Author(s)

Tingyue Wang, Jiangrong Qiao

Corresponding Author

Jiangrong Qiao

ABSTRACT

Quantitative investment is an emerging concept of systematic investment approach using computer programs combined with financial theories to perform investment analysis and trading. This paper focuses on designing a quantitative trading strategy to optimize the investment strategy and then designing the BP neural network model based on the Apriori algorithm with a risk function to facilitate the computation of different scenarios for three different types of trader investments, building a dynamic programming model later. Moreover, the robustness and sensitivity analysis of the model is tested, and the model performs stably. Besides, Using transaction cost as a perturbation term, it is concluded that transaction cost grows slightly inversely to the final value of the portfolio and the change in the number of transactions. Finally, The model is less sensitive, has good market adaptability, and has real significance.

KEYWORDS

Apriori algorithm, BP neural network, Quantitative investment, Trading strategy