Stock News API and Bulls Say Bears Say API bring real-time market news and analyst sentiment to MOUM, Meritz Securities' next-generation AI investment platform
DETROIT and SEOUL, South Korea, Sept. 8, 2026 /PRNewswire/ -- Benzinga, a leading provider of real-time financial news and market intelligence, today announced that Meritz Securities has added Benzinga's Stock News API and Bulls Say Bears Say API to its investing platform, bringing real-time market news and analyst sentiment insights directly into the trading experience for South Korean investors.
Meritz, one of South Korea's leading securities firms, continues to expand the market data available across its platform as more Korean investors look to trade U.S. equities and other global markets. As a result, the relationship gives Meritz users access to Benzinga's real-time news coverage alongside concise, balanced analyst sentiment, all within a single experience.
The integration is part of Meritz Securities' broader strategy to build MOUM as a next-generation AI investment platform by bringing together leading global market data, financial content and AI capabilities into a unified investment experience.
Benzinga's Stock News API delivers real-time headlines and breaking news across U.S. equities, covering earnings, analyst actions, mergers and acquisitions, regulatory developments, and other market-moving events as they happen. Similarly, the Bulls Say Bears Say API distills analyst research into concise bullish and bearish summaries for a given stock, helping investors quickly understand both sides of the investment case without wading through dense reports.
Through this integration, Meritz is bringing both feeds directly into MOUM, giving users a faster path from headline to context. Instead of piecing together news and analyst opinion from multiple sources, Meritz investors can view real-time coverage and sentiment summaries side by side, right where they are already making decisions.
By combining real-time market intelligence with AI-powered analysis and trading capabilities, Meritz aims to create a more seamless path from information discovery and analysis to investment decision-making within a single platform.
The relationship also marks one of Benzinga's first direct data integrations with a major South Korean brokerage, reflecting the growing demand among Asian investors for the same real-time market intelligence long relied on by U.S. and European trading platforms. For Benzinga, it is a meaningful step in expanding its data footprint across Asia's fast-growing retail investing market.
"Meritz is one of the most respected names in Korean finance, and bringing our real-time news and sentiment data to their platform reflects the growing appetite among Korean investors for the same market intelligence used by trading platforms globally," said Jae Hur, Head of Asia at Benzinga. "Stock News and Bulls Say Bears Say give Meritz users a faster, clearer read on what's moving the market and why."
"Our vision for MOUM goes beyond simply adding individual investment tools. We are building a next-generation Agentic AI investment platform that connects market data, financial content, AI-powered intelligence and trading into a single, seamless investment experience," said Jangwook Lee, Executive Vice President and Head of Inno Biz Division, overseeing Meritz Securities' platform business. "Our collaboration with Benzinga is an important part of that strategy, and we will continue to expand our global ecosystem by working with leading technology, data and financial partners around the world."
As Korean investors increasingly seek direct access to U.S. markets, real-time context has become as important as raw price data. The addition of Stock News and Bulls Say Bears Say reflects that broader shift, giving Meritz a deeper layer of market intelligence to offer its growing base of investors.
More broadly, the collaboration reflects Meritz Securities' strategy to connect Korean investors with world-class global investment infrastructure, content and technology through MOUM, while continuing to expand partnerships across the global financial ecosystem.
About Benzinga
Benzinga is a leading financial media and data technology company that empowers investors with high-quality, real-time market intelligence. Through its news platform, APIs, and data products, Benzinga provides traders, financial institutions, and fintech platforms with the insights they need to make smarter investment decisions. From breaking news and analyst ratings to corporate events and alternative datasets, Benzinga's tools help market participants stay ahead of the information that drives price movement.
To learn more, visit www.benzinga.com/apis/.
About Meritz Securities
Meritz Securities is one of South Korea's leading financial institutions, providing a broad range of investment and financial services. Through MOUM, its next-generation AI investment platform, Meritz Securities is building a differentiated digital investing experience that brings together trading, real-time market intelligence, financial content and AI-powered investment capabilities.
Meritz Securities is also expanding its global ecosystem through strategic collaboration with leading financial, technology, data and content partners, with the goal of providing Korean investors with more seamless access to global markets and world-class investment intelligence.
To learn more, visit https://link.moum.me/, https://home.imeritz.com/.
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Meritz Securities Partners with Benzinga to Bring Real-Time U.S. Market Intelligence to Korean Investors
Enabling AI to identify information gaps and choose the right reasoning language for more reliable enterprise Agentic AI
SINGAPORE, Sept. 8, 2026 /PRNewswire/ -- Appier (TSE: 4180), an AI-native company delivering Agentic AI as a Service (AaaS), continues to advance AI innovation and research while expanding the frontiers of marketing and advertising technology. As Agentic AI becomes increasingly embedded in core enterprise operations, Appier's AI Research team has published two papers examining how large language models (LLMs) can recognize when retrieved information does not support a valid answer and how selecting the appropriate reasoning language can better serve users across linguistic backgrounds — setting a more rigorous benchmark for the trustworthiness and global deployment of enterprise AI.
