Narrow Results

With the constant development of economy, how to reasonably allocate limited loans to enterprises has been an interesting issue. As a result, adaptive decision for optimal loan allocation schemes is well worth investigating. Although some researchers had utilized machine learning-based techniques to deal with such issue, they cannot handle well the scenarios where total capital amount is limited. To bridge such gap, this paper proposes an intelligent decision framework for loan allocation schemes in complex social systems. First of all, the expressions about profit and risk of the bank side are deduced, separately. On this basis, the dynamic planning approach is adopted to formulate a set of optimization model. Specifically, such model is established from two aspects: profit maximization and risk minimization. Hence, two groups of decision results can be reached from two different perspectives by searching optimal solution of the planning model. This work also gives a case study on a real-world dataset to present process of the planning model. Thus, two referential results are provided with use of optimization solution tool.

With the rapid development of intelligent techniques, smart finance has become a hot topic in daily life. Currently, financial credit is facing increasing business volume, and it is expected that investigating the intelligent algorithms can help reduce human labors. In this area, the prediction of latent credit default behaviors can help deal with loan approval affairs, and it is the most important research topic. Machine learning-based methods have received much attention in this area, and they can achieve proper performance in some scenarios. However, machine learning-based models cannot have resilient objective function, which can cause failure in having stable performance in different problem scenarios. This work introduces deep learning that has the objective function with high freedom degree, and proposes an artificial neural network-based intelligent prediction model for financial credit default behaviors. The whole technical framework is composed of two stages: information encoding and backbone network. The former makes encoding toward initial features, and the latter builds a multi-layer perceptron to output prediction results. Finally, the experiments are conducted on a real-world dataset to evaluate the efficiency of the proposed approach.

Data-driven smart investment decisions are important for financial development, which has not received much attention from academia. As a result, this paper resorts to the evolutionary game theory, and proposes a novel multi-agent financial investment decision method. Specifically, an evolutionary game theory-based decision-making approach is formulated as the main model for the research purpose. By considering the strategic choices and adaptability among various entities, a comprehensive analysis of the behavior and decision-making process of entities in the financial market is achieved. This paper combines stock exchanges and financial data providers (Bloomberg and Thomson Reuters) to conduct case studies on this method, verifying its effectiveness and feasibility in practical applications. By comparing traditional financial investment decision-making methods, it can be seen that the proposal has significant advantages in improving investment efficiency, reducing risks, and responding to market volatility. This paper delves into the multi-agent financial investment decision-making method based on the evolutionary game, providing new ideas and methods for academic research and practical applications in the financial field.

Most of the existing research works on stock price prediction ignore the synergistic effect of multi-source factors. Therefore, an enterprise stock price prediction method based on multi-source information fusion based on deep neural network (DNN) is proposed. Based on the condition of multi-source information fusion, the original data related to enterprise stock price are collected from multiple sources, and the structure level and model framework of the method are constructed. The feature extraction technology of DNN is used to extract the features useful for stock price prediction from the pre-processed data, and the DNN structure considering multi-source information fusion is established. Finally, feature extraction and evaluation settings are completed based on data variables. LOSS, ACCURACY and other indicators were used for analysis. The results show that compared with typical prediction methods, this method can make use of multiple information sources more comprehensively and improve the prediction accuracy. In addition, the proposed method has certain flexibility and can adapt to the characteristics and changes of different stock markets