基于人工神经网络和粒子群优化技术的金融危机预测智能混合模型

IF 1.1 Q3 STATISTICS & PROBABILITY
Maryam Maryam, Dimas Aryo Anggoro, Muhibah Fata Tika, Fitri Cahya Kusumawati
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引用次数: 1

摘要

金融危机预测是经济现象中的一个关键问题。正确的预测可以为利益相关者制定政策以维护和增加经济稳定提供知识。已经开发了几种预测金融危机的方法。然而,分类模型的性能和预测精度以及法律数据不足以在实际应用中使用。因此,需要一个高效的预测模型来获得更高的性能结果。本文采用了一种新的双混合智能预测模型,采用人工神经网络(ANN)进行预测,粒子群优化(PSO)进行优化。首先,PSO技术为ANN产生超参数值,以拟合最佳架构。它们是权重和阈值。然后,它们被用来预测给定数据集的性能。最后,ANN-PSO生成危机条件的预测值。所提出的ANN-PSO模型是在印度尼西亚经济状况的时间序列数据上实现的。数据集来自国际货币基金组织和印度尼西亚经济和金融统计局。使用13个潜在指标的自变量数据,即进口、出口、贸易汇率、外汇储备、综合股价指数、实际汇率、实际存款利率、银行存款、贷款和存款利率、实际BI利率与实际美联储利率之差、M1、M2乘数以及M2与外汇储备之比。同时,因变量使用基于财务压力指数的完美信号值。还通过阈值对数据集进行了详细的统计分析,以传达危机情况。实验分析表明,基于不同的评价标准,该模型是可靠的。案例分析表明,预测数据的结果与实际情况基本一致,对金融危机的预测有很大帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Hybrid Model Using Artificial Neural Networks and Particle Swarm Optimization Technique For Financial Crisis Prediction
Financial crisis prediction is a critical issue in the economic phenomenon. Correct predictions can provide the knowledge for stakeholders to make policies to preserve and increase economic stability. Several approaches for predicting the financial crisis have been developed. However, the classification model's performance and prediction accuracy, as well as legal data, are insufficient for usage in real applications. So that, an efficient prediction model is required for higher performance results. This paper adopts a novel two-hybrid intelligent prediction model using an Artificial Neural Network (ANN) for prediction and Particle Swarm Optimization (PSO) for optimization. At first, a PSO technique produces the hyperparameter value for ANN to fit the best architecture. They are weights and thresholds. Then, they are used to predict the performance of the given dataset.  In the end, ANN-PSO generates predictions value of crisis conditions. The proposed ANN-PSO model is implemented on time series data of economic conditions in Indonesia. Dataset was obtained from International Monetary Fund and the Indonesian Economic and Financial Statistics. Independent variable data using 13 potential indicators, namely imports, exports, trade exchange rates, foreign exchange reserves, the composite stock price index, real exchange rates, real deposit rates, bank deposits, loan and deposit interest rates, the difference between the real BI rate and the real FED rate, the M1, M2 multiplier, and the ratio of M2 to foreign exchange reserves. Meanwhile, the dependent variable uses the perfect signal value based on the Financial Pressure Index. A detailed statistical analysis of the dataset is also given by threshold value to convey crisis conditions. Experimental analysis shows that the proposed model is reliable based on the different evaluation criteria. The case studies show that the result for predictive data is basically consistent with the actual situation, which has greatly helped the prediction of a financial crisis.  
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来源期刊
CiteScore
3.30
自引率
26.70%
发文量
53
期刊介绍: Pakistan Journal of Statistics and Operation Research. PJSOR is a peer-reviewed journal, published four times a year. PJSOR publishes refereed research articles and studies that describe the latest research and developments in the area of statistics, operation research and actuarial statistics.
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