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Follow on Google News | ![]() Tickeron's FLMs and NLG Technology: Revolutionizing Scalable Financial Content CreationBy: Tickeron How It Works Step 1: Aggregating Data from Multiple Trusted Sources Tickeron collects and consolidates end-of-day financial data from reliable providers such as Morningstar, FRED, SEC, and other institutional sources. This includes data across stocks, ETFs, cryptocurrencies, mutual funds, and forex markets. Step 2: Intelligent Data Processing with FLMs Tickeron's proprietary FLMs analyze this data across multiple dimensions—price movements, fundamentals, earnings, dividends, insider trading, and macroeconomic indicators. Each ticker is independently assessed to generate actionable insights and identify market-moving events. Step 3: Signal Generation and Event Detection Based on the analysis, FLMs generate daily buy/sell signals and detect significant events such as:
Step 4: Automated Article Creation via NLG Using its advanced NLG engine, Tickeron converts raw insights into natural-language articles and headlines for each ticker. The system also generates dynamic comparative articles between pairs and triplets of tickers:
Step 5: Overnight Filtering and Publishing From this massive dataset, Tickeron intelligently selects the top ~1 million most relevant articles and prepares them for publication— Step 6: Personalized News Feed and Content Discovery Users can explore this content through a customizable news feed, applying filters such as asset class, signal type, event category, or market sector—empowering marketers, publishers, and traders to extract high-value, newsworthy stories instantly. View: https://tickeron.com/ Conclusion With Tickeron's FLMs and NLG platform, identifying meaningful financial news is instant. A journalist or content creator, even when assisted by ChatGPT, can quickly build on this foundation to discover and release timely, well-written articles. The automated publication of over 1 million articles daily, each indexed by Google, enables unparalleled content reach, scale, and discoverability. End
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