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The article "https://www.mdpi.com/1911-8074/18/2/66" by Murray A. Rudd and Dennis Porter (2025) develops a rigorous economic model to forecast Bitcoin prices based on its fixed, inelastic supply (capped at 21 million coins) and evolving demand dynamics, particularly institutional adoption and long-term holding behavior. Summary of Key Points: Bitcoin Supply Characteristics:** Bitcoin’s supply is fixed and perfectly inelastic with a 21 million coin cap. Supply expansion slows via periodic halvings (block reward reductions) roughly every 4 years; the latest occurred in April 2024. Liquid supply is significantly less than total issuance due to lost, long-term held (HODL) coins, and coins held as strategic reserves. Demand Side Factors:** Demand is modeled using a Constant Elasticity of Substitution (CES) demand function with parameters calibrated to market data. Institutional, sovereign, and corporate accumulation (strategic reserves) reduces liquid supply, intensifying scarcity. Credit-driven demand expansion and increasing adoption shift demand curves outward over time. Model Framework:** The model sets April 20, 2024 (fourth halving) as the baseline with: Issued Bitcoins: ~19.7 million. Liquid supply estimated at ~11.2 million. Market price at $ 64,858 USD. Projections extend to the seventh halving (~April 2036), a 12-year horizon. Model parameters can be adjusted for demand growth (demand shift multiplier), daily Bitcoin withdrawals into reserves, elasticity, adoption curve timing, and lost coins. Uses a discrete daily supply-demand equilibrium approach implemented in Analytica software. Price Forecasts and Scenarios:** | Scenario | Demand Multiplier (D) | Daily Withdrawals to Reserve | Forecast Price Range by 2036 (USD) | Notes | |----------|-----------------------|------------------------------|------------------------------------|-------| | Baseline | 1x | 0 to 2000 | Approx. $62,000 to $ 106,000 | Near current price, stable | | Moderate | 10x - 20x | 0 to 2000 | $639,000 to $ 2.2 million | Moderate adoption, growing reserves | | Bull | 30x - 40x | 0 to 2000 | $1.9 million to $ 4.4 million | Aggressive institutional adoption and high withdrawals | | Conservative (Dec 2024 Recalibration) | D=20, elasticity ~ -2.1 | 0 to 4000 | $98,577 to $102,808 (as of Dec 2024) | Prices ~$1M by mid-2028 with withdrawals | | Bull (Fast Adoption) | D=30, elasticity ~ -1.8 | 0 to 4000 | $1M by early 2027, up to$5M by early 2031 | High institutional demand | Price Trajectory Insights:** Price appreciation is sensitive to both increased demand and reductions in liquid supply. Modest withdrawals to long-term reserves (e.g., thousands per day) could drive Bitcoin to $ 1 million+ within 3-6 years. High withdrawal rates accelerate supply shocks, causing hyperbolic price growth and increased volatility. Demand growth has a more substantial short-run impact than modest changes in liquid supply. Power law models predicting decreasing volatility may be incorrect; price volatility may increase as supply dwindles. Comparisons with Other Models:** Model forecasts align well with MicroStrategy’s financial models (multi-million dollar price by 2036-2045). ARK Invest’s forecast ( $3.8M by 2030) suggests even higher demand multipliers or more constrained supply scenarios. Limitations & Future Work:** Assumptions about lost coins, HODL behavior, and market elasticities remain uncertain. The framework excludes complex network effects, Layer-2 impacts, and macro shocks explicitly. Future modelling could include stochastic simulations, multi-commodity interactions, or survey-derived demand curves. Bitcoin Price Prediction Schedule Summary: | Date/Period | Event/Predictor | Forecast Price Range (USD) | |--------------------------|------------------------------------|-------------------------------------| | April 2024 (Baseline) | Price at 4th halving (start) | $ 64,858 | | Mid-December 2024 | Market price update; scenario recalibration | ~ $98,577 - $ 102,808 | | Mid-2027 to Early 2028 | Bull scenario (high demand, withdrawals) | $1 million+ | | Mid-2028 to 2030 | Moderate to high adoption & withdrawals | $1M to$2.5 million | | Early 2031 | Bull scenario peak prices | Up to $ 5 million | | April 2036 (7th halving) | Long-term equilibrium prices | $62k (low adoption, no withdrawals) to multiple millions (high demand & withdrawals) | Summary of What Drives Price Increases: Institutional & sovereign investors withdrawing Bitcoin from the liquid market (strategic reserves). Growth in adoption shifting demand curves outward significantly (multipliers 10x to 40x). Rising scarcity as the supply schedule approaches the hard cap. The interaction of inelastic supply with evolving demand creates potential for steep price appreciation and volatility. For Reference and Further Reading You can read the full article and see detailed figures, charts, and data at the official MDPI link to the article: https://www.mdpi.com/1911-8074/18/2/66 In brief, this framework presents a flexible, realistic economic foundation for Bitcoin price forecasting showing significant long-term upside potential driven by supply constraints and increasing institutional demand, with predicted price targets ranging from ~$100K today to several million USD by 2036 depending on market dynamics.
Some brokers are offering white glove services - although these come in handy when volume is particularly high in consideration of liquidity (say, above 10-15% of the total liquidity). What is usually done, is selling through several DEXes at once, to benefit from several liquidity pockets. Some platforms allow for smart routing, splitting the order across multiple DEXes and working them in-line with volume (think, Coinbase Prime for instance). It is however not always sufficient. In such cases, some counterparties may offer to source liquidity OTC and execute in blocks until the order is filled. Again, it is the activity of certain desks, such as CES (the “white glove” service from Coinbase).
When you say his economy lost a net 178k jobs during his presidency, you omit the fact that by March 2020, Trump's admin had seen a net [gain](https://data.bls.gov/timeseries/CES3000000001) of over 350,000 manufacturing jobs. The losses were very clearly due to covid. Why do you find it necessary to present misleading "facts" like that?
[Those are rookie numbers](https://www.youtube.com/watch?v=CES1OIkFhg4)