The Economic Bomb: A Strategic Financial Warfare Tactic

This dataset provides evidence supporting the hypothesis that institutional shorting, ETF outflows, whale wallet movements, and media sentiment drive Bitcoin’s volatility and price manipulation. Central to this dataset is the Decker Sentiment-Short Interest Model (DSSIM)—an original equation developed by Nicolin Decker to quantify the relationship between market sentiment and institutional short interest. By combining sentiment scores from Natural Language Processing (NLP) and short positioning data, DSSIM offers a flexible framework for analyzing volatility in Bitcoin and other assets.

The dataset spans January 2021 to December 2024, capturing daily market activity and key price events. Each file aligns with DSSIM’s variables, enabling replication and further analysis of the findings in the doctoral-level thesis The Economic Bomb: A Strategic Financial Warfare Tactic.

Key Components:
BTC_Price_Data.csv: Daily BTC/USD closing prices from Binance, Coinbase, and Bitstamp, serving as the baseline for volatility and return calculations.

ETF_Holdings_Over_Time_Thesis.csv: Daily BTC holdings of ETFs (Grayscale, BlackRock, and Fidelity), illustrating cumulative outflows and their liquidity impact.

ETF_Outflows_Price_Impact_Data.csv: Correlates ETF outflows with BTC volatility, highlighting timing and magnitude.

Institutional_Shorting_Data.csv: Daily BTC short positions from Binance, BitMEX, Bybit, and OKX, serving as input for DSSIM’s short interest variable.

Whale_Wallet_Movements.csv: Tracks large BTC wallet movements, revealing sell-offs preceding price crashes and influencing DSSIM’s residual noise component.

Market_Liquidity_Data.csv: Daily BTC trading volume, order book depth, and liquidity ratios, validating DSSIM’s predictive capabilities.

Media_Sentiment_Scores.csv: Daily sentiment from Twitter, Reddit, Google News, and YouTube, forming DSSIM’s sentiment variable.

Monte_Carlo_Simulation_Results.csv: Simulates 1,000 BTC price paths to assess potential volatility under market stress.

VAR_Model_Data.csv: Analyzes ETF outflows’ delayed impact on BTC returns using vector autoregression.

Volatility_Clustering_Data.csv: Tracks daily BTC returns and 30-day rolling volatility, confirming persistent volatility after institutional actions.

GARCH_Model_Data.csv: Models BTC volatility using GARCH, validating volatility clustering during market shocks.

The dataset includes adjustments for major market events, such as the May 2021 Flash Crash, June 2022 Liquidation Crisis, and March 2023 Banking Crisis, ensuring realistic volatility patterns aligned with DSSIM’s modeling of sentiment shifts and institutional shorting.

Researchers can use DSSIM’s structure and data to explore similar dynamics in other cryptocurrencies, equities, commodities, and forex markets, advancing financial analysis and predictive modeling.

Access the full dataset:
https://drive.google.com/drive/folders/1pnwqBTMF_QSJoC5QcNAPSQpVtOST2n8c?usp=drive_link