Research Paper finds Bitcoin spreads ‘like a virus’
Bitcoin price and adoption figures follow mathematical rules found in nature, claims a new paper from the international Social Science and Research Network (SSRN).
A new research paper suggests that the growth and price of bitcoin is likely to proceed according to a relatively straightforward mathematical model – similar to the growth curves of Facebook and other networks.
In the paper, Timothy Peterson of Cane Island Alternative Advisors summarises:
We derive the relationships between price, number of users, and time, and show that the resulting market capitalizations likely follow a Gompertz sigmoid growth function. This function, historically used to describe the growth of biological organisms like bacteria, tumors, and viruses, likely has some application to network economics.
Peterson has previously explored the relationship between network size and crypto price in his paper, ‘Metcalfe’s Law as a Model for Bitcoin’s Value’. He starts out by noting that price developments are generally highly correlated with changes in fundamental on-chain factors such as the total number of wallets, active addresses, unique addresses, and transaction activity. Bitcoin, however, is a young and ‘noisy’ market, and fundamental measures of value should be overlaid with speculative activity, which carries the price far in excess of its baseline.
Plotting bitcoin’s growth since its inception, the author states: that the base price for bitcoin can be derived from the time since its inception. Peterson’s chart shows that, for substantial stretches of bitcoin’s history, the data has neatly fitted his equation: ‘bitcoin’s lognormal price P with respect to time t is a near-perfect horizontal parabolic arc.’ The ‘clean price’ can be shown to fit the price given by the parabolic arc.
In between these periods, the ‘manipulated price’ – time of intense speculative activity or bubbles – lifts the price off the parabolic curve. Bitcoin’s current price – just under $4,000 at the time of writing – matches closely with the parabolic line, indicating that BTC is ‘fair value’.
A very similar relationship can be observed for Facebook, by plotting share price against time. Charting monthly active users against log scale square root time also shows a convincing correlation. This precedent in the tech sector indicates the validity of using the network effect as a tool for long-term price analysis.
Peterson does not use the model to predict future price rises, though his clear implication is that this must ultimately be a by-product of an expanding user base.
‘Over time price tends toward value. The model we have presented serves as a backdrop against which potential information can be evaluated. It does not predict that bitcoin’s price will soar or crash. Rather, it suggests that the probability of those extreme those events is very small because ultimately number of users drives price.’