Prediction of stock market is acclaimed by many as one of the most challenging areas for machine learning. The existence of quant industry that makes use of artificial intelligence based computational methods to predict the market provide enough evidence contrary to Efficient Market Hypothesis and Random Walk theory. Recent research on the financial market has focused on machine learning based approaches where instead of specifying the rules, learning algorithms are employed to make use of existing data. Financial markets provide one of the most organized data sets where data from each tick is recorded. Both generative and discriminative class of machine learning techniques have been explored in search of improved accuracy. Even with the abundance of structured financial data, complex, chaotic and nonlinear nature of the market that can ride on public emotions keeps the scope for probabilistic generative methods. This paper discusses the usability of machine learning techniques from both the classes: generative and discriminative along with the characteristics of data that enables them.

Discriminative, Financial market, Generative, Machine Learning, Sentiment Analysis, Supervised Learning

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