Paper: Bubbles vs. Baselines: Token Valuation and Institutional Capital in PoS Networks under EIP-1559
Authors: Mikhail Perepelitsa
Date: 2026-06-05
Estimated Reading Time: 22 minutes

This paper develops an open economy macroeconomic model for Proof of Stake networks operating under EIP-1559 style fee burning. The framework models interactions between a Kelly optimizing institutional investor and a utility driven retail consumer to explain token valuation, staking dynamics, and network security. Two behavioral regimes are examined: one where consumers only accumulate tokens and another where consumers both buy and sell tokens for consumption. The first regime generates persistent buy side pressure that produces speculative price appreciation and excess institutional returns, while the second produces a steady state equilibrium where token prices scale with network adoption and institutional excess returns disappear. Simulations indicate that external financial market shocks primarily affect token prices rather than long run network inflation, which remains relatively stable. The model also argues that consumer purchasing behavior prevents institutional ownership from becoming monopolistic because declining prices increase retail purchasing power and stabilize the staked supply. Overall, the paper concludes that institutional excess returns arise from persistent retail demand rather than from the staking protocol itself.

Core insights

The paper examines tokenomics by treating the staking token as both a productive financial asset and a transactional utility asset. The distinction between accumulation driven demand and utility driven demand becomes the central determinant of long term token valuation. Consumer behavior therefore becomes more influential than staking rewards alone in determining sustainable price formation. From a supply perspective, the paper combines Proof of Stake issuance with EIP-1559 fee burning to create an evolving token supply. Instead of concluding that staking rewards inevitably create concentration, the model argues that endogenous trading behavior offsets this effect. This raises an important question: under what market conditions would real consumer behavior depart from the representative agent assumptions enough to alter these equilibrium results? On the demand side, the model differentiates speculative accumulation from transactional usage. The speculative regime generates continuous buy side pressure that allows institutional investors to capture returns through systematic portfolio management, whereas transactional demand produces a valuation baseline tied to network adoption. Network adoption therefore replaces speculative momentum as the primary pricing anchor in the second framework. The reward structure is interpreted as a consequence of interactions between staking yield, institutional capital allocation, and retail demand rather than as an isolated protocol incentive. A notable implication is that staking rewards alone do not generate persistent institutional outperformance. Instead, institutional gains depend on sustained inflows from consumers who continually purchase tokens for network utility. Would empirical blockchain data support this separation between protocol rewards and demand driven returns across multiple Proof of Stake networks? The simulations further distinguish between price stability and protocol stability. Traditional financial shocks increase token price volatility through institutional portfolio rebalancing, yet inflation remains relatively stable and network security is maintained through counter cyclical retail purchases. This suggests that economic volatility and consensus security need not move together. The paper concludes that institutional wealth creation is not an intrinsic property of Proof of Stake consensus. Instead, excess returns emerge only when consumer demand continuously transfers value into the ecosystem without offsetting sales. An assumption of the model is that the representative consumer and representative institutional investor adequately capture aggregate market behavior, and the conclusions should be interpreted within that modeling framework rather than as direct empirical evidence.