A comprehensive survey on NSGA-II for multi-objective optimization and applications

Peer-to-peer (P2P) lending has attracted many investors and borrowers since 2005. This financial market helps investors and borrowers to invest in or get loans without a traditional financial intermediary. Investors in the P2P lending market are allowed to invest in multiple loans instead of financing one loan entirely, so investment decision-making in P2P lending can be challenging for lenders because they are not usually expert in loan investing. The goal of this paper is to propose a data-driven investment decision-making framework for this competitive market. We use the artificial neural network and logistic regression to estimate the return and the probability of default (PD) of each individual loan. The return variable is the internal rate of return (IRR). Moreover, we formulate the investment decision-making in P2P lending as a multi-objective portfolio optimization problem based on the mean-variance theory by the use of the non-dominated sorting genetic algorithm (NSGA2). To validate the proposed model, we use a real-world dataset from one of the most popular P2P lending marketplaces. In addition, our model is compared with a single-objective model and a profit-based approach. Throughout the experiment, the empirical results reveal that our multi-objective model in comparison with the single-objective model can improve a lender's investment decision based on both objectives of investments. It means that while the return increases, the risk decreases, simultaneously. On the other hand, it is concluded that the profit scoring model leads to a more profitable investment but with a high level of risk. Finally, a sensitivity analysis is done to check the sensitivity of our model to the total investment amount.

A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending

Vulnerable nature of price forecasts, such as an unpredictability of future and numbers of socio-economic factors that affect market stability, often makes investment risky. Earlier studies in Finance suggested that constructing a portfolio can promise risk-spread gains. While Fund Standardization improved the traditional theories by reducing the computational complexity and by associating every interaction in the portfolio, such a method still cannot become a winning strategy because it does not measure the current value or the relative price of each asset. Inspired by the works of finding returns per risk, we attempt to design an optimal portfolio by searching products that have potential to grow further. More specifically, we first analyze risk-adjusted returns in the previous periods and use their inertia as a momentum. However, because historic movements alone do not fully elucidate future changes nor guarantee positive returns, we scored the relative values of each stock to make more informed estimations. Using the Capital Asset Pricing Model, we measured the values of each stock and determined those undervalued. In this study, we applied a Genetic Algorithm to optimize portfolios while incorporating the momentum strategy and the asset valuations. The proposed GA model was tested in two separate markets, S&P500 and KOSPI200, and projected greater profits than that from both the previous method with momentum method and the market indexes. From the experimental results, the proposed CAPM+ method was found to be very effective in financial data analysis and to lay a groundwork for a sustainable investment execution.

/pdf/a-genetic-algorithm-ga-approach-to-the-portfolio-design-30502b5vzl.pdf

A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

Finding useful portfolios that could be a portfolio of trading strategy or a stock portfolio from financial datasets is always an attractive research topic due to the nature of financial markets. Because investors always want an approach that can continually provide various portfolios, the issue of group stock portfolio optimization (GSPO) has been raised and the algorithms to obtain a group stock portfolio (GSP) have also been described in the past. A GSP divides the whole set of stocks into several stock groups, and the stocks in a group are exchangeable in investment. Thus, when investors are not satisfied with a suggested stock, they can select another stock from the same group to replace the original one. However, the industry diversity of stocks within a group is not regarded in the existing literatures. In this paper, an algorithm for dealing with the diverse group stock portfolio optimization (DGSPO) is proposed to obtain a diverse group stock portfolio (DGSP). The proposed algorithm is based on the group genetic algorithm with the chromosome representation and the fitness function designed for the purpose of finding a good DGSP. Especially, a factor called group diversity is designed to diversify stocks from different industries and is considered in the fitness evaluation. Another factor considers cash dividend is also applied to keep companies with good quality in the portfolio for increasing the profit of a DGSP. Two real financial datasets are used in the experiments to verify the effectiveness of the proposed approach.

/pdf/an-effective-approach-for-the-diverse-group-stock-portfolio-4k3pp1rs34.pdf

An Effective Approach for the Diverse Group Stock Portfolio Optimization Using Grouping Genetic Algorithm

Optimizing a stock portfolio from a given financial dataset is always a very attractive task, as various factors should be considered. Hence, many methods based on evolutionary algorithms have been developed in the past decades to deal with the portfolio optimization problem. To provide a more flexible stock portfolio, we propose an algorithm to optimize a group stock portfolio by using a grouping genetic algorithm. In accordance with the optimized group stock portfolio, many stock portfolios can be generated and provided to investors. Each chromosome in the genetic algorithm is composed of a grouping part, a stock part and a stock portfolio part. The grouping and stock parts are used to indicate how to divide stocks into groups. The stock portfolio part is used to represent how many stocks should be selected from groups to form a portfolio and what units should be purchased. Four fitness functions are designed to evaluate each individual. Each of them is composed of the group balance, the unit balance, the stock price balance and the portfolio satisfaction. Genetic operations, including crossover, mutation and inversion, are then executed to obtain new offspring to find the best solution. Furthermore, the proposed approach with a trading mechanism is designed to get a more useful group stock portfolio. Experiments on 31 stocks in accordance with four scenarios were conducted to show the merits and effectiveness of the proposed approach.

An intelligence approach for group stock portfolio optimization with a trading mechanism

This paper introduces a multi-objective genetic algorithm (MOGA) in regard to the portfolio optimization issue in different risk measures, such as mean-variance, semi-variance, mean-variance-skewness, mean-absolute-deviation and lower-partial-moment to optimize risk of portfolio. This study introduces a PONSGA model by appling the non-dominated sorting genetic algorithm (NSGA-II) to maximize both the expected return and the skewness of portfolio as well as to simultaneously minimize different portfolio risks. The experimental results demonstrated that the PONSGA approach is significantly superior to the GA in all performances, examined such as the coefficient of variation, Sharpe index, Sortino index and portfolio performance index. The statistical significance tests also showed that the NSGA-II models outperformed the GA models in different risk measures.

Portfolio optimization and risk measurement based on non-dominated sorting genetic algorithm

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Portfolio optimization and risk measurement based on non-dominated sorting genetic algorithm