3. Methodology

3.1 Research Design

This study employs a mixed-methods approach combining:

• Conceptual Analysis: Formulation of the CDE theoretical hypothesis based on market observations and theoretical dialectics.
• Literature Review: Synthesis of available academic research, industry reports (CoinMarketCap, Glassnode, Blockchain Intelligence Group), and expert opinions.
• Empirical Analysis: Quantitative examination of historical data on BTC dominance, altcoin proliferation, and capital flows across the period 2013-2025.
• Case Studies: Detailed examination of selected "alt seasons" (2017-2018, 2020-2021) and their impact on Bitcoin dominance.

3.2 Variables and Operationalization

3.3 Data Sources and Period

Our primary data encompasses the period from 2013 to 2025, capturing the evolution from Bitcoin's early dominance through multiple market cycles including the 2017-2018 ICO boom, the 2020-2021 DeFi/NFT surge, the 2022 bear market, and the 2024-2025 institutional adoption phase via ETFs.

3.4 Methodological Limitations

3.4.1 Incomplete On-Chain Data: Quantifying the Invisible Market

A significant methodological challenge involves capital flows that remain opaque due to privacy-enhancing features and off-chain transactions. Recent blockchain analytics research (2025) demonstrates that comprehensive data collection has substantially improved, with platforms like Dune Analytics, Flipside Crypto, and Arkham Intelligence now providing SQL-based querying and real-time analytics across Bitcoin, Ethereum, EVM, and non-EVM blockchains. However, critical blind spots persist:

• Privacy Coins: As of 2025, the privacy coin sector (led by Monero and Zcash) represents approximately $14.3 billion in market capitalization—a relatively small fraction (~0.7%) of Bitcoin's $2 trillion market cap. While Zcash surged to $388 (an 8-year high) with a $6.2 billion market cap in late 2024, privacy coin transaction volumes remain deliberately opaque by design, employing ring signatures (Monero) and zero-knowledge proofs (Zcash) that obscure sender, receiver, and transaction amounts. This creates an estimated 1-2% data gap in total crypto market visibility.
• Decentralized Exchange (DEX) Volume: DEX trading has exploded, with Q2 2025 data showing $876.3 billion in spot trading volume (up 25% quarter-over-quarter) and average weekly volume of $18.6 billion. Uniswap commands 35.9% market share ($111.8 billion monthly), while PancakeSwap achieved record $310 billion annual volume in 2024 (179% year-over-year growth). DEX perpetual futures reached $898 billion in Q2 2025, dominated by Hyperliquid (73% market share). While DEX analytics have dramatically improved, unique wallet counts (9.7 million mid-2025, up from 6.8 million) represent only a fraction of actual trading activity due to multi-wallet strategies and automated trading bots.
• Off-Chain Transactions: British Accounting Review research (2025) examining Bitcoin market efficiency from 2014-2022 confirms that off-chain factors (liquidity and investor attention) interact significantly with on-chain metrics (active wallets, transaction fees, transaction volume). Layer-2 solutions (Lightning Network, Liquid Network) and centralized exchange internal transfers remain partially untracked, though their impact on Bitcoin dominance calculations is mitigated by the fact that BTC.D is computed from market capitalization rather than transaction volume.

Mitigation Strategy: We triangulate data from multiple sources (CoinMarketCap, CoinGecko, Glassnode, DefiLlama) and focus our analysis on market capitalization-based dominance metrics, which aggregate price × supply and are less vulnerable to off-chain transaction opacity than volume-based metrics. The growing accessibility of on-chain analytics tools across multiple blockchain ecosystems reduces but does not eliminate this limitation.

3.4.2 Endogeneity and Causality: Disentangling Correlation from Cause

The fundamental challenge of distinguishing causation from correlation pervades cryptocurrency market analysis. The core question—Does altcoin proliferation cause Bitcoin dominance decline, or does Bitcoin dominance decline create conditions that attract altcoin investment?—remains difficult to resolve definitively. Recent econometric research provides methodological frameworks for addressing this challenge:

• Granger Causality Testing: Multiple studies employ Granger causality tests to examine temporal precedence in cryptocurrency markets. Research analyzing Bitcoin's relationship with macroeconomic variables, gold prices, and M2 money supply demonstrates that Granger causality can identify whether changes in one variable systematically precede changes in another. Studies examining Bitcoin-altcoin dynamics through network analysis reveal complex interdependencies, with Bitcoin typically exhibiting high "in-degree" (receiving causal influences) and "out-degree" (exerting causal influences) in cryptocurrency networks.
• Reflexivity and Endogeneity Indices: Mark et al. (2022) developed a "reflexivity index" to quantify activity generated endogenously within cryptocurrency markets, measuring the extent to which market movements are self-reinforcing versus driven by external factors. This approach acknowledges that cryptocurrency markets exhibit significant endogenous dynamics—price movements trigger narrative shifts that attract capital flows, creating feedback loops that complicate causal inference.
• Instrumental Variables Approach: Addressing endogeneity requires identifying exogenous instrumental variables correlated with altcoin supply growth but not directly affecting Bitcoin dominance through other channels. Potential instruments include: (1) regulatory announcements affecting token issuance costs, (2) blockchain technology breakthroughs enabling new token standards (ERC-20 in 2017, BRC-20 in 2023), and (3) venture capital funding cycles in blockchain startups. However, valid instruments remain challenging to identify given the interconnected nature of crypto ecosystem developments.
• Macro-Micro Factor Integration: Recent research (2025) demonstrates that integrating diverse data sources—technical indicators, on-chain metrics, sentiment indicators, traditional market indices, and macroeconomic indicators—substantially improves cryptocurrency forecasting accuracy. On-chain metrics prove paramount for both short-term and long-term predictions, while traditional market indices and macroeconomic indicators gain relevance for longer-term forecasts. This suggests that Bitcoin dominance dynamics result from complex interactions between crypto-specific factors and broader financial market conditions, further complicating causal identification.

Our Approach: Rather than claiming definitive causal direction, we acknowledge bidirectional feedback mechanisms. We present correlation analysis (Pearson r = -0.92 between altcoin supply and BTC dominance) as descriptive evidence of association, while carefully avoiding causal language. We triangulate multiple analytical approaches—time-series analysis of market cycles, year-over-year dominance changes, institutional capital allocation patterns—to build a circumstantial case that altcoin proliferation represents a structural force rather than merely a consequence of Bitcoin weakness. Future research employing panel data methods, quasi-experimental designs around regulatory shocks, or machine learning approaches to causal inference could strengthen causal claims beyond our current correlational framework.

3.4.3 Limited Historical Data

The cryptocurrency market's ~16-year existence constrains long-term trend analysis and limits our ability to distinguish secular trends from super-cycles. Traditional financial markets benefit from century-long datasets enabling identification of multi-decade patterns; cryptocurrency markets provide barely more than one full economic cycle (2009-2025), spanning only two Federal Reserve rate-hiking cycles and three major bear markets.