Understanding the past can sometimes help us better predict what might happen in the future. But in complex adaptive systems – such as financial markets – simply understanding the past is usually not good enough.
Regression-based models for predicting stock market behaviour are flawed and no longer deliver the best results.
In the current challenging macroeconomic environment, banks must turn to the latest technologies in order to produce more accurate simulations of future market behaviour.
Why historical data belongs in the past
Ultimately, in the real world, every trade has an effect on the market which changes the behaviour of other traders. Historical data cannot account for this behaviour because it is simply a static recreation of the past.
As a result, it is often unclear if your data has a blind spot in relation to a period of high volatility until it is too late.
Occurrences such as flash crashes and unprecedented market patterns are rare, but that is little consolation if you miss one because your model is unable to predict events that have not happened before, or worse, if your model reacts in a way that contributes to the disorder.
What’s more, historical data is usually expensive to acquire and can be difficult to ...
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