Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural network that is technically indistinguishable from the reality, which guarantees the reasonableness of the experiment. Second, GAN generates multitudes of fake data, which implements half of the experiment. In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment). The new architecture is termed GANr. Battling against two distinctive networks: discriminator and regressor, GANr's generator aims to simulate a stock market that is close to the reality while allow for all possible scenarios. The resulting portfolio resembles a robust portfolio with data-driven ambiguity. Our empirical studies show that GANr portfolio is more resilient to bleak financial scenarios than CLSGAN and LASSO portfolios.
{"title":"Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection","authors":"Chi Seng Pun, Lei Wang, H. Y. Wong","doi":"10.2139/ssrn.3613739","DOIUrl":"https://doi.org/10.2139/ssrn.3613739","url":null,"abstract":"Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural network that is technically indistinguishable from the reality, which guarantees the reasonableness of the experiment. Second, GAN generates multitudes of fake data, which implements half of the experiment. In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment). The new architecture is termed GANr. Battling against two distinctive networks: discriminator and regressor, GANr's generator aims to simulate a stock market that is close to the reality while allow for all possible scenarios. The resulting portfolio resembles a robust portfolio with data-driven ambiguity. Our empirical studies show that GANr portfolio is more resilient to bleak financial scenarios than CLSGAN and LASSO portfolios.","PeriodicalId":260722,"journal":{"name":"PROD: Simulation (Topic)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128613473","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}
This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the simultaneous generalized method of moments to get each component of the variance covariance of the disturbance in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.
{"title":"Simultaneous Generalized Method of Moments Estimator for Panel Data Models with Spatially Correlated Error Components","authors":"Marius C. O. Amba, Taoufiki Mbratana","doi":"10.2139/ssrn.3103564","DOIUrl":"https://doi.org/10.2139/ssrn.3103564","url":null,"abstract":"This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the simultaneous generalized method of moments to get each component of the variance covariance of the disturbance in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.","PeriodicalId":260722,"journal":{"name":"PROD: Simulation (Topic)","volume":"39 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132237048","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}
This article deals with the analysis of large or complex system dynamics (SD) models, exploring the benefits of a multimethodological approach to model analysis. We compare model analysis results from SD and social network analysis (SNA) by deploying SNA techniques on a pertinent example from the SD literature — the world dynamics model. Although SNA is a clearly distinct method from SD in that it focuses on social actors and their interrelationships, we contend that SD can indeed learn from SNA, particularly in terms of model structure analysis. Our argumentation follows renowned system dynamicists who acknowledge the potential of SD to synthesize and advance theories in social science at both the conceptual and technical levels.
{"title":"Can System Dynamics Learn from Social Network Analysis?","authors":"Lukas Schoenenberger, A. Schenker-Wicki","doi":"10.2139/ssrn.2550593","DOIUrl":"https://doi.org/10.2139/ssrn.2550593","url":null,"abstract":"This article deals with the analysis of large or complex system dynamics (SD) models, exploring the benefits of a multimethodological approach to model analysis. We compare model analysis results from SD and social network analysis (SNA) by deploying SNA techniques on a pertinent example from the SD literature — the world dynamics model. Although SNA is a clearly distinct method from SD in that it focuses on social actors and their interrelationships, we contend that SD can indeed learn from SNA, particularly in terms of model structure analysis. Our argumentation follows renowned system dynamicists who acknowledge the potential of SD to synthesize and advance theories in social science at both the conceptual and technical levels.","PeriodicalId":260722,"journal":{"name":"PROD: Simulation (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134515875","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}
Pub Date : 2000-07-01DOI: 10.1080/10196780050177062
Veith Körner, Hans-Dieter Zimmermann
he developments of the Digital Economy will have a fundamental impact on the structures and processes of economic systems. Within this new economy the importance of the creation and maintenance of a relationship between a customer and a supplier is increasing, but the way to handle this relationship is changing. Therefore, we focus on the upcoming challenges to relationships between suppliers and customers. Viewing this focus in more detail we will develop the outline of a new, holistic approach to the management of customer relationship in Business Media. In order to manage the relationship between suppliers and customers in a successful way, a model for the Management of Customer Relationship will be presented. This model includes several building blocks, which should be considered when designing and maintaining the relationship in an online environment. This model should, therefore, serve as a framework to combine the various instruments of the Management of Customer Relationship.
{"title":"Management of Customer Relationship in Business Media (MCR-BM)","authors":"Veith Körner, Hans-Dieter Zimmermann","doi":"10.1080/10196780050177062","DOIUrl":"https://doi.org/10.1080/10196780050177062","url":null,"abstract":"he developments of the Digital Economy will have a fundamental impact on the structures and processes of economic systems. Within this new economy the importance of the creation and maintenance of a relationship between a customer and a supplier is increasing, but the way to handle this relationship is changing. Therefore, we focus on the upcoming challenges to relationships between suppliers and customers. Viewing this focus in more detail we will develop the outline of a new, holistic approach to the management of customer relationship in Business Media. In order to manage the relationship between suppliers and customers in a successful way, a model for the Management of Customer Relationship will be presented. This model includes several building blocks, which should be considered when designing and maintaining the relationship in an online environment. This model should, therefore, serve as a framework to combine the various instruments of the Management of Customer Relationship.","PeriodicalId":260722,"journal":{"name":"PROD: Simulation (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108071","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}
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