To train their CNN for the experiment, the research team used S&P 500 data from 2009 to 2016. Taking into account present-day factors increases the yield over both random guessing and trading algorithms not capable of real-time learning. Their proposed network can adjust its buy/sell thresholds based on what is happening both in the present moment and the past. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.īy letting their proposed network analyze current data layered over past data, they are taking market forecasting a step further, allowing for a type of learning that more closely mirrors the intuition of a seasoned investor rather than a robot. At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. The University of Cagliari-based team set out to create an AI-managed “buy and hold” (B&H) strategy - a system of deciding whether to take one of three possible actions - a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica. Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning - a discipline within artificial intelligence - to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. Researchers combined convolutional neural networks (CNNs) with deep learning to develop a market forecasting system that may have greater gains and fewer losses than previous AI-based attempts to manage stock portfolios.
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