Pub Date : 2024-05-18DOI: 10.1007/s10614-024-10614-4
Riccardo Lucchetti, Luca Pedini
{"title":"Correction to: The Spherical Parametrisation for Correlation Matrices and its Computational Advantages","authors":"Riccardo Lucchetti, Luca Pedini","doi":"10.1007/s10614-024-10614-4","DOIUrl":"https://doi.org/10.1007/s10614-024-10614-4","url":null,"abstract":"","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124826","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}
{"title":"Portfolio Optimization During the COVID-19 Epidemic: Based on an Improved QBAS Algorithm and a Dynamic Mixed Frequency Model","authors":"Siyao Wei, Pengfei Luo, Jiashan Song, Kunliang Jiang","doi":"10.1007/s10614-024-10621-5","DOIUrl":"https://doi.org/10.1007/s10614-024-10621-5","url":null,"abstract":"","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125670","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}
Pub Date : 2024-05-18DOI: 10.1007/s10614-024-10622-4
Igor Sadoune, Marcelin Joanis, Andrea Lodi
We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI.
{"title":"Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data","authors":"Igor Sadoune, Marcelin Joanis, Andrea Lodi","doi":"10.1007/s10614-024-10622-4","DOIUrl":"https://doi.org/10.1007/s10614-024-10622-4","url":null,"abstract":"<p>We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060426","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}
Pub Date : 2024-05-18DOI: 10.1007/s10614-024-10627-z
Xiaoxiao Liu, Wei Wang
This study examines the influence of the sliding window in the LSTM model on its predictive performance in the stock market. The investigation encompasses three aspects: the impact of the stationarity of the original data, the effect of the time interval, and the influence of the input order of data. Additionally, a standard VAR model is established for a comparative benchmark. The experimental dataset comprises the daily stock index prices of the six major stock markets from the January 2010 to December 2019. The experimental results demonstrate that stationary input data enhances the predictive performance of the LSTM model. Furthermore, shorter time interval tends to yield improved outcomes, while the order of input data does not impact the performance of the LSTM. Although the predictive capability of the LSTM model may not consistently surpass that of the standard VAR model, which is different from the previous research, it serves to compensate for the conditional limitations associated with VAR model construction.
本研究探讨了 LSTM 模型中的滑动窗口对其股市预测性能的影响。研究包括三个方面:原始数据静态性的影响、时间间隔的影响以及数据输入顺序的影响。此外,还建立了一个标准 VAR 模型作为比较基准。实验数据集包括 2010 年 1 月至 2019 年 12 月期间六大股票市场的每日股指价格。实验结果表明,静态输入数据提高了 LSTM 模型的预测性能。此外,较短的时间间隔往往会产生更好的结果,而输入数据的顺序不会影响 LSTM 的性能。虽然 LSTM 模型的预测能力可能无法持续超越标准 VAR 模型,这与之前的研究有所不同,但它可以弥补与 VAR 模型构建相关的条件限制。
{"title":"Improving Sliding Window Effect of LSTM in Stock Prediction Based on Econometrics Theory","authors":"Xiaoxiao Liu, Wei Wang","doi":"10.1007/s10614-024-10627-z","DOIUrl":"https://doi.org/10.1007/s10614-024-10627-z","url":null,"abstract":"<p>This study examines the influence of the sliding window in the LSTM model on its predictive performance in the stock market. The investigation encompasses three aspects: the impact of the stationarity of the original data, the effect of the time interval, and the influence of the input order of data. Additionally, a standard VAR model is established for a comparative benchmark. The experimental dataset comprises the daily stock index prices of the six major stock markets from the January 2010 to December 2019. The experimental results demonstrate that stationary input data enhances the predictive performance of the LSTM model. Furthermore, shorter time interval tends to yield improved outcomes, while the order of input data does not impact the performance of the LSTM. Although the predictive capability of the LSTM model may not consistently surpass that of the standard VAR model, which is different from the previous research, it serves to compensate for the conditional limitations associated with VAR model construction.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060392","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}
{"title":"Pricing Convertible Bonds with the Penalty TF Model Using Finite Element Method","authors":"Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, Dongming Wei","doi":"10.1007/s10614-024-10625-1","DOIUrl":"https://doi.org/10.1007/s10614-024-10625-1","url":null,"abstract":"","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963533","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}
Pub Date : 2024-05-13DOI: 10.1007/s10614-024-10613-5
Yuhe Zhao, Ronghua Ju
{"title":"Investor Structure and Corn Futures Price Volatility in China: Evidence Based on the Agent-Based Model","authors":"Yuhe Zhao, Ronghua Ju","doi":"10.1007/s10614-024-10613-5","DOIUrl":"https://doi.org/10.1007/s10614-024-10613-5","url":null,"abstract":"","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984139","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}
Pub Date : 2024-05-12DOI: 10.1007/s10614-024-10608-2
Xiaoliang Li, Kongyan Chen, Wei Niu, Bo Huang
Since Kopel’s duopoly model was proposed about 3 decades ago, there are almost no analytical results on the equilibria and their stability in the asymmetric case. The first objective of our study is to fill this gap. This paper analyzes the asymmetric duopoly model of Kopel analytically by using several tools based on symbolic computations. We discuss the possibility of the existence of multiple positive equilibria and establish conditions for a given number of positive equilibria to exist. The possible positions of the equilibria in Kopel’s model are also explored. Furthermore, in the asymmetric model of Kopel, if the duopolists adopt the best response reactions or homogeneous adaptive expectations, we establish conditions for the local stability of equilibria for the first time. The occurrence of chaos in Kopel’s model seems to be supported by observations through numerical simulations, which, however, is challenging to prove rigorously. The second objective of this paper is to prove the existence of snapback repellers in Kopel’s map, which implies the existence of chaos in the sense of Li–Yorke according to Marotto’s theorem.
