTM | 17.6% |
| FCF/Rev 3Y Avg | 14.7% |
Valuation
| Median | |
|---|---|
| Name | |
| Mkt Cap | 27.6 |
| P/S | 2.6 |
| P/Op Inc | 22.2 |
| P/EBIT | 22.5 |
| P/E | 18.1 |
| P/CFO | 15.5 |
| Total Yield | 7.1% |
| Dividend Yield | 0.3% |
| FCF Yield 3Y Avg | 5.6% |
| D/E | 0.0 |
| Net D/E | -0.0 |
Returns
| Median | |
|---|---|
| Name | |
| 1M Rtn | -3.2% |
| 3M Rtn | -19.6% |
| 6M Rtn | -27.6% |
| 12M Rtn | -22.3% |
| 3Y Rtn | 46.2% |
| 1M Excs Rtn | -11.9% |
| 3M Excs Rtn | -26.0% |
| 6M Excs Rtn | -37.6% |
| 12M Excs Rtn | -52.5% |
| 3Y Excs Rtn | -27.7% |
Price Behavior
| Market Price | $103.83 | |
| Market Cap ($ Bil) | 3.3 | |
| First Trading Date | 08/11/1993 | |
| Distance from 52W High | 0.0% | |
| 50 Days | 200 Days | |
| DMA Price | $44.77 | $54.51 |
| DMA Trend | indeterminate | down |
| Distance from DMA | 131.9% | 90.5% |
| 3M | 1YR | |
| Volatility | 189.3% | 120.9% |
| Downside Capture | -0.80 | 1.18 |
| Upside Capture | 227.08 | 298.49 |
| Correlation (SPY) | 21.6% | 32.6% |
| 1M | 2M | 3M | 6M | 1Y | 3Y | |
|---|---|---|---|---|---|---|
| Beta | 3.88 | 3.01 | 3.24 | 3.17 | 3.21 | 2.79 |
| Up Beta | 4.13 | 3.89 | 4.07 | 4.28 | 2.80 | 1.74 |
| Down Beta | 3.37 | 2.01 | 2.16 | 2.49 | 3.19 | 2.69 |
| Up Capture | 247% | 262% | 292% | 250% | 823% | 82862% |
| Bmk +ve Days | 15 | 22 | 31 | 66 | 141 | 428 |
| Stock +ve Days | 12 | 21 | 30 | 50 | 120 | 363 |
| Down Capture | 835% | 295% | 307% | 247% | 202% | 113% |
| Bmk -ve Days | 4 | 18 | 30 | 56 | 108 | 321 |
| Stock -ve Days | 10 | 22 | 34 | 74 | 130 | 385 |
[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 | 138.3% | 121.7% | 1.18 | - |
| Sector ETF (XLK) | 64.5% | 20.8% | 2.29 | 46.1% |
| Equity (SPY) | 28.1% | 12.5% | 1.78 | 48.7% |
| Gold (GLD) | 42.9% | 26.9% | 1.30 | 9.3% |
| Commodities (DBC) | 48.6% | 18.0% | 2.14 | 2.7% |
| Real Estate (VNQ) | 13.6% | 13.5% | 0.70 | 9.8% |
| Bitcoin (BTCUSD) | -22.4% | 41.7% | -0.50 | 24.9% |
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Based On 5-Year Data
| Annualized Return | Annualized Volatility | Sharpe Ratio | Correlation with INOD | |
|---|---|---|---|---|
| INOD | 72.0% | 105.8% | 0.95 | - |
| Sector ETF (XLK) | 22.0% | 24.8% | 0.78 | 35.3% |
| Equity (SPY) | 12.9% | 17.1% | 0.59 | 34.4% |
| Gold (GLD) | 21.2% | 17.9% | 0.96 | 6.8% |
| Commodities (DBC) | 13.5% | 19.1% | 0.58 | 7.9% |
| Real Estate (VNQ) | 3.6% | 18.8% | 0.09 | 20.4% |
| Bitcoin (BTCUSD) | 8.5% | 56.0% | 0.36 | 14.1% |
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 | |
|---|---|---|---|---|
| INOD | 46.1% | 88.7% | 0.80 | - |
| Sector ETF (XLK) | 25.1% | 24.4% | 0.93 | 25.8% |
| Equity (SPY) | 15.0% | 17.9% | 0.72 | 24.2% |
| Gold (GLD) | 13.4% | 15.9% | 0.70 | 4.8% |
| Commodities (DBC) | 9.5% | 17.7% | 0.45 | 7.1% |
| Real Estate (VNQ) | 5.6% | 20.7% | 0.24 | 14.8% |
| Bitcoin (BTCUSD) | 68.1% | 66.9% | 1.07 | 7.5% |
Smart multi-asset allocation framework can stack odds in your favor. Learn How
