Computational Economics最新文献_第8页

Perturbating and Estimating DSGE Models in Julia 在 Julia 中扰动和估计 DSGE 模型

IF 2 4区经济学

Computational Economics Pub Date : 2024-06-02 DOI: 10.1007/s10614-024-10632-2

Alvaro Salazar-Perez, Hernán D. Seoane

引用次数: 0

Predicting the Brazilian Stock Market with Sentiment Analysis, Technical Indicators and Stock Prices: A Deep Learning Approach 利用情绪分析、技术指标和股票价格预测巴西股市：深度学习方法

IF 2 4区经济学

Computational Economics Pub Date : 2024-06-01 DOI: 10.1007/s10614-024-10636-y

Arthur Emanuel de Oliveira Carosia, Ana Estela Antunes da Silva, Guilherme Palermo Coelho

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引用次数: 0

On the Efficiency of the Informal Currency Markets: The Case of the Cuban Peso 论非正规货币市场的效率：古巴比索案例

IF 2 4区经济学

Computational Economics Pub Date : 2024-05-31 DOI: 10.1007/s10614-024-10638-w

Alejandro García-Figal, Alejandro Lage-Castellanos, Daniel A. Amaro, R. Mulet

引用次数: 0

PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price PCA-ICA-LSTM：基于降维方法的混合深度学习模型，用于预测标准普尔 500 指数价格

IF 2 4区经济学

Computational Economics Pub Date : 2024-05-28 DOI: 10.1007/s10614-024-10629-x

Mehmet Sarıkoç, Mete Celik

{"title":"PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price","authors":"Mehmet Sarıkoç, Mete Celik","doi":"10.1007/s10614-024-10629-x","DOIUrl":"https://doi.org/10.1007/s10614-024-10629-x","url":null,"abstract":"<p>In this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key limitations in existing research: (1) the lack of standardized datasets, time scales, and evaluation metrics, and (2) the focus on prediction return. The proposed model employs a two-stage preprocessing approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and de-noising, followed by Independent Component Analysis (ICA) for feature extraction. A Long Short-Term Memory (LSTM) network with five layers is fed with this preprocessed data to predict the price of the next day using a 5 day time horizon. To ensure comparability with existing literature, experiments employ an 18 year dataset of the Standard & Poor's 500 (S&P500) index and include over 40 technical indicators. Performance evaluation encompasses six metrics, highlighting the model's superiority in accuracy and return rates. Comparative analyses demonstrate the superiority of the proposed PCA-ICA-LSTM model over single-stage statistical methods and other deep learning architectures, achieving notable improvements in evaluation metrics. Evaluation against previous studies using similar datasets corroborates the model's superior performance. Moreover, extensions to the study include adjustments to dataset parameters to account for the COVID-19 pandemic, resulting in improved return rates surpassing traditional trading strategies. PCA-ICA-LSTM achieves a 220% higher return compared to the “hold and wait” strategy in the extended S&P500 dataset, along with a 260% higher return than its closest competitor in the comparison. Furthermore, it outperformed other models in additional case studies.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141173200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition 利用情绪分析和经验模式分解预测比特币价格

IF 2 4区经济学

Computational Economics Pub Date : 2024-05-28 DOI: 10.1007/s10614-024-10588-3

Serdar Arslan

{"title":"Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition","authors":"Serdar Arslan","doi":"10.1007/s10614-024-10588-3","DOIUrl":"https://doi.org/10.1007/s10614-024-10588-3","url":null,"abstract":"<p>Cryptocurrencies have garnered significant attention recently due to widespread investments. Additionally, researchers have increasingly turned to social media, particularly in the context of financial markets, to harness its predictive capabilities. Investors rely on platforms like Twitter to analyze investments and detect trends, which can directly impact the future price movements of Bitcoin. Understanding and analyzing Twitter sentiments can potentially provide insights into future Bitcoin price movements and can shed light on how investor sentiment affects cryptocurrency markets. In this study, we explore the correlation between Twitter activity and Bitcoin prices by examining tweets related to Bitcoin price sentiments. Our proposed model consists of two distinct networks. The first network exclusively utilizes historical price data, which is further decomposed into various components using the Empirical Mode Decomposition method. This decomposition helps mitigate the impact of irregular fluctuations on Bitcoin price predictions. Each of these components is then separately processed by Long Short-Term Memory (LSTM) networks. The second network focuses on modeling user sentiments and emotions in conjunction with Bitcoin market data. User opinions are categorized into positive and negative classes and are integrated with historical data to predict the next-day price using LSTM networks. Finally, the outputs of each network are combined to form the ultimate prediction values. Experimental results demonstrate that Twitter sentiment can effectively helps us predict Bitcoin price trends. Furthermore, to validate our proposed model, we compared it with several state-of-the-art methods. The results indicate that our approach outperforms these existing models in terms of accuracy.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141171615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On a Black–Scholes American Call Option Model 关于布莱克-斯科尔斯美式看涨期权模型

IF 2 4区经济学

Computational Economics Pub Date : 2024-05-25 DOI: 10.1007/s10614-024-10623-3

Morteza Garshasbi, Shadi Malek Bagomghaleh

引用次数: 0

In Memoriam David A. Kendrick (1937–2024) 悼念大卫-肯德里克（1937-2024）

IF 2 4区经济学

Computational Economics Pub Date : 2024-05-25 DOI: 10.1007/s10614-024-10612-6

Hans Amman, Ruben Mercado, Berç Rustem

引用次数: 0

A Hybrid Multi-population Optimization Algorithm for Global Optimization and Its Application on Stock Market Prediction 全局优化的混合多人口优化算法及其在股市预测中的应用