2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)最新文献_第3页

A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing 市场择时多目标粒子群优化算法的性能研究

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776019

Ismail Mohamed, F. E. B. Otero

{"title":"A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing","authors":"Ismail Mohamed, F. E. B. Otero","doi":"10.1109/CIFEr52523.2022.9776019","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776019","url":null,"abstract":"Market timing is the issue of deciding when to buy or sell a given asset on a financial market. As one of the core issues of algorithmic trading systems, designers of such systems have turned to computational intelligence methods to aid them in this task. In our previous work, we introduced a number of Particle Swarm Optimization (PSO) algorithms to compose strategies for market timing using a novel training and testing methodology that reduced the likelihood of overfitting and tackled market timing as a multiobjective optimization problem. In this paper, we provide a detailed analysis of these multiobjective PSO algorithms and address two limitations in the results presented previously. The first limitation is that the PSO algorithms have not been compared to well-known algorithms or market timing techniques. This is addressed by comparing the results obtained against NSGA-II and MACD, a technique commonly used in market timing strategies. The second limitation is that we have no insight regarding diversity of the Pareto sets returned by the algorithms. We address this by using RadViz to visualize the Pareto sets returned by all the algorithms, including NSGA-II and MACD. The results show that the multiobjective PSO algorithms return statistically significantly better results than NSGA-II and MACD. We also observe that the multiobjective PSOSP algorithm consistently displayed the best spread in its returned Pareto sets despite not having any explicit diversity promoting measures.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114653108","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

Concept and Practice of Artificial Market Data Mining Platform 人工市场数据挖掘平台的概念与实践

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776095

Masanori Hirano, Hiroki Sakaji, K. Izumi

引用次数: 1

Customized Stock Return Prediction with Deep Learning 基于深度学习的定制股票收益预测

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776177

Christopher Felder, Stefan Mayer

引用次数: 0

Instability of financial markets by optimizing investment strategies investigated by an agent-based model 投资策略优化对金融市场不稳定性的影响

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-02-02 DOI: 10.1109/CIFEr52523.2022.9776207

Takanobu Mizuta, Isao Yagi, K. Takashima

{"title":"Instability of financial markets by optimizing investment strategies investigated by an agent-based model","authors":"Takanobu Mizuta, Isao Yagi, K. Takashima","doi":"10.1109/CIFEr52523.2022.9776207","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776207","url":null,"abstract":"Most finance studies are discussed on the basis of several hypotheses, for example, investors rationally optimize their investment strategies. However, the hypotheses themselves are sometimes criticized. Market impacts, where trades of investors can impact and change market prices, making optimization impossible. In this study, we built an artificial market model by adding technical analysis strategy agents searching one optimized parameter to a whole simulation run to the prior model and investigated whether investors’ inability to accurately estimate market impacts in their optimizations leads to optimization instability. In our results, the parameter of investment strategy never converged to a specific value but continued to change. This means that even if all other traders are fixed, only one investor will use backtesting to optimize his/her strategy, which leads to the time evolution of market prices becoming unstable. Optimization instability is one level higher than \"non-equilibrium of market prices.\" Therefore, the time evolution of market prices produced by investment strategies having such unstable parameters is highly unlikely to be predicted and have stable laws written by equations. This nature makes us suspect that financial markets include the principle of natural uniformity and indicates the difficulty of building an equation model explaining the time evolution of prices.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114239016","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

An Earth Mover’s Distance Based Graph Distance Metric For Financial Statements 基于地球移动距离的财务报表图形距离度量

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2021-12-14 DOI: 10.1109/CIFEr52523.2022.9776204

Sander Noels, Benjamin Vandermarliere, Ken Bastiaensen, T. D. Bie

{"title":"An Earth Mover’s Distance Based Graph Distance Metric For Financial Statements","authors":"Sander Noels, Benjamin Vandermarliere, Ken Bastiaensen, T. D. Bie","doi":"10.1109/CIFEr52523.2022.9776204","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776204","url":null,"abstract":"Quantifying the similarity between a group of companies has proven to be useful for several purposes, including company benchmarking, fraud detection, and searching for investment opportunities. This exercise can be done using a variety of data sources, such as company activity data and financial data. However, ledger account data is widely available and is standardized to a large extent. Such ledger accounts within a financial statement can be represented by means of a tree, i.e. a special type of graph, representing both the values of the ledger accounts and the relationships between them. Given their broad availability and rich information content, financial statements form a prime data source based on which company similarities or distances could be computed.In this paper, we present a graph distance metric that enables one to compute the similarity between the financial statements of two companies. We conduct a comprehensive experimental study using real-world financial data to demonstrate the usefulness of our proposed distance metric. The experimental results show promising results on a number of use cases. This method may be useful for investors looking for investment opportunities, government officials attempting to identify fraudulent companies, and accountants looking to benchmark a group of companies based on their financial statements.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126153916","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

High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning 基于深度强化学习的高维股票组合交易

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2021-12-09 DOI: 10.1109/CIFEr52523.2022.9776121

Uta Pigorsch, S. Schäfer

引用次数: 5

Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation 理解消费行为:循环神经网络的解释和诠释

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2021-09-24 DOI: 10.1109/CIFEr52523.2022.9776210

Charl Maree, C. Omlin

{"title":"Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation","authors":"Charl Maree, C. Omlin","doi":"10.1109/CIFEr52523.2022.9776210","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776210","url":null,"abstract":"Micro-segmentation of customers in the finance sector is a nontrivial task and has been an atypical omission from recent scientific literature. Where traditional segmentation classifies customers based on coarse features such as demographics, micro-segmentation depicts more nuanced differences between individuals, bringing forth several advantages including the potential for improved personalization in financial services. AI and representation learning offer a unique opportunity to solve the problem of micro-segmentation. Although ubiquitous in many industries, the proliferation of AI in sensitive industries such as finance has become contingent on the explainability of deep models. We had previously solved the micro-segmentation problem by extracting temporal features from the state space of a recurrent neural network (RNN). However, due to the inherent opacity of RNNs, our solution lacked an explanation. In this study, we address this issue by extracting a symbolic explanation for our model and providing an interpretation of our temporal features. For the explanation, we use a linear regression model to reconstruct the features in the state space with high fidelity. We show that our linear regression coefficients have not only learned the rules used to recreate the features, but have also learned the relationships that were not directly evident in the raw data. Finally, we propose a novel method to interpret the dynamics of the state space by using the principles of inverse regression and dynamical systems to locate and label a set of attractors.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065353","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: 5

Portfolio optimization with choice of a probability measure 具有概率度量选择的投资组合优化

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2021-03-31 DOI: 10.2139/ssrn.3816173

Taiga Saito, Akihiko Takahashi

引用次数: 1