2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)最新文献_第2页

Twin-Delayed Deep Deterministic Policy Gradient Algorithm for Portfolio Selection 投资组合选择的双延迟深度确定性策略梯度算法

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776067

N. Baard, Terence L van Zyl

{"title":"Twin-Delayed Deep Deterministic Policy Gradient Algorithm for Portfolio Selection","authors":"N. Baard, Terence L van Zyl","doi":"10.1109/CIFEr52523.2022.9776067","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776067","url":null,"abstract":"State-of-the-art RL algorithms have shown suboptimal performance in some market conditions with regard to the portfolio selection problem. The reason for suboptimal performance could be due to overestimation bias in actor-critic methods through the use of neural networks as the function approximator. The resulting bias leads to a suboptimal policy being learned by the agent, hindering performance. This research focuses on using the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm for portfolio selection to achieve greater results than previously achieved. In addition, an analysis of the overall effectiveness of the algorithm in various market conditions is needed to determine the TD3’s robustness. This research establishes a RL environment for portfolio selection and trains the TD3 alongside three state-of-the-art algorithms in five different market conditions. The algorithms are tested by allowing the agent to manage a portfolio in each market for a specified period. The results are used for the analysis of the algorithms. The research shows improved results achieved by the TD3 algorithm for portfolio selection compared to other state-of-the-art algorithms. Furthermore, the performance of the TD3 across the five selected markets proves the robustness of the algorithm in its use for the portfolio selection problem.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"11 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":"128059717","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

ANN, LSTM, and SVR for Gold Price Forecasting 基于神经网络、LSTM和SVR的黄金价格预测

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776141

Jiacheng Yang, Denis De Montigny, P. Treleaven

{"title":"ANN, LSTM, and SVR for Gold Price Forecasting","authors":"Jiacheng Yang, Denis De Montigny, P. Treleaven","doi":"10.1109/CIFEr52523.2022.9776141","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776141","url":null,"abstract":"This paper investigates a series of machine learning models (e.g. ANN, LSTM, SVR) to predict gold prices according to traditional indices, emerging indicators, commodities, and historical price time series of gold. In our approach, three machine learning algorithms, Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), are applied to build the models that forecast the gold price. The dataset for this research is a time-series from 1st January 2017 to 31st December 2020, containing two major indices in the US (S&P 500 and DJI), two popular cryptocurrencies (BTC and ETH), two commodities (silver and crude oil), USD index (United States Dollar against Euro), and the gold prices (historical price and volatility) [24]. The evaluation benchmarks are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). In the first stage, a comparative analysis is applied to three models. In the second stage, the assessment of the impact of cryptocurrency on the models is demonstrated. It was observed that the SVR model outperforms the other two models, and our result indicates that the additional data of cryptocurrencies has a positive impact on all three models.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"31 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":"126377925","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: 3

PC Members List 个人电脑会员名单

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/cifer52523.2022.9776146

引用次数: 0

Balancing Profit, Risk, and Sustainability for Portfolio Management 平衡投资组合管理的利润、风险和可持续性

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776048

Charl Maree, C. Omlin

{"title":"Balancing Profit, Risk, and Sustainability for Portfolio Management","authors":"Charl Maree, C. Omlin","doi":"10.1109/CIFEr52523.2022.9776048","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776048","url":null,"abstract":"Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks. This is a particularly well-suited problem for reinforcement learning, as daily rewards are compounding and objective functions may include more than just profit, e.g., risk and sustainability. We developed a novel utility function with the Sharpe ratio representing risk and the environmental, social, and governance score (ESG) representing sustainability. We show that a state- of-the-art policy gradient method – multi-agent deep deterministic policy gradients (MADDPG) – fails to find the optimum policy due to flat policy gradients and we therefore replaced gradient descent with a genetic algorithm for parameter optimization. We show that our system outperforms MADDPG while improving on deep Q-learning approaches by allowing for continuous action spaces. Crucially, by incorporating risk and sustainability criteria in the utility function, we improve on the state-of-the-art in reinforcement learning for portfolio optimization; risk and sustainability are essential in any modern trading strategy, and we propose a system that does not merely report these metrics, but that actively optimizes the portfolio to improve on them.","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":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519297","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

Information Retrieval from Alternative Data using Zero-Shot Self-Supervised Learning 利用零点自监督学习从备选数据中检索信息

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776094

A. Assareh

引用次数: 1

Iterative Filtering Algorithms for Computing Consensus Analyst Estimates 计算共识分析师估计的迭代滤波算法

