Loan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers fulfill the repayment plan; however, some of them fail to pay back their loans. Therefore, an imbalanced classification method can be utilized to discriminate such default borrowers. In this context, the aim of this paper is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based entropy fuzzy support vector machine (IEFSVM). IEFSVM is a modified version of the existing entropy fuzzy support vector machine (EFSVM) in terms of an instance-based scheme. IEFSVM can reflect the pattern of nearest neighbors entropy with respect to the change of its size instead of fixing it in unified neighborhood size. Therefore, IEFSVM allows the class change of nearest neighbors in the determination of fuzzy membership. Applying the model to the lending club dataset, we determine loans that are predicted to be fully paid. Then, we also provide a multiple regression model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. Throughout the experiment, the empirical results reveal that IEFSVM outperforms not only EFSVM but also the six other state-of-the-art classifiers including the cost-sensitive adaptive boosting, cost-sensitive random forest, EasyEnsemble, random undersampling boosting, weighted extreme learning machine, and cost-sensitive extreme gradient boosting in terms of loan status classification. Also, the investment performance of the multiple regression model using IEFSVM is higher and more robust than that of two other benchmarks. In this regard, we conclude that the proposed investment model is a decent and practical approach to support decisions in the P2P lending market.INDEX TERMS Entropy, support vector machines, financial management, decision support systems, peer-to-peer lending.

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample's neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.

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The efficient market hypothesis (EMH) assumes that all available information in an efficient financial market is ideally fully reflected in the price of an asset. However, whether the reality that asset prices are not informational efficient is an opportunity for profit or a systemic risk of the financial system that needs to be corrected is still a ubiquitous concept, so many economic participants and research scholars have conducted related studies in order to understand the phenomenon of the financial market. This research employed attention entropy of the log-returns of 27 global assets to analyze the time-varying informational efficiency. International markets could be classified hierarchically into groups with similar long-term efficiency trends; however, at the same time, the ranks and clusters were found to remain stable only for a short period of time in terms of short-term efficiency. Therefore, a complex network representation analysis was performed to express whether the short-term efficiency patterns have interacted with each other over time as a coherent picture. It was confirmed that the network of 27 international markets was fully connected, strongly globalized and entangled. In addition, the complex network was composed of two modular structures grouped together with similar efficiency dynamics. As a result, although the informational efficiency of financial markets may be globalized to a high-efficiency state, it shows a collective dynamics pattern in which the global system may fall into risk due to the spread of systemic risk.