Price parities ensure fair pricing of data assets. Fair pricing is arbitrage-free. Activist, capital structure arbitrage, asset-backed credit hedging, and other strategies secure this freedom. Thereby, minorities retain a say, capital structure remains optimal, and the assets exchange hands if the debt is delinquent.

Entrepreneurs value productive relevance of data assets. Shareholders value privacy of data assets. Creditors value economic efficiency of data assets. These different approaches to data asset pricing must coincide. Whenever you are interested in specific data assets, Quantifiable manages, trades, and funds such assets on your behalf.

How do data entrepreneurs establish connection between fundraising and sales? On behalf of our clients, we identify and remove causes of vulnerability from vulnerable investments. On behalf of our investors, we provide use cases in data equity investments. Our data asset pricing capability enables us to connect the two.

Capital Valuation Adjustment (KVA) addresses regulatory capital requirements based on the exposure profile of a derivative position. It ensures that the derivative price reflects the cost of holding sufficient capital to cover potential risks. KVA incorporates the cost of maintaining capital throughout the position’s lifetime and reflects the time value of money by discounting future capital costs.

Price movements leak proprietary information. If the leaks pertaining to a client can be organized, then a security issued by the client becomes vulnerable. Whenever we identify such a vulnerability we have remedies on offer. The fair value of data is the price that our client pays to eliminate causes of vulnerability from vulnerable investments.

As a rule, a good company responds only to key challenges. We solve your challenges by applying due diligence, asset pricing, and privacy preservation to common data resource. If your company needs stable investor relations, we enhance your AGMs, EGMs, and class meetings with effective fundraising, agenda-setting, and voting strategies.

We trade corporate data. We also restructure corporate data assets. To this end, we use formalisms to extract and curate sensitive open-source information, and diagnose its security-related characteristics. Our current applications are derived from the Arrow information, Grossman-Stiglitz, and Newcomb’s paradoxes. 

Our foundation stewards and nurtures the study of formalisms. We source formal paradoxes, fallacies, and puzzles from across formal languages. We endorse, curate, and fund their formal statements, as long as their decidability fuses together entirely different aspects of formal reasoning or rigorously introduces entirely new formalisms.

How would you diagnose inefficiency from a data record? How would you distinguish between trading and governing inefficiencies from that record? How would you profile failed state of data commons from that record? How would you infer if the record discloses existence of a secret? Can you characterize the secret? Let us let you know!

Concentrated data markets warrant privacy regulation. Externalities warrant incentivizing traded data assets. Data commons should be provided as commons typically are. However, data is an anti-commons. Why wouldn’t we provide it ourselves if all the reasons to do so are already in place?