Can low-carbon value bring high returns? Novel quantitative trading from portfolio-of-investment targets in a new-energy market

As the world continues to focus on carbon emissions, many policies have been proposed to achieve low-carbon economies. This has resulted in the innovative development of new-energy sources that may persuade microinvestors to alter their investment behaviors. With this in mind, this paper proposes a dynamic quantitative trading system to assist investors in improving their new-energy sector profitability and better analyzing the potential impacts of selected stocks. First, the closing prices were reconstructed by comparing upward and downward volatility with selected risk factors for label matching and calculating the intersection of the two labels. A proposed quantitative trading system based on deep learning and a convolutional neural network was then used to backtest stock selection decisions in the subsequent six months. A quantitative trading model based on this dynamic new-energy stock selection was found to potentially achieve an annualized return of 493.55%. It was found that the profitability of new-energy stocks in companies with low energy consumption and carbon emissions was significantly better than in companies with high energy consumption and high carbon emissions.