Docent lecture: Blockchain and machine learning in real estate finance
Welcome to Bertram Steininger's Docent lecture upon admission as Docent at KTH in the subject Real Estate and Finance.
The presentation is open to the public. No registration required, but places are limited. Please note that the docent lecture will be held in English.
Time: Mon 2021-04-26 14.00
Participating: Bertram Steininger
Blockchain and machine learning in real estate finance
Over the past years, an increasing amount of legislation has allowed the digital form of asset securitization – the security token. Just as with traditional forms of fractional ownership (e.g. stocks), the security token is equipped with a value, rights, and obligations. These tokens can be time- and cost-efficiently exchanged through smart contracts which are self-executing computer codes whose execution is based on Distributive Ledger Technologies (Blockchain) and can consequently be executed without the involvement of third parties (banks, clearing houses, and stock exchanges). Properties and land are identified as the most suitable assets for digital tokenization since they suffer from a long trading period and many involved parties. If this securitization concept is based on a solid legal and economic framework, it may push forward the digital revolution to a decentralized financial system.
The length and complexity of firms’ disclosures make it increasingly difficult and time-consuming for investors to read and interpret all the information which are necessary for wise investment decisions. For example, the risk sections of U.S. firms’ 10-K had increased from roughly 41,000 to 93,000 words between 2006 and 2019. Certainly, investors are not able to handle the large number of corporate disclosures provided by dozens or hundreds of firms in a portfolio. To cope with the flood of information, unsupervised machine learning algorithms can be used to identify the “topics” discussed in the text corpora. By using machine learning, investors can gather more information and react more quickly.