Data Science in Finance and Economics最新文献_第2页

Research on crude oil price forecasting based on computational intelligence 基于计算智能的原油价格预测研究

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2023015

Ming Li, Y. Li

{"title":"Research on crude oil price forecasting based on computational intelligence","authors":"Ming Li, Y. Li","doi":"10.3934/dsfe.2023015","DOIUrl":"https://doi.org/10.3934/dsfe.2023015","url":null,"abstract":"The crude oil market, as a complex evolutionary nonlinear driving system, is by nature a highly noisy, nonlinear and deterministic chaotic series of price series. In this paper, a computational intelligence-based portfolio model is constructed to forecast crude oil prices using weekly price data of West Texas intermediate crude oil (WTI) crude oil futures from 2011 to 2021. First, the WTI crude oil price series are decomposed using the ensemble empirical modal decomposition method (EEMD) and the set of component series is reconstructed using the cluster analysis method. Second, the reconstructed series are modeled and predicted using neural network models such as time-delay neural network (TDNN), extreme learning machine (ELM), multilayer perceptron (MLP) and the GM (1, 1) gray prediction algorithm and the output of the model with the best prediction effect for each component is integrated. Finally, the EGARCH model is used to further optimize the predictive power of the combined model and output the final predicted values. The results show that the combined model based on computational intelligence has higher forecasting accuracy than single models such as GM (1, 1), ARIMA, MLP and the combined EEMD-ELM model for forecasting crude oil futures prices.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129771983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What reflects investor sentiment? Empirical evidence from China 什么反映了投资者的情绪?来自中国的经验证据

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021013

Zimei Huang, Zhenghui Li

引用次数: 2

An analysis of enterprise resource planning systems and key determinants using the Delphi method and an analytic hierarchy process 运用德尔菲法和层次分析法对企业资源规划系统及其关键决定因素进行了分析

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2023010

Yueh-Hsia Huang, Lan Sun, Tyng-Bin Ger

{"title":"An analysis of enterprise resource planning systems and key determinants using the Delphi method and an analytic hierarchy process","authors":"Yueh-Hsia Huang, Lan Sun, Tyng-Bin Ger","doi":"10.3934/dsfe.2023010","DOIUrl":"https://doi.org/10.3934/dsfe.2023010","url":null,"abstract":"\u0000 Enterprise resource planning (ERP) systems are software module packages which can be customized up to a certain limit to suit the specific needs of each organization. Many ERP projects have not been effective at achieving all of the intended results due to high cost and high failure risks in ERP implementation. This study integrates the prior theories and knowledge gained from several textile industry practitioners for ERP projects. A two-stage method involving the Delphi and analytic hierarchy process decision support methodology was conducted. Based on the case of the textile industry in Taiwan, the findings illustrate the top 10 key factors: a clear project plan by defined ERP capacity requirement; a limited scope and focused flowchart; goal congruence between ERP project implementation and corporate strategy; top management support and commitment; the extent of standard operating procedures and institutional processes; a user-friendly interface; systems integrated with aggressive schedules and timelines; provision of technical assistance for rapid, effective transfer of best practice interventions; good interdepartmental communication and coordination on a focused issue solution; and enablement of business process reengineering and solid management for project team building. The findings of this research will be beneficial to those apparel companies that adopted the ERP.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129367589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Validation of corporate probability of default models considering alternative use cases and the quantification of model risk 考虑可选用例和模型风险量化的默认模型的公司概率的验证

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2022002

M. Jr.

{"title":"Validation of corporate probability of default models considering alternative use cases and the quantification of model risk","authors":"M. Jr.","doi":"10.3934/dsfe.2022002","DOIUrl":"https://doi.org/10.3934/dsfe.2022002","url":null,"abstract":"\u0000\u0000In this study we consider the construction of through-the-cycle (\"TTC\") probability-of-default (\"PD\") models designed for credit underwriting uses and point-in-time (\"PIT\") PD models suitable for early warning uses, considering which validation elements should be emphasized in each case. We build PD models using a long history of large corporate firms sourced from Moody's, with a large number of financial, equity market and macroeconomic candidate explanatory variables. We construct a Merton model-style distance-to-default (\"DTD\") measure and build hybrid structural and reduced-form models to compare with the financial ratio and macroeconomic variable-only models. In the hybrid models, the financial and macroeconomic explanatory variables still enter significantly and improve the predictive accuracy of the TTC models, which generally lag behind the PIT models in that performance measure. While all classes of models have high discriminatory power by most measures, on an out-of-sample basis the TTC models perform better than the PIT models. We measure the model risk attributable to various model assumptions according to the principle of relative entropy and observe that omitted variable bias with respect to the DTD risk factor, neglect of interaction effects and incorrect link function specification has the greatest, intermediate and least impacts, respectively. We conclude that care must be taken to judiciously choose how we validate TTC vs. PIT models, as criteria may be rather different and apart from standards such as discriminatory power. This study contributes to the literature by providing expert guidance to credit risk modeling, model validation and supervisory practitioners in managing model risk.\u0000\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129908627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A modification term for Black-Scholes model based on discrepancy calibrated with real market data Black-Scholes模型的一种修正术语，它基于与实际市场数据校准的差异

