Stocks 2.0: metrics, blogs and reviews

The Web 2.0 movement should be providing a whole new level of accessible validated aggregated information regarding publicly traded companies.

1) Standardized non-financial metrics
There should be standardized reporting (as for financial statements) and a template and widget code for socially responsible investment screening data in the usual areas: Business Practices and Governance, Environment, Human Rights, Diversity, Labor, Community, Animal Testing and Tobacco, Alcohol, Weapons and Gambling.

Currently, this research is developed and implemented in proprietary ways by Citizens Funds, Calvert, the Social Venture Technology Group and others who could work in standards bodies and open source ways.

2) Ecosystem information
Corporate employee blogs should be on Yahoo Finance, Google Finance, etc. Customer, vendor and supplier reviews and D&B, Better Business Bureau and Chamber of Commerce data should be aggregated. More usable data regarding insider trades, litigation and negative PR would also be helpful. Companies can make themselves more attractive by providing this information as well as access to some permissioned-level of internal prediction markets and other innovative investment-worthy information.

3) Publicly available supervisor reviews
As the world is Yelp-ified with ubiquitous reviews, businesses can be competitive in complying with the expectation for reviews. Public CEO, executive and general management reviews may become commonplace and expected. Academic review sites have surmounted the attendant legal issues and become de rigueur in providing professor reviews with sites like RateMyProfessors and CourseReviews; why not for corporate bosses too.

4) NLP and other AI tech tools
Mutual funds and hedge funds are paying up for a new set of AI tools using NLP, natural language processing, to rapidly assess early signs of potential stock movement from rumblings in the blogosphere and news articles provided by companies such as CollectiveIntellect and Novamente. At some point all information posted to the Internet including podcasts and videos will need to be searched and aggregated. Making some versions of these bubble-up NLP-based summarization tools publicly-available could improve their value and acceptance.