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776160

Kheng Kua, A. Ignjatović

{"title":"Iterative Filtering Algorithms for Computing Consensus Analyst Estimates","authors":"Kheng Kua, A. Ignjatović","doi":"10.1109/CIFEr52523.2022.9776160","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776160","url":null,"abstract":"In equity investment management, sell side analysts serve an important role in forecasting metrics of companies’ financial performance. These estimates are often produced in an opaque manner, namely, the process upon which the estimate is initiated or revised is not directly observable. With multiple analysts covering the same company, and an analyst covering multiple companies, we have an n-m relationship. The systematic capture of analyst estimates provide a systematic and quantitative proxy for market sentiment. Thus far the academic literature analysing this dataset has resolved to use relatively simple methods for aggregating the individual estimates to arrive at a consensus estimate.In this paper we propose a novel method for aggregating analyst estimates utilising iterative filtering algorithms. This work is inspired by applications of such classes of algorithms to the robust aggregation of sensor network data and online reviews. We conduct experiments using real-world datasets to demonstrate the efficacy of this approach. The results suggest iterative filtering methods improve upon the forecast accuracy of the consensus forecast compared to the simple mean consensus.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"29 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":"132190129","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: 2

Technical and Sentiment Analysis in Financial Forecasting with Genetic Programming 遗传规划在财务预测中的技术和情绪分析

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776186

Eva Christodoulaki, Michael Kampouridis, Panagiotis A. Kanellopoulos

引用次数: 6

State-ANFIS: A Generalized Regime-Switching Model for Financial Modeling 状态- anfis:一种用于金融建模的广义状态切换模型

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776208

Gregor Lenhard, D. Maringer

引用次数: 0

Comparison of Fuzzy Risk Forecast Intervals for Cryptocurrencies 加密货币模糊风险预测区间的比较

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776213

Sulalitha Bowala, Japjeet Singh, A. Thavaneswaran, R. Thulasiram, S. Mandal

{"title":"Comparison of Fuzzy Risk Forecast Intervals for Cryptocurrencies","authors":"Sulalitha Bowala, Japjeet Singh, A. Thavaneswaran, R. Thulasiram, S. Mandal","doi":"10.1109/CIFEr52523.2022.9776213","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776213","url":null,"abstract":"Data-driven volatility models and neuro-volatility models have the potential to revolutionize the area of Computational Finance. Volatility measures the variation of a time series data, and thus it is also a driving factor for the risk forecasting of returns from investment in cryptocurrencies. A cryptocurrency is a decentralized medium of exchange that relies on cryptographic primitives to facilitate the trustless transfer of value between different parties. Instead of being physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions.Many commonly used risk forecasting models do not take into account the uncertainty associated with the volatility of an underlying asset to obtain the risk forecasts. Some tools from the fuzzy set theory can be incorporated into the forecasting models to account for this uncertainty. Interest in the use of hybrid models for fuzzy volatility forecasts is growing. However, a major drawback is that the fuzzy coefficient hybrid models used in fuzzy volatility forecasts are not data-driven. This paper uses fuzzy set theory with data-driven volatility and data-driven neuro-volatility forecasts to study the fuzzy risk forecasts. The study focuses on long-term volatility forecasts with daily price data while briefly exploring forecasting models with high-frequency (hourly) data as an avenue for future research. Simple yet effective models incorporating fuzziness to obtain fuzzy risk volatility forecasts and fuzzy VaR forecasts are presented. The key underlying idea, unlike the existing risk forecasting, is the use of a hybrid nonlinear adaptive fuzzy model for volatility.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"17 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":"126189561","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

Construction of real-time manufacturing industry production activity estimation models using high-frequency electricity demand data 基于高频电力需求数据的制造业生产活动实时估计模型的构建

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776152

Yoshiyuki Suimon, Hiroto Tanabe

{"title":"Construction of real-time manufacturing industry production activity estimation models using high-frequency electricity demand data","authors":"Yoshiyuki Suimon, Hiroto Tanabe","doi":"10.1109/CIFEr52523.2022.9776152","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776152","url":null,"abstract":"In this paper we describe how we estimated production activity in the manufacturing industry in Japan by analyzing the characteristics of fluctuations in the high-frequency electricity demand data published by major Japanese electric power companies, on the basis that the manufacturing industry consumes electricity when carrying out production activity. We constructed mathematical models to estimate production activity in each area of Japan on the basis of electricity data provided by multiple electric power companies, and then combined the estimates generated by these models to estimate production activity in Japan as a whole. The industrial production index published by Japan's Ministry of Economy, Trade and Industry (METI) is an example of government data that reflects production activity in the manufacturing industry. However, the industrial production index for a particular month is not published until the end of the following month, so there is something of a time lag between the production activity itself and the publication of this government data. The method we set out in this paper makes it possible to estimate manufacturing industry production activity around one month before METI's industrial production index is published through the use of highly timely electricity demand data. Furthermore, the industrial production index is normally calculated on a monthly basis, but in this paper, by taking advantage of the high degree of time granularity of the electricity demand data we use, we are able to present a mathematical model that generates highly timely estimates on a weekly basis.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"6 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":"126654084","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