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

An Empirical Comparison of Cross-Validation Procedures for Portfolio Selection 投资组合选择交叉验证程序的实证比较

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776132

A. Paskaramoorthy, Terence L van Zyl, T. Gebbie

引用次数: 0

Optimizing Mixed-Asset Portfolios Involving REITs 优化包含REITs的混合资产组合

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776074

Fatim Z. Habbab, Michael Kampouridis, Alexandros A. Voudouris

{"title":"Optimizing Mixed-Asset Portfolios Involving REITs","authors":"Fatim Z. Habbab, Michael Kampouridis, Alexandros A. Voudouris","doi":"10.1109/CIFEr52523.2022.9776074","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776074","url":null,"abstract":"Real Estate Investment Trusts (REITs) is a popular investment choice as it allows investors to hold shares in real estate rather than investing large sums of money to purchase real estate by themselves. Previous work studied the effectiveness of multi-asset portfolios that include REITs via an efficient frontier analysis. However, the advantages of including (both domestic and international) REITs in multi-asset portfolios, as well as analyzing all the possible combinations of asset classes, has not been investigated before. In this paper, we fill in this gap by performing a thorough investigation across 456 different portfolios to demonstrate the added value of including REITs in mixed-asset portfolios in terms of different important financial metrics. To this end, we use a genetic algorithm approach to maximize the Sharpe ratio of the portfolios. Our results show that optimization via a genetic algorithm outperforms the results obtained from a global minimum variance portfolio. More importantly, our results also show that there can be significant improvements in average returns, risk and Sharpe ratio when including REITs.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"21 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":"125947917","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: 8

Intelligent Probabilistic Forecasts of VIX and its Volatility using Machine Learning Methods 基于机器学习方法的VIX及其波动率的智能概率预测

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776069

A. Thavaneswaran, You Liang, Sanjiv Ranjan Das, R. Thulasiram, Janakumar Bhanushali

{"title":"Intelligent Probabilistic Forecasts of VIX and its Volatility using Machine Learning Methods","authors":"A. Thavaneswaran, You Liang, Sanjiv Ranjan Das, R. Thulasiram, Janakumar Bhanushali","doi":"10.1109/CIFEr52523.2022.9776069","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776069","url":null,"abstract":"The market focuses on the Cboe Volatility Index (VIX) or Fear Index, an option-implied forecast of 30 calendar-day realized volatility of S&P 500 returns derived from a cross-section of vanilla options. The VIX is determined using a formula that derives the market’s expectation of realized one-month standard deviation of returns backed out from the near-term call and put options on the S&P 500 index. Market participants such as traders, asset managers, and risk managers, keenly watch the VIX index, and are interested in achieving accurate intelligent probabilistic forecasts of the VIX, and also of the realized volatility of individual stocks. These volatility forecasts are useful to options traders placing bets on the future volatility of individual stocks. This paper examines models that only utilize past values of the VIX and document improvements in forecasting the VIX (and its volatility) over different horizons. The approaches include long short-term memory (LSTM) models, simple moving average methods, data-driven neuro volatility techniques, and industry models like Prophet. Uniquely, we propose a novel VIX price interval forecasting model. The driving idea, unlike the existing VIX price forecasting models, is that the proposed novel LSTM interval forecasting method trains two LSTMs to obtain price forecasts and the forecast error volatility forecasts. All the proposed forecasting methods also avoid model identification and estimation issues, especially for a series like the VIX which is non-stationary. We compare models and document which ones perform best for varied horizons.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"23 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":"116444496","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

