Monitoring Redistributions of Asset Stock
By: Johann Colloredo-Mansfeld
Tags: Ethereum, Cryptocurrency Investment, Bitcoin, Cryptocurrency, Bolt Research

Monitoring Redistributions of Asset Stock

Large scale movements in the circulating supply of a network’s token bear implications for price. The logic underpinning the dynamic is described here.

Within many investment theses, velocity sinks appear to perform an important and value-accretive function. Evidence of a velocity sink would amount to an increasing trend among a network’ s participants to place capital in cold storage wallets and move capital away from exchange hot wallets. This amounts to the redistribution of existing token stock, and is a dynamic that Boltzmann tracks using on-chain analysis.

Just a Sinkhole

Within token-enabled blockchain networks, validators, such as miners, perform an activity that is remunerated with a token. Validators thus introduce fresh supply into a network, causing marginal changes to the total circulating asset base with each additional block and pushing out the supply curve. The rate at which validators release fresh supply into the network is a function of inter-temporal arbitrage and bears implications for future price. While understanding supply flow dynamics is critical, it only captures a portion of a network’s supply-side dynamics.

Supply-side dynamics comprise both the introduction of an asset (release of remunerated assets into the market) and the redistributions of the stock of an asset among market participants. The stock of an asset describes the present total circulating supply of a token less the amount held in reserve by validators. Almost invariably, the changes in the distribution of the total stock of an asset will bear greater implications for price than will the introduction of new supply. The reason for this is quite simple: the existing stock of an asset is typically orders of magnitude greater than any amount being introduced.

To describe these dynamics, Boltzmann offers three analytical frameworks that are not only scalable across the entire history of transactions for each of the networks we support, but are also generalizable across networks. This last point is particularly important as we seek to establish reference frames that allow for cross-asset comparisons that may drive relative valuations.

Boltzmann can examine the redistributions of the stock of an asset by constructing three network identities: Network Sources, Network Pipes, and Network Sinks. As with our other metrics, we construct these identities using the characteristics of the wallets transacting within each block. We do not attempt to label assets or associate them with any real world identity. Instead, we infer from the transactional behavior of a wallet what their role within the network most likely is.

With this classification schema in mind, we provide three metrics that monitor the redistribution of the existing stock of assets among the three aforementioned network constituents.

Source-Sink Flow measures the total amount of asset flowing from wallets classified as network sources to those classified as network sinks. Classification of a wallet as a source or a sink depends on the net flow across it in a variable time window: significant positive flow (net inflow) defines a sink wallet, while significant negative flow (net outflow) defines a source wallet.

Sink-Source Flow measures the total amount of asset flowing from wallets classified as network sinks to those classified as network sources.

Pipe-Pipe Flow measures the total amount of asset flowing through network intermediaries. These intermediaries are wallets that have approximately as much inflow as outflow and therefore do not serve as a source or sink on the network.

By using these archetypal classifications, we can monitor the redistribution of assets among a network’s participants and understand how it changes over time.