Returns Analyses
Earnings Returns History
Expand for More| Forward Returns | |||
|---|---|---|---|
| Earnings Date | 1D Returns | 5D Returns | 21D Returns |
| 5/7/2026 | 86.0% | ||
| 2/26/2026 | -7.2% | -6.7% | -22.0% |
| 11/6/2025 | 6.9% | -6.7% | -8.4% |
| 7/31/2025 | -18.1% | -22.0% | -30.8% |
| 5/8/2025 | -15.8% | -13.9% | 18.4% |
| 2/20/2025 | 13.5% | -3.6% | -23.4% |
| 11/7/2024 | 75.8% | 60.9% | 79.3% |
| 8/8/2024 | 13.3% | 4.5% | -12.9% |
| ... | |||
| SUMMARY STATS | |||
| # Positive | 17 | 12 | 9 |
| # Negative | 5 | 9 | 12 |
| Median Positive | 11.6% | 7.0% | 18.4% |
| Median Negative | -16.6% | -6.7% | -17.8% |
| Max Positive | 86.0% | 63.8% | 124.9% |
| Max Negative | -24.9% | -37.8% | -48.6% |
SEC Filings
Expand for More| Report Date | Filing Date | Filing |
|---|---|---|
| 03/31/2026 | 05/07/2026 | 10-Q |
| 12/31/2025 | 02/26/2026 | 10-K |
| 09/30/2025 | 11/06/2025 | 10-Q |
| 06/30/2025 | 07/31/2025 | 10-Q |
| 03/31/2025 | 05/09/2025 | 10-Q |
| 12/31/2024 | 02/24/2025 | 10-K |
| 09/30/2024 | 11/07/2024 | 10-Q |
| 06/30/2024 | 08/09/2024 | 10-Q |
| 03/31/2024 | 05/08/2024 | 10-Q |
| 12/31/2023 | 03/04/2024 | 10-K |
| 09/30/2023 | 11/03/2023 | 10-Q |
| 06/30/2023 | 08/11/2023 | 10-Q |
| 03/31/2023 | 05/12/2023 | 10-Q |
| 12/31/2022 | 02/24/2023 | 10-K |
| 09/30/2022 | 11/10/2022 | 10-Q |
| 06/30/2022 | 08/12/2022 | 10-Q |
Recent Forward Guidance [BETA]
Latest: Q1 2026 Earnings Reported 5/7/2026
| Forward Guidance | Guidance Change | ||||||
|---|---|---|---|---|---|---|---|
| Metric | Low | Mid | High | % Chg | % Delta | Change | Prior |
| 2026 Revenue Growth | 40.0% | 14.3% | 5.0% | Raised | Guidance: 35.0% for 2026 | ||
| 2026 Revenue | 51.00 Mil | Higher New | |||||
Prior: Q4 2025 Earnings Reported 2/26/2026
| Forward Guidance | Guidance Change | ||||||
|---|---|---|---|---|---|---|---|
| Metric | Low | Mid | High | % Chg | % Delta | Change | Prior |
| 2026 Revenue Growth | 35.0% | -22.2% | -10.0% | Lowered | Guidance: 45.0% for 2025 | ||
Insider Activity
Expand for More| # | Owner | Title | Holding | Action | Filing Date | Price | Shares | Transacted Value | Value of Held Shares | Form |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Mishra, Ashok | EVP and COO | Direct | Sell | 12082025 | 60.16 | 23,654 | 1,423,025 | 3,609,600 | Form |
| 2 | Forlenza, Louise C | Direct | Sell | 11122025 | 65.00 | 8,278 | 538,070 | 256,295 | Form | |
| 3 | Mishra, Ashok | EVP and COO | Direct | Sell | 11122025 | 61.39 | 200,000 | 12,278,939 | 3,683,682 | Form |
| 4 | Mishra, Ashok | EVP and COO | Direct | Sell | 11122025 | 62.52 | 28,094 | 1,756,437 | 3,751,200 | Form |
External Quote Links
| Y Finance | Barrons |
| TradingView | Morningstar |
| SeekingAlpha | ValueLine |
| Motley Fool | Robinhood |
| CNBC | Etrade |
| MarketWatch | Unusual Whales |
| YCharts | Perplexity Finance |
| FinViz |
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