According to the World Economic Forum, by 2025 10% of global gross domestic product (GDP) will be stored on blockchains, a type of decentralised database and distributed shared ledger. Smart contracts are automated computable contracts that are executed in blockchains, with the benefit of removing intermediaries and reducing costs. Their uses in finance include: in cross-border payments, to capture obligations, minimize operational errors and expedite transfers; for property and casualty claims in insurance, to automate claims processing through third-party data sources and codification of business rules; for deposits and lending in syndicated loans, to facilitate real-time loan funding and automated servicing activities without intermediaries; for deposits and lending in trade finance, to automate the creation and management of credit facilities, ultimately eliminating correspondent banks; for contingent convertible bonds in capital raising, to alert regulators when loan absorption needs to be activated, minimizing need for point-in-time stress tests; for compliance in investment management, to execute reporting and facilitate the automated creation of periodic filings; for proxy voting in investment management, to automate end-to-end confirmation by the validation of votes, increasing transparency; for asset rehypothecation in market provisioning, to enable the real-time reporting of asset history and the enforcement of regulatory constraints, including facilitating clearing and settlement to eliminate need for intermediaries and reduce settlement time; and for equity post-trade in market provisioning, to simultaneously transfer equity and cash in real time, reducing the likelihood of errors impacting settlement.
The policy implications introduced by decentralization require that economists and lawyers understand this technological shift, and more importantly, the risks related to tangible (e.g consensus selection as a security choice) and intangible (e.g contract incompleteness/code errors) factors. We demonstrate a decision making method where utility is measured by “levels of trust” using artifacts from fields finance applied to a portfolio of institutional smart contract companies. Expected utility is measured by mapping a demand vector field (the attention level), and funding by plotting a scalar field (the investment level). The associated risk exposure is implicit in the consensus mechanism tradeoffs, according to the progression of firms represented in the system of coordinates. The goal is to provide a device for portfolio analysis and construction. The data comes from a panel of 200 million internet users, and investment databases such as CrunchBase (https://data.crunchbase.com). The result is a comprehensive and scalable view of decentralised portfolios, inspired by the methods of behavioural finance.