An Aggregated Decision Making Aid in Personal Investment Analysis

Ai Yue

Abstract

This paper studies the application of various Multiple Criteria Decision Analysis (MCDA) tools in personal investment decision making and provides an explicit case study based on Chinese fund market.

This paper extends the traditional risk and return 2-criteria optimization model to address different preference of individuals. For return, instead of using a single mathematical representation of return, the following 3 sub-criteria related to return are considered: 6-month return, 24-month return, and dividend payout rate. There 3 subcriteria address different individual preferences over either short term or long term, and over either cash flow or book value gain. Similarly, for risk, the following 3 subcriteria are considered: volatility, down-side risk measured by MorningStar risk rating, and the fund category risk. While volatility addressed the uncertainty of the return, the MorningStar risk rating addressed specifically the downside risk of loss. On the other side, the fund category risk addressed the inherent risk related to the fund investment style and strategy. With the 6 criteria to be considered in the decision making process, a multi-objective programming model is formulated to optimize different objectives.

A 6-step decision making procedure involving interactions between 3 parties, namely the decision maker, the decision aiding system and the expert knowledge source, is proposed in the paper. A series of MCDA tools, including AHP methods, mutli-attribute utility theory, mutli-objective programming and interactive decision making, are applied to get the satisfying decision.

A specific case study is included to illustrate the process and a comparable case study based on different decision maker profile using historical data is performed to test the effectiveness of the procedure. The result shows that the proposed procedure is able to help the decision maker to make a good decision based on his preference and the performance of the portfolio matches the decision maker’s preference.

Overall, although there are still many limitations to be addressed, this paper moved one step forward in the field of personal investment decision making using MCDA techniques and hopes to provide a convenient, easily accessible, yet useful decision making aid for individual investors in the future.