A new area of research has focused on whether machine learning through deep learning strategies can be applied to finance and market prediction. Consider hedge funds or investment banks that engages in arbitrage strategies. Statistical arbitrage is an investment strategy that takes advantage of temporal price differences between similar assets. The success of this strategy depends on the portfolios of the assets, their temporary price deviations, and how trading could optimize the result of the trade. Machine learning using neutral networks can optimally solve these complex arbitrage prediction issues because neutral networks—deep learning techniques—can learn the arbitrage signals and make a trading allocation. Consider now stock market movements. Deep learning can work with nonlinear equations and examine neutral networks containing public market data. Research has already suggested that deep learning systems could predict stock prices, develop financial decisions, and analyze the stock price movement of a particular stock.
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