{"title":"Stability and Chaos of the Duopoly Model of Kopel: A Study Based on Symbolic Computations","authors":"Xiaoliang Li, Kongyan Chen, Wei Niu, Bo Huang","doi":"10.1007/s10614-024-10608-2","DOIUrl":"https://doi.org/10.1007/s10614-024-10608-2","url":null,"abstract":"<p>Since Kopel’s duopoly model was proposed about 3 decades ago, there are almost no analytical results on the equilibria and their stability in the asymmetric case. The first objective of our study is to fill this gap. This paper analyzes the asymmetric duopoly model of Kopel analytically by using several tools based on symbolic computations. We discuss the possibility of the existence of multiple positive equilibria and establish conditions for a given number of positive equilibria to exist. The possible positions of the equilibria in Kopel’s model are also explored. Furthermore, in the asymmetric model of Kopel, if the duopolists adopt the best response reactions or homogeneous adaptive expectations, we establish conditions for the local stability of equilibria for the first time. The occurrence of chaos in Kopel’s model seems to be supported by observations through numerical simulations, which, however, is challenging to prove rigorously. The second objective of this paper is to prove the existence of snapback repellers in Kopel’s map, which implies the existence of chaos in the sense of Li–Yorke according to Marotto’s theorem.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941975","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}
Pub Date : 2024-05-10DOI: 10.1007/s10614-024-10605-5
Alexandre Silva de Oliveira, Paulo Sergio Ceretta, Daniel Pastorek
In an experimental study, we investigated the application of artificial neural networks (ANNs) and long-tail probability ranking in constructing investment portfolios to achieve superior returns compared to a benchmark. Our objective is to demonstrate that portfolio formation can be conceptualized as a classification problem by leveraging the inherent capabilities of ANNs to capture complex relationships and facilitate more informed decisions regarding portfolio composition. We conducted the experiment using lagged asset return information to predict stock returns, employing a pilot sample of 70 assets and a validation sample consisting of all companies belonging to the Standard & Poor's 500 (S&P 500) index. The study covers the period from 2018 to 2022, with 585,650 daily observations of active assets. The results indicate that the classification method proposed in this study, using the asymmetric probabilities of the Student´s (t) distribution, outperforms the market and traditional portfolios. Furthermore, the results suggest that the combined approach of ANN and security classification based on their asymmetric leptokurtic probabilities demonstrates superiority over portfolios that rely solely on security signal classification.
{"title":"An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns","authors":"Alexandre Silva de Oliveira, Paulo Sergio Ceretta, Daniel Pastorek","doi":"10.1007/s10614-024-10605-5","DOIUrl":"https://doi.org/10.1007/s10614-024-10605-5","url":null,"abstract":"<p>In an experimental study, we investigated the application of artificial neural networks (ANNs) and long-tail probability ranking in constructing investment portfolios to achieve superior returns compared to a benchmark. Our objective is to demonstrate that portfolio formation can be conceptualized as a classification problem by leveraging the inherent capabilities of ANNs to capture complex relationships and facilitate more informed decisions regarding portfolio composition. We conducted the experiment using lagged asset return information to predict stock returns, employing a pilot sample of 70 assets and a validation sample consisting of all companies belonging to the Standard & Poor's 500 (S&P 500) index. The study covers the period from 2018 to 2022, with 585,650 daily observations of active assets. The results indicate that the classification method proposed in this study, using the asymmetric probabilities of the Student´s <span>(t)</span> distribution, outperforms the market and traditional portfolios. Furthermore, the results suggest that the combined approach of ANN and security classification based on their asymmetric leptokurtic probabilities demonstrates superiority over portfolios that rely solely on security signal classification.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929994","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}
Pub Date : 2024-05-10DOI: 10.1007/s10614-024-10617-1
Davood Pirayesh Neghab, Mucahit Cevik, M. I. M. Wahab, Ayse Basar
The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian–U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada’s main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model’s decisions, which are supported by theoretical considerations.
{"title":"Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning","authors":"Davood Pirayesh Neghab, Mucahit Cevik, M. I. M. Wahab, Ayse Basar","doi":"10.1007/s10614-024-10617-1","DOIUrl":"https://doi.org/10.1007/s10614-024-10617-1","url":null,"abstract":"<p>The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian–U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada’s main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model’s decisions, which are supported by theoretical considerations.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929893","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}