## 1 Introduction  
A portfolio is defined as a collection of investment assets. Portfolio management refers to the process of investment decision making based on customized tactical investment strategies to match maximize the return for each investing time horizon. There are two popular approaches to manage the investment portfolio: traditional and quantitative. Both approaches share some common characteristics such as investigating a small set of key-driving factors of equity values, analyzing historical data to estimate these key drivers, adopting eligibility criteria for stock-selection decisions, and evaluating the performance over time. However, while traditional portfolio management relies heavily on the judgment depth analysis, regime shifts, key characteristics, and qualitative factors, quantitative portfolio management focuses on universe exploration, discipline, verification, risk management, and lower fees. Not only can it uncover mode opportunities, but it can also do a better job of controlling unintended risks [1]. Quantitative trading consists of trading strategies based on quantitative investment analysis, which relies on mathematical models to design an automated trading system. In quantitative trading,portfolio construction is the process of selecting and allocating investment on multiple stocks, which can be understood as diversification in quantitative trading in order to minimize the risk in trading. Market trend, entry and exit trade, price history, and volume are the key factors for each quantitative trading strategy. Developing an accurate forecasting model is considered as the most critical process to construct an efficient portfolio in the quantitative approach. In quantitative trading, stock prediction plays an important role in forecasting the movement of the market in general or a particular stock. Forecasting the stock price has been considered as one of the most challenging tasks in the financial market owing to the complexity of multivariate time series attributes as well as the amount of involved financial data. Numerous studies have been carried out to enhance prediction accuracy such as statistical and machine learning approaches [2]. Recently, artificial intelligence (AI) and deep learning algorithms offer a number of potential advantages over existing traditional prediction models on both accuracy and decision-making support. Deep learning algorithms allow for designing multiple trading strategies that are implemented consistently and are able to adapt to a real-time market [3; 4]. Although deep learning has been extensively studied for its potentials in stock prediction, little attention has been paid to take advantage of the stock prediction phase to construct efficient quantitative portfolios. In this paper, a special variation of recurrent neural network (RNN), long short-term memory (LSTM), is proposed to build a prediction model for the stock price prediction, and then portfolio optimization techniques are applied to leverage the prediction results. Multiple quantitative portfolios are constructed based on a strategic asset allocation trading strategy. For each experiment, the prediction model achieves high accuracy in prediction, and our constructed portfolios have a considerable return in multiple predicted time periods compared with actual trading. The constructed portfolios outperform to the benchmark Standard and Poor 500 (S&P 500) index in terms of active return and risk control. The main contributions of this paper are summarized as follows:  
* The LSTM prediction model was proposed to predict stock price in order to construct and optimize portfolios in quantitative trading. * Presenting a comparison between LSTM prediction model performance to gated recurrent units (GRUs) and other conventional machine learning models such as linear regression (LR) and support vector regression (SVR) for stock prediction. * Simulation modeling and optimization modeling approaches were used to optimize portfolios in quantitative trading.