Ethereum Price Prediction: The Value Investor's Guide

ETHEREUM PRICE PREDICTION

The Value Investor’s Guide

Percy Venegas

ECONOMY MONITOR

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Copyright © 2017 by Percy Venegas

Published by ECONOMY MONITOR

Interior design by Pronoun

ISBN: 9781537858449

TABLE OF CONTENTS

Ethereum Price Prediction: What matters in the long term? : Introduction

Problem statement

Exploratory analysis

Model

Expanding the Ethereum price model: Ethereum wallet online

Ethereum wallet download

Ethereum mining

Ethereum mining rig

Ethereum classic

Ethereum Dapp

Ethereum investing

Ethereum ICO funders selloffs

Ethereum whales

Ethereum trading: Ethereum CFD

Ethereum CFD

Ethereum news

Ethereum going down

Ethereum coin

What is a ethereum coin?

Bitcoin’s scaling deadlock

Ethereum tokens

Soft factors

Ethereum risk

Implementation of Price Analytics at Scale

Conclusions

Resources

References

Appendix: Initial Coin Offering (ICOs) in Blockchain Trustless crypto-markets

Appendix to the paper: a brief history of The DAO

About the author

Disclaimer

More by Percy Venegas

ETHEREUM PRICE PREDICTION: WHAT MATTERS IN THE LONG TERM?

INTRODUCTION

AS OF JUNE 17TH 2017 Ethereum had the largest market cap ($35,199,137,095) of all cryptocurrencies, second only to Bitcoin ($43,280,307,102). But unlike BTC, ETH not only works as a currency (e.g. a medium of exchange or store of value), the Ethereum blockchain is inherently programmatic: you can run smart contracts that have asset-like properties and execute all sorts operations, including the full automation of a fund, a supply chain, or an organization.

ETH Price. Source: Etherscan.io

The largest financial and industrial companies in the world are investing in Ethereum-based technology, including J.P Morgan, Toyota, Merck, and many others. However, a series of lingering concerns about risk exposures and stability of the whole system remain: the core of the project is open source, incomplete smart contracts are prone to abuse, and the market is still unregulated, which limits any control that could be exerted over speculation-induced volatility. A crash or other unexpected events could hamper adoption, or at the very least destroy value: as it happened in mid-2016 when an attack on The DAO, the largest crowdsale to that point in history, left investors with millions in losses ($55M from The DAO fund, and half-billion dollars in market value erased from Ethereum).

However unpredictable the price of financial instruments (including cryptocurrencies) might be, what lies at the heart of the issue is a matter of demand-side economics: the coders who contribute to the public project, the ones who develop decentralised applications on the Ethereum blockchain, and those who peruse knowledge of the development community for private projects, all are suppliers of technology –but consumers of Ethereum related-content. Likewise, the miners who validate transactions are suppliers to the ecosystem (they create Ether, the fuel of the Ethereum blockchain), but consume information about mining pools, mining calculators, and the like. Finally, end-users signal demand by using wallets, exchanges, and retailers that accept ETH as a payment method. Investors looking for Ethereum price predictions in 2017, 2018, 2020 or 2030 will therefore benefit from approaching the subject with a demand economics mindset.

PROBLEM STATEMENT

Can the price direction of Ethereum be predicted for periods of days, using non-financial measurements of demand, to facilitate hedging against emerging financial risks?

EXPLORATORY ANALYSIS

We analyze daily usage data for 69 services and groups in the Ethereum ecosystem, for the period from December 2015 to May 2017. From the time series data on our selected measure of demand we can make interesting observations, including the initial role of faucets to seed the economy, how activity in The DAO community and development groups related to The DAO was correlated with price drops, and the apparent effect that the addition of mining capacity has on pumping up the price.

Estimated demand of Ethereum related services

MODEL

We use machine learning to search in a space of millions of possible solutions. For a start, we prefer equations with a combination of fitness and interpretability; for instance, when choosing

ETH_price = 0.00053560429554645*sma(wallet_1, 12)

over

ETH_price = 3.91902029107539 + 0.000130115228409987*rankings_1

for some added complexity in the form/size of the equation, the error is reduced sharply. Furthermore, the chosen model provides deeper insight into the mechanics of the system: price can be better approximated using a Simple Moving Average (SMA) and demand for Ethereum wallets, rather than usage of cryptocurrency rankings portals.

Model:ETH_price = a*sma(wallet_1, 12)

This is