Big Data transforming investment landscape when coupled with key elements

“There was widespread agreement that Big Data and Machine Learning is transforming the investment landscape across different trading frequencies, with more than 80% of participants expecting a reduction in relevance of traditional data sources…”

– JP Morgan

Big data is transforming the investment industry

Our post earlier this year noting that 2017 is the year of alternate market data contained insights regarding big data’s role in this sea change for data in the investment industry. Information edge or the ability to capitalize on data asymmetry is a crucial component of generating alpha in the stock market. With increasing frequency this information edge is being provided to the investment industry through big data and powerful new methods of transforming data into insights.

Only with key elements does Big Data add value

According to an article Matt Turner in “This is the future of investing, and you probably can’t afford it“, a recent report from the TABB Group indicates “52% of investors felt big data was already rendering traditional data sources (like financial statements and economic releases) ineffective.” At RelateTheNews we transform big data into insights for the capital markets. Quite often without this transformation big data alone is not specifically useful. There are 3 key components in transforming big data into a powerful component of the investment lifecycle:

  1. Repeatable – The analysis process must be effectively repeatable even when proprietary. Artificial Intelligence (AI) provides one mechanism for this to be achieved.
  2. Insightful – The resulting signals from transformational analysis must provide greater insight than is possible by looking at the raw data or available via traditional sources of data.
  3. Actionable – The signals generated from big data analysis must be effective in models for portfolio selection, execution/trading and risk management. The ability to drive action from the signals from within models throughout the investment lifecycle is crucial to driving alpha from big data.