Predications from financial data through analytics

While no one denies financial markets are very complex systems, determining the best composition of a prosperous portfolio doesn’t have to be.

For many years, simple trading strategies purely based on analytics generated from market data performed very well. Out of these approaches, the two most famous ones being:

  1. Momentum: Dictates to buy those stocks that have had high returns over a predefined past period, and to sell those that have had poor returns over the same timescale. The underlying assumption is that the best bet about tomorrows market movements is that they will continue in the same direction.
  2. Mean Reversion: Opposing momentum, this strategy bets on the assumption that markets will even out over time and prices tend to move to their corresponding average. Stocks with prices below their historical averages therefore represent an opportunity.

By enhancing the underlying financial analytics with sophisticated technology and artificial intelligence, professionals are now able to build far more complex and intelligent algorithms, helping to make financial predictions more accurate and efficient.

This evolution is part of a larger trend of our whole society using Big Data to include macroeconomic announcements, company reports and social networks to assess market sentiment.

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