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    <title>the quant notebook</title>
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    <description>Quantitative finance meets machine learning. Projects, articles, and experiments by Ibrahim Fırat Soysal.</description>
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      <title><![CDATA[Polymarket Earnings Prediction: Beating the Crowd with ML + Alternative Data]]></title>
      <link>https://thequantnotebook.com/articles/polymarket-earnings-prediction-beating-the-crowd-with-ml-alternative-data</link>
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      <description><![CDATA[An end-to-end ML pipeline that predicts S&P 500 and Russell 2000 earnings beats, finds mispriced markets on Polymarket, and sizes positions using Diversified Kelly criterion with GPT-4o, FinBERT, insider trading, and options sentiment as risk filters. 81.5% win rate, +278% return on backtest.]]></description>
      <pubDate>Mon, 30 Mar 2026 00:00:00 GMT</pubDate>
      <category>Quantitative Finance</category>
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      <title><![CDATA[Beyond the Bell Curve: Why Markets Don't Follow Dice Laws (and the Math Behind It)]]></title>
      <link>https://thequantnotebook.com/articles/beyond-the-bell-curve-why-markets-dont-follow-dice-laws</link>
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      <description><![CDATA[Dice follow the Bell Curve; markets do not. The standard Normal model dangerously assumes constant volatility and mathematically erases the probability of extreme crashes. To capture reality, true quants combine GARCH to track how volatility clusters and the Student-t distribution to measure the devastating "fat tails" of the crowd.]]></description>
      <pubDate>Thu, 19 Feb 2026 00:00:00 GMT</pubDate>
      <category>Statistics</category>
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