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Tickeron's FLMs and NLG Technology: Revolutionizing Scalable Financial Content Creation

Tickeron's FLMs and NLG Technology: Revolutionizing Scalable Financial Content Creation

By: 

NEW YORK - July 14, 2025 - PRLog -- Tickeron has developed a powerful combination of Financial Learning Models (FLMs) and Natural Language Generation (NLG) technology to automate and scale financial content creation, enabling the delivery of highly relevant market insights to a global audience in real time.

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:

  • Sharp volume or price changes
  • Trend reversals
  • Unusual volatility or momentum
  • Earnings beats/misses and dividend updates


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:

  • ~10,000 single-ticker articles daily
  • ~50 million two-ticker comparisons
  • ~250 million three-ticker comparisons


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—each with a unique SEO-optimized URL—ready to be indexed by Google and surfaced to users.

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/app/prelogin/?homePageMode=trader&am...

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.

Disclaimers and Limitations

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