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boom-bust-cycles

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chirindaopensource / does_awareness_of_self_reinforcing_market_dynamics_improve_LLMs_financial_market_forecasters

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End-to-End Python implementation of "Reflexivity as Prompt" (Park, 2026). Evaluates LLMs as financial forecasters during boom-bust cycles using George Soros's Reflexivity Theory as a zero-shot prompt scaffold. Implements P/E-ratio-invariant normalization, buy-and-hold calibration gates, and a structured diagnostic reproducibility audit taxonomy.

python jupyter-notebook econometrics sharpe-ratio quantitative-finance reproducibility zero-shot-learning time-series-forecasting sp500 financial-forecasting large-language-models prompt-engineering behavioral-finance rolling-window llm-evaluation boom-bust-cycles market-cycles reflexivity-theory empirical-replication soros-reflexivity
  • Updated Jun 7, 2026
  • Jupyter Notebook

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