Novel heuristics for Stock portfolio optimization using machine learning and Modern Portfolio Theory

Stock market portfolio optimization is a very important aspect for stock market trading. Various techniques have been proposed in the literature ranging from technical indicator-based methods to statistical methods and machine learning for portfolio optimization. However, there has been lack of effort to find the impact of machine learning along with technical indicators to build optimal stock portfolios for trading. From this line of research, this paper focusses on the use of deep learning-based LSTM (Long Short Term Memory) models along with Ichimoku cloud indicators to build optimal portfolios, which can be used in a practical stock market environment. This model is developed with combining the above prediction models such as LSTM and Ichimoku Cloud Indicators with Modern Portfolio Theory and Sharpe Ratio to optimize the model by reducing the risk to reward ratio. The model is tested with real life data of London Stock Exchange and Bombay Stock Exchange. By doing extensive experimental analysis, it was found that, the model combining LSTM network and Ichimoku Indicator, paired with Modern Portfolio Theory and Sharpe Ratio giving consistent performance in the market despite volatility.

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