Predicting Financial Volatility from Personal Transactional Data 从个人交易数据预测金融波动

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776206

Rui Ying Goh, G. Andreeva, Yi Cao

{"title":"Predicting Financial Volatility from Personal Transactional Data","authors":"Rui Ying Goh, G. Andreeva, Yi Cao","doi":"10.1109/CIFEr52523.2022.9776206","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776206","url":null,"abstract":"Cash flow transactions of individuals fluctuate over time and can be irregular. Financial volatility measures the variation of individuals’ financial behaviours i.e., the degree of uncertainty from the cash flow fluctuations. The evaluation of financial volatility is important in order to identify potentially risky behaviours that may harm financial wellbeing. This study predicts financial volatility from transactional data coming from current accounts. In this work, we develop a financial volatility composite index as the target variable, which simultaneously accounts for the fluctuations in income, expenditure, and financial buffer (or balance). Then, we fit a linear regression model to investigate the relationship between transactional behaviours and financial volatility. Lastly, we compare the performance of linear regression with XGBoost, a machine learning algorithm, in predicting financial volatility. We observe some risky volatile behaviours that imply financial difficulties. High financial volatility signals an increased risk, if it is associated with potential financial struggles that require long term dependence on overdraft, lower spending on fixed and living costs, or problems in catching up with regular financial commitments. At the same time, low financial volatility may be implying an increased risk too, if it is associated with restricted transactions due to extreme negative balances or consistent heavy overdraft usage. In general, the proposed financial volatility predictive model provides insights into the implicit risk of customers and their vulnerability.","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":"127949888","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

Impact of False Information from Spoofing Strategies: An ABM Model of Market Dynamics 欺骗策略对虚假信息的影响:市场动态的ABM模型

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776070

HaoHang Li, Steve Y. Yang

{"title":"Impact of False Information from Spoofing Strategies: An ABM Model of Market Dynamics","authors":"HaoHang Li, Steve Y. Yang","doi":"10.1109/CIFEr52523.2022.9776070","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776070","url":null,"abstract":"Spoofing has been identified a form of market manipulation, and it is harmful to the stability of the financial market. However, the effect of spoofing activity is hard to analyze due to its complex interactions within the market and lack of data. This paper presents an agent-based simulation model of the continuous double auction market to replicate and analyze the market dynamics under spoofing conditions. The simulated market consists of fundamentalist, chartist, zero intelligence agents, and spoofing agents where several existing market stylized facts are validated. The results show that in the presence of the spoofing agents and their market manipulation activities, the market volatility would increase, and spoofing activities would exacerbate the price variations. The fundamentalist agents would suffer a loss during the spoofing period but would be able to make profit during the price recovery phase. The chartist agents would suffer a loss when the spoofing agent realized its profit and the price recovery process start, at which they falsely believed the price movement trend would continue. The Sharpe ratio analysis also indicates the market manipulation activities of the spoofing agent would give themselves an unfair advantage resulting in a significantly higher Sharpe ratio than the other agents.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"125 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":"125042204","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

Probabilistic Inference of South African Equity Option Prices Under Jump-Diffusion Processes 跳跃-扩散过程下南非股票期权价格的概率推断

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776189

W. Mongwe, T. Sidogi, R. Mbuvha, T. Marwala

{"title":"Probabilistic Inference of South African Equity Option Prices Under Jump-Diffusion Processes","authors":"W. Mongwe, T. Sidogi, R. Mbuvha, T. Marwala","doi":"10.1109/CIFEr52523.2022.9776189","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776189","url":null,"abstract":"Jump-diffusion processes have been utilised to capture the leptokurtic nature of asset returns and to fit the market observed option volatility skew with great success. These models can be calibrated to historical share price data or forward-looking option market data. In this work, we infer South African equity option prices using the Bayesian inference framework. This approach allows one to attain uncertainties in the parameters of the calibrated models and confidence intervals with any predictions produced with the models. We calibrate the one-dimensional Merton jump-diffusion model to European put and call option data on the All-Share price index using Markov Chain Monte Carlo methods: the Metropolis Adjusted Langevin Algorithm, Hamiltonian Monte Carlo, and the No-U-Turn Sampler. Our approach produces a distribution of the jump-diffusion model parameters, which can be used to build economic scenario generators and price exotic options such as those embedded in life insurance contracts. The empirical results show that our approach can, on test data, exactly price all put option prices regardless of their moneyness, with slight miss-pricing on very deep in the money calls.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"64 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":"131622621","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

