{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Tech Stories Tech Brief By HackerNoon","title":"I Built a Causal AI Model to Find What Actually Causes Stock Drawdowns","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/75340592\"></iframe>","width":"100%","height":180,"duration":985,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/i-built-a-causal-ai-model-to-find-what-actually-causes-stock-drawdowns.\n             Do valuations cause crashes? Use Causal AI & EODHD data to prove how profitability and beta drive downside risk during market shocks. Move beyond correlation. \n            Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.\n            You can also check exclusive content about #causal-ai, #stock-drawdowns, #causal-inference, #stock-drawdown-modelling, #inverse-probability-weighting, #causal-ai-for-markets, #counterfactual-risk-analysis, #maximum-drawdown-modeling,  and more.\n            \n            \n            This story was written by: @nikhiladithyan. Learn more about this writer by checking @nikhiladithyan's about page,\n            and for more stories, please visit hackernoon.com.\n            \n                \n                \n                The EODHD causal AI framework analyzes how valuation, volatility, and profitability affect a stock’s downside. The data comes from ten years of S&P 500 stocks, which is more than enough to see how company characteristics shape real risk, not just statistical noise.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/IuqXIpaNNuezY7jNfIDnL5gqB1iL_SEndwUUzLGdljY/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxNDI5LzE2ODM1/ODM0NjQtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}