Fuzzy Logic-Based Data Analytics on Predicting the Effect of Hurricanes on the Stock Market

In the current era of big data, high volumes of a wide variety of valuable data of different veracity are generated or collected at a high velocity. A rich source of these big data is the stock market. Since the inception of the stock market, people have been trying to "beat" it for the purpose of monetary gain. A stock market is an exchange where people trade shares of companies, also called stocks. The purpose of the exchange is to make it easy to match buyers and sellers together to make transactions. The usual goal of someone participating in the stock market it to generate profit through the buying and selling of stocks. The main way people accomplish this is by buying a stock, waiting anywhere from seconds to decades, and then hopefully selling for more than they bought it for. This is where the common term "buy low, sell high" comes from. There are many factors (e.g., hurricanes) that may affect the stock price. In this paper, we present a computational intelligent tool that applies fuzzy logic-based data analytics to predict the effect of hurricanes on the stock market.

[1]  Peter Braun,et al.  Knowledge Discovery from Big Social Key-Value Data , 2016, 2016 IEEE International Conference on Computer and Information Technology (CIT).

[2]  Ayman E. Khedr,et al.  Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis , 2017 .

[3]  Tak-Chung Fu,et al.  Mining of Stock Data: Intra- and Inter-Stock Pattern Associative Classification , 2006, DMIN.

[4]  Sungshin Kim,et al.  Automatic structure identification of TSK fuzzy model for stock index forecasting , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[5]  Hahn-Ming Lee,et al.  Stock Trend Prediction by Sequential Chart Pattern via K-Means and AprioriAll Algorithm , 2012, 2012 Conference on Technologies and Applications of Artificial Intelligence.

[6]  Alfredo Cuzzocrea,et al.  A Machine Learning Tool for Supporting Advanced Knowledge Discovery from Chess Game Data , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[7]  Yang Dong.  MINING SEQUENTIAL PATTERNS IN WEB LOGS , 2000 .

[8]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[9]  Carson Kai-Sang Leung,et al.  Mathematical Model for Propagation of Influence in a Social Network , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[10]  Ken Tse,et al.  Stocks market prediction using Support Vector Machine , 2013, 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering.

[11]  Massimo Panella,et al.  A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[12]  Guoqing Chen,et al.  Finding temporal associative patterns of Web search with stock price movements for trading strategy design , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[13]  Giancarlo Fortino,et al.  Managing Data and Processes in Cloud-Enabled Large-Scale Sensor Networks: State-of-the-Art and Future Research Directions , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[14]  Robert Ivor John,et al.  A new dynamic approach for non-singleton fuzzification in noisy time-series prediction , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[15]  Mario Cannataro,et al.  A Probabilistic Approach to Model Adaptive Hypermedia Systems , 2001, WebDyn@ICDT.

[16]  Carson Kai-Sang Leung,et al.  Stock Price Prediction in Undirected Graphs Using a Structural Support Vector Machine , 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).

[17]  Carson Kai-Sang Leung,et al.  Big Data Analysis and Mining , 2019, Advances in Computer and Electrical Engineering.

[18]  David Enke,et al.  The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..

[19]  Carson K. Leung,et al.  Interactive Mining of Strong Friends from Social Networks and Its Applications in E-Commerce , 2014, J. Organ. Comput. Electron. Commer..

[20]  Robert Ivor John,et al.  Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[21]  Giangiacomo Gerla,et al.  Fuzzy logic, continuity and effectiveness , 2002, Arch. Math. Log..

[22]  Yang Wang,et al.  A machine learning approach for stock price prediction , 2014, IDEAS.

[23]  Carson Kai-Sang Leung,et al.  A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments , 2015, Algorithms.

[24]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[25]  Joseph Y. Halpern.  Reasoning about uncertainty , 2003 .