Look Under The Hood: FXL Has 10% Upside
Written by Emily J. Thompson, Senior Investment Analyst
Updated: Jul 18 2024
0mins
Should l Buy ESTC?
Source: NASDAQ.COM
- Analyst Target Price Comparison: ETF Channel compared trading prices of holdings in the First Trust Technology AlphaDEX Fund ETF (FXL) against average analyst 12-month forward target prices, revealing a 9.70% upside potential for FXL.
- Analyzing Analyst Targets: The article questions whether analysts' targets are justified or overly optimistic, emphasizing the importance of further investor research to understand the implications of high price targets relative to current stock prices.
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Analyst Views on ESTC
Wall Street analysts forecast ESTC stock price to rise
26 Analyst Rating
16 Buy
10 Hold
0 Sell
Moderate Buy
Current: 60.380
Low
75.00
Averages
101.14
High
130.00
Current: 60.380
Low
75.00
Averages
101.14
High
130.00
About ESTC
Elastic NV is a Netherlands-based data analytics company. The Company’s platform, which is available as both a hosted, managed service across public clouds as well as self-managed software, allows its customers to find insights from large amounts of data and take action. The Company offers three search-powered solutions - Enterprise Search, Observability, and Security - that are built into the platform, which is built on the Elastic Stack, a set of software products that ingest data from any source, in any format, and perform search, analysis, and visualization of that data. The Company helps organizations, their employees, and their customers find what they need, while keeping mission-critical applications running smoothly, and protecting against cyber threats. The Company generates revenue primarily from sales of subscriptions to its platform.
About the author
Emily J. Thompson
Emily J. Thompson, a Chartered Financial Analyst (CFA) with 12 years in investment research, graduated with honors from the Wharton School. Specializing in industrial and technology stocks, she provides in-depth analysis for Intellectia’s earnings and market brief reports.
Elastic to Release Q3 Fiscal 2026 Financial Results
- Earnings Release Schedule: Elastic will announce its financial results for the third quarter of fiscal 2026 on February 26, 2026, after the U.S. market closes, providing insights into the company's latest financial performance.
- Conference Call Timing: The company will host a conference call at 2:00 p.m. PT (5:00 p.m. ET) on the same day to review its financial results and business outlook, aiming to bolster investor confidence in the company's future growth.
- Webcast Availability: The conference call will be accessible via a live webcast on Elastic's investor relations website, allowing real-time information access, with a replay available for two months to ensure that investors who cannot attend live can still obtain critical insights.
- Company Background: Elastic, known for its expertise in search technology and artificial intelligence, has its Search AI Platform utilized by over 50% of Fortune 500 companies, highlighting its significant industry impact and market recognition.
See More
Elastic Launches Inference Service via Cloud Connect for Self-Managed Deployments
- Service Launch: Elastic's Inference Service (EIS) is now available via Cloud Connect for self-managed Elasticsearch deployments, allowing users to access on-demand capabilities without managing GPU infrastructure, significantly lowering the technical barrier for adoption.
- Model Access: Users can now immediately access multilingual and multimodal embedding models provided by Jina.ai, enhancing the quality and relevance of search results, which in turn improves customer experience and business decision-making capabilities.
- Infrastructure Simplification: EIS enables self-managed clusters to securely offload embedding generation and search inference to Elastic Cloud's managed GPU fleet while maintaining their existing architecture and data, optimizing resource allocation and operational efficiency.
- Rapid Deployment Advantage: With a single setup, self-managed customers can quickly access a range of cloud services from automated diagnostics to fast AI inference, further driving the application of data-driven decision-making in enterprises and enhancing market competitiveness.
See More
Elastic Launches New Jina Reranker Models for Enhanced Search
- Low-Latency Inference: Elastic's newly launched Jina Reranker models on the Elastic Inference Service deliver low-latency, multilingual processing, significantly enhancing search quality by helping users quickly find the most relevant matches across multi-query results, thereby improving user experience.
- Production-Friendly Architecture: Jina Reranker v3 is optimized for low-latency inference, capable of reranking up to 64 documents in a single inference call, which reduces inference usage and is well-suited for RAG and agentic workflows requiring defined top-k results, thus enhancing overall efficiency.
- Unbounded Candidate Support: Jina Reranker v2 scores documents independently, allowing it to handle arbitrarily large candidate sets, enabling developers to incrementally rerank results without relying on strict top-k limits, thereby increasing flexibility and accuracy.
