The Application of Machine Learning to Financial Market Prediction

The search for non-traditional models to predict the prices of financial markets is still a highly researched topic for an obvious reason. Unlike traditional methods, machine learning techniques allow to process a vast amount of information of different nature in order to extract non-random patterns hidden od price behavior in input data. Input data can be historical data on prices and volumes (high frequency included), other types of data as fundamental indicators or data analytics, all of them can serve for collect trading signals. A wide range of ML techniques will be analyzed to find the strength and weaknesses of each method. They can be used individually, but above all in combination to improve their predictive forecasting performance. Finding an optimal structure of predictive apparatus, an optimal set of their hyperparameters is a worthy research task to be solved.


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