Market traders often engage in the frequent transaction of volatile assets to optimize their total return. In this study, we introduce a novel investment strategy model, anchored on the 'lazy factor.' Our approach bifurcates into a Price Portfolio Forecasting Model and a Mean-Variance Model with Transaction Costs, utilizing probability weights as the coefficients of laziness factors. The Price Portfolio Forecasting Model, leveraging the EXPMA Mean Method, plots the long-term price trend line and forecasts future price movements, incorporating the tangent slope and rate of change. For short-term investments, we apply the ARIMA Model to predict ensuing prices. The Mean-Variance Model with Transaction Costs employs the Monte Carlo Method to formulate the feasible region. To strike an optimal balance between risk and return, equal probability weights are incorporated as coefficients of the laziness factor. To assess the efficacy of this combined strategy, we executed extensive experiments on a specified dataset. Our findings underscore the model's adaptability and generalizability, indicating its potential to transform investment strategies.

翻译：市场交易者常通过频繁交易高波动性资产来优化总回报。本研究提出一种基于"惰性因子"的新型投资策略模型，该模型采用双分支架构：价量组合预测模型与含交易成本的均值-方差模型，并以概率权重作为惰性因子的系数。价量组合预测模型借助EXPMA均值方法绘制长期价格趋势线，通过切线斜率与变动率预测未来价格走势；针对短期投资则应用ARIMA模型进行后续价格预测。含交易成本的均值-方差模型采用蒙特卡洛方法构建可行域，为在风险与收益间取得最优平衡，引入等概率权重作为惰性因子的系数。为评估该组合策略的有效性，我们在特定数据集上开展了大量实验。研究结果表明该模型具有良好的适应性与泛化能力，展现出变革投资策略的潜力。