Both capabilities are increasingly important in real-world business scenarios. Consider an e-commerce service agent: if a shopper asks about a product not covered by an existing return policy, an AI that applies rules for a similar product could provide misleading information and create disputes. Likewise, a game studio expanding overseas may rely on AI to understand local players, but linguistic nuances can shape market preferences and risks. If the AI does not reason in the local language, it may overlook insights that a native speaker would recognize. To support enterprise decision-making effectively, Agentic AI must be able to manage uncertainty and select the reasoning approach and language best suited to each task.
Recognizing Information Gaps: Teaching AI to Answer Honestly When Data Falls Short
To complete tasks autonomously, Agentic AI typically retrieves information from enterprise knowledge bases, documents, or external sources before reasoning and responding. Retrieval-Augmented Generation (RAG) is a common architecture for this process. However, when retrieval fails to provide sufficient information, the model's ability to recognize that gap directly affects the reliability of any subsequent decision.
In "None of the Above, Less of the Right: Parallel Patterns between Humans and LLMs on Multi-Choice Questions Answering," Appier's AI Research team used "None of the Above" (NA) options to simulate scenarios in which no valid answer was available. The team tested 28 leading LLMs of varying sizes and found that model accuracy fell by 30% to 50% when "none of the above" was the correct response. The findings show that even when models possess relevant knowledge, they tend to select suboptimal or incorrect options rather than proactively flagging insufficient information.
This capacity to "know what you don't know" matters most in tasks like business ethics, where models must assess several plausible options holistically, a more complex task than solving problems with directly verifiable answers, such as mathematics.
Appier's AI Research team applied two training methods—Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO)—to teach models to recognize "none of the above" scenarios. SFT trains models using correct examples, while DPO exposes them to both correct and incorrect responses so they can learn to distinguish between them. DPO improved model accuracy in identifying questions with no correct answer by nearly 30 percentage points, showing that targeted training can strengthen this capability.
However, "none of the above" is not suitable for every question. It is most effective when answers are clearly defined and the options are mutually independent, suggesting that an AI's ability to withhold an answer should be trained for specific tasks. In enterprise knowledge scenarios, this means improving retrieval accuracy while also introducing a checkpoint to confirm whether sufficient information is available before the AI acts. If the information is insufficient, the system can search again or escalate to a human.
Choosing the Right Reasoning Language for Each Task
Beyond managing uncertainty, Agentic AI also needs to select the right reasoning language for each task. Appier's research paper, "Language Matters: How Do Multilingual Input and Reasoning Paths Affect Large Reasoning Models?" found that the language used by a large reasoning model (LRM) can directly affect its logical reasoning, safety judgments, and cultural understanding. Even when prompted in another language, models often default to high-resource languages such as English for reasoning. For some models, the reasoning language differed from the response language in over 90% of cases.
Using a "text prefilling" technique—setting an opening phrase that prompts the model to reason in a specified language—Appier's AI Research team examined how language choice affected task performance. High-resource languages such as English generally produced stronger results in mathematics and knowledge-based tasks. For tasks requiring cultural understanding, however, reasoning in the local language captured local context more effectively. Safety testing also showed that local-language reasoning was more effective at identifying harmful or illegal queries.
In other words, the optimal reasoning language should be chosen dynamically based on the nature of the task. This research opens the door to "reasoning-language routing": beyond selecting the right model, tools, and data, Agentic AI could, in the future, dynamically select the most suitable reasoning language based on task type, market, and cultural context, while continuing to respond in the user's preferred language.
Beyond Answer Accuracy: Recognizing Limits and Choosing the Right Approach
"These two papers redefine the standard for evaluating AI. As AI moves from answering questions to making autonomous decisions, measuring whether a model produces the correct answer is no longer enough. We must also assess whether it can recognize insufficient information, adjust its actions accordingly, and select the reasoning approach best suited to each task," said Chih Han Yu, CEO and Co-founder of Appier. "These capabilities will help Agentic AI evolve from simply executing instructions into a reliable decision-making system capable of navigating real-world complexity. Through sustained foundational research, Appier aims to turn these critical questions into measurable and improvable AI capabilities, enabling enterprises across markets and languages to adopt Agentic AI with greater confidence."
Looking ahead, Appier's AI Research team will continue advancing research into large language models and Agentic AI while exploring applications across the company's three product lines—Ad Cloud, Personalization Cloud, and Data Cloud. The goal is to enable AI not only to act autonomously but also to make more reliable judgments based on the available information, task requirements, and market context—helping enterprises turn Agentic AI into scalable, tangible business value.
About Appier
Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers business decision-making with cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of "Making AI Easy by making software intelligent," Appier endeavors to help businesses turn AI into ROI with its Ad Cloud, Personalization Cloud, and Data Cloud solutions. Now Appier has 17 offices across APAC, the US and EMEA, and is listed on the Tokyo Stock Exchange. Visit www.appier.com for more company information, and visit ir.appier.com/en/ for more IR information.
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Beyond Guesswork: Appier Research Teaches AI to Recognize Its Limits and Choose the Right Reasoning Approach