- Expanded Model Catalog: The release of these models extends Elastic's existing catalog on EIS, integrating open-source multilingual and multimodal embeddings, further strengthening Elastic's competitive position in the search and AI sectors, with more models expected to be added to meet market demands.
See More
Elastic Launches Cloud Connect Inference Service for Self-Managed Clusters
- Inference Service Launch: Elastic announced the availability of Elastic Inference Service (EIS) via Cloud Connect for self-managed Elasticsearch deployments, enabling organizations to access cloud-hosted inference capabilities on-demand while avoiding GPU infrastructure management, thereby enhancing operational efficiency.
- Seamless Integration: Launched in Elasticsearch 9.3, EIS allows users to leverage GPU-based embedding and reranking models, including leading models from Jina.ai, enabling rapid implementation of powerful semantic search capabilities that improve the quality of search results.
- Data Security: Self-managed clusters can retain their existing architecture and keep data on-premises while securely offloading embedding generation and search inference to Elastic Cloud's managed GPU fleet, ensuring data privacy and security.
- Operational Simplification: According to Steve Kearns, Elastic's General Manager of Search, EIS simplifies the complexity of GPU infrastructure, making it easier for self-managed customers to adopt semantic search, offering a range of cloud services from automated diagnostics to fast AI inference, thus enhancing customer experience.
See More
Elastic (NYSE: ESTC) Launches Agent Builder to Simplify AI Agent Development
- Comprehensive Agent Builder: Elastic's newly launched Agent Builder provides developers with a complete set of capabilities to quickly build secure, reliable, context-driven AI agents, significantly enhancing enterprise data search and analysis capabilities.
- Seamless Microsoft Integration: Agent Builder's native MCP and A2A protocol support enables seamless deployments within Microsoft Foundry and Microsoft Agent Framework, enhancing users' ability to build context-rich AI agents.
- Workflow Functionality Extension: Elastic also introduced Elastic Workflows (tech preview), allowing agents built with Agent Builder to reliably take action across systems, closing the reliability gap in AI automation.
- Model Agnosticism and Compatibility: Agents developed with Agent Builder are model-agnostic and compatible with managed model-as-a-service providers, including cloud hyperscalers, ensuring broad applicability and flexibility.
See More
Elastic (NYSE:ESTC) Launches Agent Builder for Enterprise AI Agent Development
- AI Agent Development: Elastic's newly launched Agent Builder provides developers with a comprehensive set of capabilities to quickly build secure, reliable, context-driven AI agents, significantly enhancing the efficiency and accuracy of enterprise data processing.
- Context-Driven Intelligence: Built on Elasticsearch, Agent Builder effectively extracts enterprise context from unstructured data sources, enabling teams to reason more accurately and deliver better outcomes, thereby improving the quality of business decisions.
- Workflow Functionality: Elastic also introduced Elastic Workflows (tech preview), allowing agents built with Agent Builder to reliably execute actions across systems, addressing the reliability gap in AI automation and facilitating the transition from pilot projects to real-world applications.
- Compatibility and Availability: Agent Builder is now available in Elastic Cloud Serverless and included in the Enterprise Tier of Elastic Cloud Hosted, ensuring existing customers can seamlessly access this new functionality to enhance their AI agent development capabilities.
See More
Elastic to Release Q3 Fiscal 2026 Financial Results
- Earnings Release Schedule: Elastic will announce its financial results for the third quarter of fiscal 2026 on February 26, 2026, after the U.S. market closes, providing insights into the company's latest financial performance.
- Conference Call Timing: The company will host a conference call at 2:00 p.m. PT (5:00 p.m. ET) on the same day to review its financial results and business outlook, aiming to bolster investor confidence in the company's future growth.
- Webcast Availability: The conference call will be accessible via a live webcast on Elastic's investor relations website, allowing real-time information access, with a replay available for two months to ensure that investors who cannot attend live can still obtain critical insights.
- Company Background: Elastic, known for its expertise in search technology and artificial intelligence, has its Search AI Platform utilized by over 50% of Fortune 500 companies, highlighting its significant industry impact and market recognition.
See More
Elastic Launches Inference Service via Cloud Connect for Self-Managed Deployments
- Service Launch: Elastic's Inference Service (EIS) is now available via Cloud Connect for self-managed Elasticsearch deployments, allowing users to access on-demand capabilities without managing GPU infrastructure, significantly lowering the technical barrier for adoption.
- Model Access: Users can now immediately access multilingual and multimodal embedding models provided by Jina.ai, enhancing the quality and relevance of search results, which in turn improves customer experience and business decision-making capabilities.
