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The stock price is the cost of purchasing a security or stock in a stock exchange. The stock price prediction has been the aim of investors since the beginning of the stock market. It is the act of forecasting the future price of a company's stock. Nowadays, deep learning techniques are widely used for identifying the stock trends from large amounts of past data. This research has experimented two big and robust commercial banks listed in the Nepal Stock Exchange (NEPSE) and compared stock price prediction performance of GRU with three widely used gradient descent optimization techniques: Momentum, RMSProp, and Adam. GRU with Adam is more accurate and consistent approach for predicting stock prices from the present study.

Stock price prediction has been the aim of stock investors since the beginning, which is important for the investors to make rational decisions about buying and selling stocks. Nowadays deep learning techniques and technical indicators are popular tools among researchers for predicting stock prices. Mainly researchers from the field of computer science, Statistics, and finance are actively involving in this research field. This research paper proposed a 3-way gated recurrent unit (3-GRU) architecture to forecast the next day's close price. The model is a combination of component GRU networks, where each component GRU network predicts the next day's close price using a different set of technical indicators. The proposed 3-GRU model was evaluated by comparing its performance with all component GRU networks. Its performance was also compared with the GRU network that combines different sets of the technical indicators into one feature vector uses it to predict the next day's close price.From the experimental results, we observed that the 3-GRU network architecture is able to predict the next day's close price with lower mean squared error and greater consistency than other models and hence concluded that it is the better approach for predicting next day's stock price.

<p><br />Intelligent stock trading systems are demand of the modern information age. This research paper proposed a directional movement index based machine learning (DMI-ML) strategy for predicting stock trading signals. Performance of the proposed strategy was evaluated in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of profitable trades executed by the trading strategy. In addition, performance of the proposed model was evaluated against the strategies viz. traditional DMI, Buy-Hold. From the experimental results, we observed that the proposed strategy outperformed other strategies in terms of all three parameters. On average, the ARR obtained from the DMI-ML strategy was 52.58% higher than the ARR obtained from the Buy-Hold strategy. At the same time, the ARR of the proposed one was found 75.12% higher than the ARR obtained from the traditional DMI strategy. Furthermore, the Sharpe ratio for the DMI-ML strategy was positive for all stocks. On the other side, the percentage of profitable trades executed by the DMI-ML strategy soared in comparison to the percentage of such trades by the traditional DMI. This study also extended analysis of the proposed model with the various intelligent trading strategies proposed by authors in various literatures and concluded that the proposed DMI-ML strategy is the better strategy for stock trading.</p>