2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)最新文献

{"title":"Smart Wealth Management System for Robo-Advisory","authors":"Yi Xiang, Zhixi Li, Tsz Ho Lee, Du Tang, Kent Wu, Zhi Bin Lei, Yali Wang","doi":"10.1109/CIFEr.2019.8759063","DOIUrl":"https://doi.org/10.1109/CIFEr.2019.8759063","url":null,"abstract":"The Smart Wealth Management Platform (SWMP) is an intelligent cloud-based system that aims to provide a unified wealth management service with smart risk-assessment and diversified products for institutional and retail investors in China and Hong Kong. An efficient computational architecture supports high performance data processing for financial analysis, huge amount of calculation for portfolio optimization and simulation, and know your customer (KYC) module based on behavioral finance. Leading market core technologies from big data, AI, financial engineering, and financial real time data analysis are cohesively integrated to form a dynamic platform with excellent user experience to support rationalized investment and deliver specialized consultancy.","PeriodicalId":368382,"journal":{"name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114314776","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}

{"title":"On Stock Market Movement Prediction Via Stacking Ensemble Learning Method","authors":"S. Gyamerah, P. Ngare, Dennis Ikpe","doi":"10.1109/CIFEr.2019.8759062","DOIUrl":"https://doi.org/10.1109/CIFEr.2019.8759062","url":null,"abstract":"The capital market act as an intermediary between capital providers (investors) and capital seekers. Investors are able to distribute their money to demand points through this market. For a smooth running of the market, the market have to be efficient and liquid. This implies that investors can buy or sell a share of a stock at a reasonably fair price. The decision to buy or sell a share of the stock is a major investment decision to be made by investors/market players in the capital market. In this paper, different machine learning techniques are used to predict the movement of stocks on the stock market. Stocks are classified into labels according to their current and day-ahead closing price. That is, if the day-ahead closing price is greater than or equal to the current closing price, the investor/market player sells the shares of the stock, else the investor/market player buys additional shares of the stock. Four features (the difference between the price low and price high, the difference between the closing and opening price, the market capitalization, and the volume traded) are used to predict the labels. Two machine learning classifiers (Adaptive Boosting and K-Nearest Neighbour) and Stacking ensemble classifier are trained and used for the classification problem. Using dataset obtained from the Nairobi Stock Exchange, the robustness and effectiveness of the methods on the testing datasets are validated. The results shows that: 1) the Stacking Ensemble Learning Method with two base learners (Adaptive Boosting and K-Nearest Neighbour) and Gradient Boosting Machine as the meta-classifier outperforms the two individual classifiers with an accuracy of 0.7810, area under curve of 0.8238, a kappa of 0.5516, and an out of bag error (OOB) rate of 21.89%, 2) the Volume of shares traded on a specific day does not have much importance when buying or selling shares on the Nairobi stock exchange capital market, and 3) machine learning classifiers can be applied to the stock market for optimal investment decisions. Pan African University, Institute for Basic Sciences, Technology, and Innovation, African Union","PeriodicalId":368382,"journal":{"name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128433156","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}

{"title":"Risk-Managed Strategy Index","authors":"Michael Zhang, Shen-Shin Lu, Yu Lu, Jiao Chen","doi":"10.1109/CIFEr.2019.8759126","DOIUrl":"https://doi.org/10.1109/CIFEr.2019.8759126","url":null,"abstract":"In this index investing research, we studied the relationship between general market index and its historic volatility. As a result, we constructed a new type of risk managed strategy index for the index investing method. The construction method of the new strategy index is a position management method. Based on the original index, the method will increase the index portfolio size when the index volatility is low, and to reduce the index portfolio size when the index volatility is high. Comparing to the original index, the new strategy index has the property of lower volatility and higher risk-adjusted return. We also used relative volatility segmented position management method and targeted volatility position management method to analyze the risk-managed strategy index in different markets, including equity market, debt market, commodity market and FX market with focus on Chinese market. From the selected markets, we found the risk-managed strategy index can improve the risk-adjusted return in the equity market, while its performance is not so significant in other markets. We further explained the reason why the risk-managed strategy index can become more efficient in equity market.","PeriodicalId":368382,"journal":{"name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120921830","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}

{"title":"Japanese long-term interest rate forecast considering the connection between the Japanese and US yield curve","authors":"Yoshiyuki Suimon, Hiroki Sakaji, T. Shimada, K. Izumi, Hiroyasu Matsushima","doi":"10.1109/CIFEr.2019.8759107","DOIUrl":"https://doi.org/10.1109/CIFEr.2019.8759107","url":null,"abstract":"In recent years., overseas financial system crises (e.g., Lehman shock and European debt crisis) exerted major influence on the Japanese interest rates market through global financial transactions., such as interest rates derivative contracts and many types of interest rates arbitrage strategies. In this research., we examined the effect of overseas interest rates movements to the Japanese rates market. Then., we developed a forecasting model of Japanese yield curve based on a variety of machine learning methods., by considering the information obtained from overseas markets. Finally., we confirmed that the prediction accuracy of Japanese long-term interest rate improved by using the US interest rates data in addition to the Japanese interest rates data for machine learning. Furthermore., we confirmed that the prediction accuracy increased by using US and Japanese rates markets data in recent years., particularly after 2006. This result suggests that information of overseas interest rates can be used to forecast Japanese rates market nowadays.","PeriodicalId":368382,"journal":{"name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123332166","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}