Cryptocurrencies in the Balance Sheet: Insights from (Micro)Strategy -- Bitcoin Interactions

TL;DR

The paper addresses how corporate BTC treasury strategies reshape BTC–equity interdependencies by assembling a dataset of 39 BTC-holding firms and applying Pearson correlations, a BTC price beta, and Transfer Entropy (TE) to quantify information flow. Using a mixed-method approach that includes static and rolling TE, as well as a single-factor BTC beta framework, the study finds that BTC generally acts as the dominant information driver with an average BTC beta of $\eta$̂ = 0.62 and $12$ firms exhibiting $\hat{\beta} > 1$, while feedback from equities to BTC is rare and concentrated around major events. Rolling TE analyses for MSTR reveal persistent BTC-to-MSTR information transmission ($\text{TE}_{BTC\to MSTR}$ averaging $0.0241$ bits) with significant episodic bursts, whereas $\text{TE}_{MSTR\to BTC}$ is smaller and less frequent ($0.0191$ bits on average). These results imply that dynamic hedging and adaptive risk management are essential as digital assets become more integrated into corporate finance and market dynamics, highlighting asymmetries in information transmission and potential implications for portfolio diversification and policy oversight.

Abstract

This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries.

Figures (7)

• Figure 1: Red vertical lines mark key events in the BTC market timeline. Figure \ref{['fig:first_dates']}: distribution of first acquisition dates across companies in the dataset, highlighting the initial waves of institutional adoption. Figure \ref{['fig:last_dates']}: distribution of last acquisition dates, capturing more recent activity and assessing whether interest is currently accelerating. Figure \ref{['fig:all_dates']}: overall distribution of BTC acquisition dates for all recorded transactions in the dataset. There is a clear acceleration in acquisition activity starting in early $2024$, peaking in $2025$. Earliest first date: Dec $31$, $2017$ (Hut 8 Mining Corp); latest last date: April $3$, $2025$ (MARA Holdings, Inc. and Riot Platforms, Inc.). Data are updated as of April $11$, $2025$.
• Figure 2: Frequency of BTC purchased by firm.
• Figure 3: The box plot displays the distribution of BTC holdings among the $39$ companies in our dataset. The lower and upper edges of the box correspond to the first ($25\%$) and third ($75\%$) quartiles, respectively; the line within the box marks the median ($50\%$). Whiskers extend to the minimum and maximum values within the distribution, and small diamond markers beyond the whisker indicate outlier firms with exceptionally large BTC positions.
• Figure 4: BTC price (in orange), MSTR price (in blue), and cumulative BTC holdings (in green).
• Figure 5: Single factor model of MSTR daily returns on BTC (Apr $1$, $2023$ to Apr $1$, $2025$). The estimated BTC $\beta = 1.37$ indicates that a $1\%$ move in BTC is associated with an average $1.37\%$ move in MSTR; $\alpha = 0.0020$; $R^{2}=0.44$. The red band depicts the $99\%$ confidence interval around the fitted line.