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021017

Xiaozheng Lin, Meiqing Wang, Choi-Hong Lai

引用次数: 1

An investment strategy based on the first derivative of the moving averages difference with parameters adapted by machine learning 一种基于移动平均线差分的一阶导数的投资策略，通过机器学习调整参数

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2022005

A. Wiliński, Mateusz Sochanowski, Wojciech Nowicki

{"title":"An investment strategy based on the first derivative of the moving averages difference with parameters adapted by machine learning","authors":"A. Wiliński, Mateusz Sochanowski, Wojciech Nowicki","doi":"10.3934/dsfe.2022005","DOIUrl":"https://doi.org/10.3934/dsfe.2022005","url":null,"abstract":"\u0000 The article presents a certain investment strategy based on the difference between two moving averages, modified to allow the extraction of patterns. The strategy concept dropped the traditionally considered intersections of two averages and opening positions just after those intersections. Based on the observation of changes happening in the moving averages difference, it has been noticed that for some values of this difference and some values of additional strategy parameters, an interesting pattern appears that allows short-term prediction. These patterns also depended on the first derivative of the moving averages difference and the location of the current price relative to certain thresholds of the difference. Therefore, the strategy uses five parameters, including Stop Loss, adapted to the properties of the time series through machine learning. The importance of machine learning is highlighted by comparing simulation results with and without it. The strategy effectiveness was tested in the Matlab environment on the time series of the WIG20 (primary index of the Warsaw Stock Exchange) historical data. Satisfactory results were obtained considered in terms of minimizing investment risk measured by the Calmar indicator.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130262749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Survey on the application of deep learning in algorithmic trading 深度学习在算法交易中的应用综述

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021019

Yongfeng Wang, Guofeng Yan

引用次数: 3

A bounded rational agent-based model of consumer choice 基于有限理性主体的消费者选择模型

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2023018

Georgios Alkis Tsiatsios, J. Leventides, E. Melas, C. Poulios

{"title":"A bounded rational agent-based model of consumer choice","authors":"Georgios Alkis Tsiatsios, J. Leventides, E. Melas, C. Poulios","doi":"10.3934/dsfe.2023018","DOIUrl":"https://doi.org/10.3934/dsfe.2023018","url":null,"abstract":"In a bottom-up approach, agent-based models have been extensively used in finance and economics in order to understand how macro-level phenomena can emerge from myriads of micro-level behaviours of individual agents. Moreover, in the absence of (big) data there is still the need to test economic theories and understand how macro-level laws can be materialized as the aggregate of a multitude of interactions of discrete agents. We exemplify how we can solve this problem in a particular instance: We introduce an agent-based method in order to generate data with Monte Carlo and then we interpolate the data with machine learning methods in order to derive multi-parametric demand functions. In particular, the model we construct is implemented in a simulated economy with 1000 consumers and two products, where each consumer is characterized by a unique set of preferences and available income. The demand for each product is determined by a stochastic process, incorporating the uncertainty in consumer preferences. By interpolating the data for the demands for various scenarios and types of consumers we derive poly-parametric demand functions. These demand functions are partially in tension with classical demand theory since on certain occasions they imply that the demand of a product increases as its price increases. Our proposed method of generating data from discrete agents with Monte Carlo and of interpolating the data with machine learning methods can be easily generalized and applied to the assessment of economic theories and to the derivation of economic laws in a bottom-up approach.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126202972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time Series forecasting global price of bananas using Hybrid ARIMA-NARNN model 基于ARIMA-NARNN混合模型的全球香蕉价格时间序列预测

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2022013

Y. Chi, Orson Chi

{"title":"Time Series forecasting global price of bananas using Hybrid ARIMA-NARNN model","authors":"Y. Chi, Orson Chi","doi":"10.3934/dsfe.2022013","DOIUrl":"https://doi.org/10.3934/dsfe.2022013","url":null,"abstract":"\u0000 This study tried to demonstrate the role of Time Series models in a modeling and forecasting process using publicly available long-term records of monthly global price of bananas during the period of January 1990 to November 2021 reported in the International Monetary Fund. Following the Box–Jenkins methodology, an ARIMA (2,1,4) with a drift model was selected as the best-fit model for the Time Series, according to its lowest AIC value. Using the Levenberg-Marquardt algorithm, the results revealed that the NARNN model with 12 neurons in the hidden layer and 6 times delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The ARIMA and NARNN models are good at modelling linear and nonlinear problems for the Time Series, respectively. However, using the HYBRID model, a combination of the ARIMA and NARNN models that has both linear and nonlinear modeling capabilities can be a better choice for modeling the Time Series. The comparative results revealed that the HYBRID model with 11 neurons in the hidden layer and 3 times delays yielded higher accuracy than the NARNN model with 12 neurons in the hidden layer and 6 times delays, and the ARIMA (2,1,4) with a drift model, according to its lowest MSE in this study.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121426142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic tail dependence on China's carbon market and EU carbon market 中国碳市场和欧盟碳市场的动态尾部依赖

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021021

Juan Meng, Sichuang Hu, Bin Mo

引用次数: 2