Multilingual AI

40% of the information on the web is in languages other than English. EMAlpha’s multilingual AI allows investors to track information in any language in the world which not only helps mitigate risks but also allows them to stay on top of every major development.

  • Thematic and ESG data
  • Geopolitical risk data
  • Trend and sentiment analysis
  • Customer sentiment

Emerging markets data

Information discovery is a big challenge in emerging markets due to the local language barrier. EMAlpha provides EM risk datasets with various underlying themes, thus providing extensive coverage of emerging market companies.

  • Country-level risk data
  • Sentiment data

ESG data

With coverage of more than 55 countries and 15000+ companies, EMAlpha’s ESG data covers all the major frameworks such as SASB, TCFD, UNSDG etc. EMAlpha also provides sovereign ESG data, private companies’ ESG data, as well as AI-driven green bond scores.

  • Framework data
  • Thematic data
  • Green bond data

Using multilingual NLP technology, EMAlpha extracts relevant data from over 50+ developed and emerging markets including US, UK, Japan, China, India, Brazil, and South Korea.

With climate change gaining priority, how are investors’ portfolios aligned with net-zero and climate goals?

How do company’s disclosures such as its sustainability report fare when assessed under a global framework? What does an ESG report convey?

With all the various ESG frameworks and jargon that are being used in the sustainability space, many companies are finding it confusing to report ardently on ESG.

Perform EM sentiment analysis for multiple companies. Check real time news flow to stay abreast of the latest happenings.

As the popularity of green bond has increased exponentially, it has become necessary to leverage technology to spot greenwashing and ensure that capital is invested in the aligned impact projects for the right reasons.


Who are we?

The EMAlpha team consists of members with diverse backgrounds and experience covering Emerging Markets, Portfolio Management, Trading, and Machine Learning.

The team consists of domain experts in emerging markets with significant experience in fundamental and quantitative analysis. A number of our team members have PhDs in finance and hard sciences and have spent considerable time applying machine learning methods to the markets. One team member has been a recipient of the Thomson Reuters’ Starmine Analyst Award for ‘Best Earnings Estimates’ and ‘Best Stock Picker’. Another member has published highly cited papers in Theoretical Physics. We believe that a strong team with diverse backgrounds helps us craft a multidimensional approach towards solving the data and investment problems in front of us.




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