- Infrastructure Simplification: EIS enables self-managed clusters to securely offload embedding generation and search inference to Elastic Cloud's managed GPU fleet while maintaining their existing architecture and data, optimizing resource allocation and operational efficiency.
- Rapid Deployment Advantage: With a single setup, self-managed customers can quickly access a range of cloud services from automated diagnostics to fast AI inference, further driving the application of data-driven decision-making in enterprises and enhancing market competitiveness.
See More
Elastic Launches New Jina Reranker Models for Enhanced Search
- Low-Latency Inference: Elastic's newly launched Jina Reranker models on the Elastic Inference Service deliver low-latency, multilingual processing, significantly enhancing search quality by helping users quickly find the most relevant matches across multi-query results, thereby improving user experience.
- Production-Friendly Architecture: Jina Reranker v3 is optimized for low-latency inference, capable of reranking up to 64 documents in a single inference call, which reduces inference usage and is well-suited for RAG and agentic workflows requiring defined top-k results, thus enhancing overall efficiency.
- Unbounded Candidate Support: Jina Reranker v2 scores documents independently, allowing it to handle arbitrarily large candidate sets, enabling developers to incrementally rerank results without relying on strict top-k limits, thereby increasing flexibility and accuracy.
- Expanded Model Catalog: The release of these models extends Elastic's existing catalog on EIS, integrating open-source multilingual and multimodal embeddings, further strengthening Elastic's competitive position in the search and AI sectors, with more models expected to be added to meet market demands.
See More
Elastic Launches Cloud Connect Inference Service for Self-Managed Clusters
- Inference Service Launch: Elastic announced the availability of Elastic Inference Service (EIS) via Cloud Connect for self-managed Elasticsearch deployments, enabling organizations to access cloud-hosted inference capabilities on-demand while avoiding GPU infrastructure management, thereby enhancing operational efficiency.
- Seamless Integration: Launched in Elasticsearch 9.3, EIS allows users to leverage GPU-based embedding and reranking models, including leading models from Jina.ai, enabling rapid implementation of powerful semantic search capabilities that improve the quality of search results.
- Data Security: Self-managed clusters can retain their existing architecture and keep data on-premises while securely offloading embedding generation and search inference to Elastic Cloud's managed GPU fleet, ensuring data privacy and security.
- Operational Simplification: According to Steve Kearns, Elastic's General Manager of Search, EIS simplifies the complexity of GPU infrastructure, making it easier for self-managed customers to adopt semantic search, offering a range of cloud services from automated diagnostics to fast AI inference, thus enhancing customer experience.
See More
Elastic (NYSE: ESTC) Launches Agent Builder to Simplify AI Agent Development
- Comprehensive Agent Builder: Elastic's newly launched Agent Builder provides developers with a complete set of capabilities to quickly build secure, reliable, context-driven AI agents, significantly enhancing enterprise data search and analysis capabilities.
- Seamless Microsoft Integration: Agent Builder's native MCP and A2A protocol support enables seamless deployments within Microsoft Foundry and Microsoft Agent Framework, enhancing users' ability to build context-rich AI agents.
- Workflow Functionality Extension: Elastic also introduced Elastic Workflows (tech preview), allowing agents built with Agent Builder to reliably take action across systems, closing the reliability gap in AI automation.
- Model Agnosticism and Compatibility: Agents developed with Agent Builder are model-agnostic and compatible with managed model-as-a-service providers, including cloud hyperscalers, ensuring broad applicability and flexibility.
See More
Elastic (NYSE:ESTC) Launches Agent Builder for Enterprise AI Agent Development
- AI Agent Development: Elastic's newly launched Agent Builder provides developers with a comprehensive set of capabilities to quickly build secure, reliable, context-driven AI agents, significantly enhancing the efficiency and accuracy of enterprise data processing.
- Context-Driven Intelligence: Built on Elasticsearch, Agent Builder effectively extracts enterprise context from unstructured data sources, enabling teams to reason more accurately and deliver better outcomes, thereby improving the quality of business decisions.
- Workflow Functionality: Elastic also introduced Elastic Workflows (tech preview), allowing agents built with Agent Builder to reliably execute actions across systems, addressing the reliability gap in AI automation and facilitating the transition from pilot projects to real-world applications.
- Compatibility and Availability: Agent Builder is now available in Elastic Cloud Serverless and included in the Enterprise Tier of Elastic Cloud Hosted, ensuring existing customers can seamlessly access this new functionality to enhance their AI agent development capabilities.
See More
