Dynamic asset combination transaction method and system

Abstract

The invention discloses a dynamic asset combination transaction method. The method comprises the following steps: S2, establishing a capital asset pricing model; S4, obtaining a total optimization model according to the capital asset pricing model; S6, obtaining a dynamic weight optimization model according to the total optimization model; and S8, outputting an adjustment scheme according to the dynamic weight optimization model. The method has the following beneficial effects: the scheme of the invention performs optimization function calculation in real time according to the market environment, so that the response speed is high enough for the change of all factors influencing the decision; the transaction decision of the dynamic asset combination scheme is carried out all the time, so that the impact cost is low; correlation among different assets, beta risk indexes of total position holding, buying and selling cost functions and different states of risk indexes in a position holding set at different moments are all considered in scheme decisions, and are harmoniously unified together by utilizing a formula, which is also not possessed by a traditional asset combination scheme.

Description

technical field [0001] This application relates to the field of asset portfolio transactions, and in particular, relates to a method and system for dynamic asset portfolio transactions. Background technique [0002] In the field of quantitative investment, generating forecast signals and allocating asset portfolios are two very important issues. Classical investment models use multi-factor models to generate signals, and use static portfolio optimization models to complete the construction of portfolios. However, existing solutions have the following disadvantages: [0003] 1. Most of the traditional asset portfolio schemes are static rebalancing schemes, and their operating logic is to make separate decisions at each time point. The core problem is that (1) the model cannot respond in real time according to changes in the market environment (for example, if it is not If there is a strong sell signal on the rebalancing day, the system cannot make a reasonable response in r...

AI Technical Summary

Problems solved by technology

[0003] 1. Most of the traditional asset portfolio schemes are static rebalancing schemes, and their operating logic is to make separate decisions at each time point. The core problem is that (1) the model cannot respond in real time according to changes in the market environment (for example, if it is not If there is a strong sell signal on the rebalancing day, the system cannot make a reasonable response in real time)

(2) Rebalancing at the same time node has a high impact cost

[0004] 2. Several traditional asset portfolio schemes are highly homogeneous and lack innovation in the process of investment practice, while the uniqueness of the financial market lies in "market effectiveness" (that is, the more a set of schemes is practiced, the more effective it will be. will be worse

[0005] 3. A fatal shortcoming of the reinforcement learning asset portfolio scheme is that its utilization rate of model training samples is very low, which is why the current reinforcement learning asset portfolio scheme only exists in theory and has few applications

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