Applying Sentiment Analysis, Topic Modeling, and XGBoost to Classify Implied Volatility 应用情感分析、主题建模和XGBoost对隐含波动率进行分类

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776196

Farshid Balaneji, D. Maringer

{"title":"Applying Sentiment Analysis, Topic Modeling, and XGBoost to Classify Implied Volatility","authors":"Farshid Balaneji, D. Maringer","doi":"10.1109/CIFEr52523.2022.9776196","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776196","url":null,"abstract":"Implied volatility is an important indicator that shows the market participants’ expectations about the future fluctuations in the options market. This paper evaluates the question of whether the combination of topics and sentiment scores extracted from mainstream financial news could improve forecasting the directional changes of the expected implied volatility index in the next month (iv30call). We select six stocks from the Dow Jones list of companies and acquire over 190,000 news published between January 2019 and September 2019. By building text processing and topic modeling pipelines, we can examine (i) the role of daily mean and medium of sentiment scores; and (ii) the influence of topic models on the classification metrics. The results demonstrate that adding a topic model has a positive effect on the model’s accuracy, which reaches higher accuracy in classifying the iv30call of the next business day in five out of six companies. The outcome suggests that applying the mean of the daily sentiment scores improves the models’ accuracy compared to the daily median for the selected assets.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"7 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":"114352592","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

A deep learning-based high-order operator splitting method for high-dimensional nonlinear parabolic PDEs via Malliavin calculus: application to CVA computation 基于Malliavin演算的高维非线性抛物型偏微分方程的深度学习高阶算子分裂方法:在CVA计算中的应用

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776096

Riu Naito, Toshihiro Yamada

引用次数: 0

Organization Committee 组织委员会

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/cifer52523.2022.9776209

引用次数: 0

Long-Term Energy Consumption Forecast for a Commercial Virtual Power Plant Using a Hybrid K-means and Linear Regression Algorithm 基于k均值和线性回归混合算法的商业虚拟电厂长期能耗预测

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) Pub Date : 2022-05-01 DOI: 10.1109/CIFEr52523.2022.9776211

Isla Almeida Oliveira, Pâmela Rugoni Belin, C. Santos, M. A. Ludwig, J. R. H. Rodrigues, C. Pica

{"title":"Long-Term Energy Consumption Forecast for a Commercial Virtual Power Plant Using a Hybrid K-means and Linear Regression Algorithm","authors":"Isla Almeida Oliveira, Pâmela Rugoni Belin, C. Santos, M. A. Ludwig, J. R. H. Rodrigues, C. Pica","doi":"10.1109/CIFEr52523.2022.9776211","DOIUrl":"https://doi.org/10.1109/CIFEr52523.2022.9776211","url":null,"abstract":"With regard to the development of a commercial Virtual Power Plant (VPP) – whose objective is to aggregate consumer and generator units that receive contractual benefits through a joint operation –, arises the necessity to implement a long-term energy consumption forecast algorithm, with the competence to provide inputs for the decision on the purchase or sale of long-term energy contracts. To perform this forecast, a hybrid algorithm with k-means clustering is used to cluster seasonal patterns of daily energy consumption through unsupervised machine learning, also applying regression concepts to identify trends and compose forecasted consumption. The model traces daily consumption profiles throughout the year utilizing measurement data to forecast the monthly energy consumption, which is segmented in peak and off-peak periods, in virtue of additional taxes that are charged for distributors of electricity in high demand hours. The proposed forecast model resulted in elevated accuracy in the aggregated loads context – which is the main objective of the VPP application –, increasing the usefulness of the VPP application as a decision-making tool for retailers, power distribution companies and other purposes involving grouping of electricity consumption.","PeriodicalId":234473,"journal":{"name":"2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)","volume":"6 7 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":"125735731","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