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Thenmozhi, M.
- Stock Index Return Forecasting and Trading Strategy Using Hybrid ARIMA-Neural Network Model
Abstract Views :404 |
PDF Views:0
Authors
Manish Kumar
1,
M. Thenmozhi
2
Affiliations
1 Research Scholar, Department of Management Studies, Indian Institute of Technology, Madras, Chennai, IN
2 Management Studies, Indian Institute of Technology, Madras, Chennai, IN
1 Research Scholar, Department of Management Studies, Indian Institute of Technology, Madras, Chennai, IN
2 Management Studies, Indian Institute of Technology, Madras, Chennai, IN
Source
International Journal of Financial Management, Vol 2, No 1 (2012), Pagination: 1-14Abstract
This study presents the application and development of hybrid methodology that combines both ARIMA and Artificial Neural Network model to take advantage of the unique strengths of both linear and non-linear modeling to model and predict the stock market index returns. The performance of the hybrid ARIMA Neural Network model is compared with the performance of ARIMA and Neural Network model. The performance of the models are evaluated in terms of widely used statistical metrics, correctness of sign and direction change and various trading performance measures like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio, etc. via a trading strategy. The findings of the study reveal that the hybrid ARIMA Neural Network model developed is the best Forecasting model to achieve greater accuracy and yields better trading results.Keywords
ARIMA, Artificial Neural Network, Forecasting, Stock Market Trading
References
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- An Analysis on the Performance of Tree and Trie Based Dictionary Implementations with Different Data Usage Models
Abstract Views :215 |
PDF Views:0
Authors
M. Thenmozhi
1,
H. Srimathi
2
Affiliations
1 Department of Information Technology, SRM University, Chennai, IN
2 Department of Computer Applications, SRM University, Chennai, IN
1 Department of Information Technology, SRM University, Chennai, IN
2 Department of Computer Applications, SRM University, Chennai, IN
Source
Indian Journal of Science and Technology, Vol 8, No 4 (2015), Pagination: 364-375Abstract
Tree and Trie are the Abstract Data Types (ADTs) that provide efficient implementation of ordered dictionary. The performance of a data structure will depend on hardware capabilities of the computing devices such as RAM size, Cache memory size and even the speed of the physical storage media. Hence, an application which will be running on real or virtualized hardware environment certainly will have restricted access to memory and other resources of the real hardware. Further, the time taken for any operation on a data structure rely on the data usage model and the most significant operations/tests are very much depend on the size of the "character payload objects" which we use in dictionary like implementations. In this work, we do an analysis on the performance of Tree and Trie based Dictionary ADT Implementations with different data usage models. We consider data usage models such as a typical electronic dictionary with more than million of words or a typical electronic encyclopedia with large string data elements. In this work, we studied the performance of three popular Tree based Dictionary Implementations rbtree, googlebtree, stxbtree, and three Trie based Dictionary Implementations tommy-trie, tommy-trie-inplace, nedtrie under different hardware and software configurations. Among all, tommy trie is proved to be the best for character payload objects with 16 bytes and 4096 bytes. In some operations/tests googlebtree seems to be better. Our evaluation on different tree and trie structures shows tommy trie implementations perform well irrespective of size of application.Keywords
Cache, Googlebtree, Rbtree, Stx Btree, Tommy Trie, Trie.Full Text
- An Ontology Mapping Maintenance Approach Using Change History Log in Ontology Based Data Integration
Abstract Views :197 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, IN
2 Department of Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, IN
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, IN
2 Department of Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 7 (2013), Pagination: 292-300Abstract
Data integration systems aim at integrating data from multiple heterogeneous, distributed and autonomous systems to provide a uniform access interface to end users. Today, ontologies are finding their way into a wide variety of applications including data integration. When using various ontologies to integrate data, mappings have to be produced. Mapping between two ontologies is used to achieve interoperability and to share information in an efficient manner. The requirement of updating the recent advances in a particular ontology leads to ontology evolution. Due to ontology evolution, the existing mapping between the ontologies become unreliable, invalid and outdated. In this paper we propose an ontology mapping maintenance approach using Change History Log (CHL). Our approach computes matching between the changed entities and adapts the existing mappings accordingly. It also ensures that the application of ontology changes should result in a mapping document conforming to the set of ontology mapping consistency constraints. The proposed approach can reduce the time needed for regenerating mappings for each time changes occurring in the ontology.Keywords
Change History Log, Knowledge Sharing, Mapping Technique, Ontology Evolution, Ontology Mapping.- Do Healthcare Professionals Cross the Border to Serve Rural Population?
Abstract Views :137 |
PDF Views:0
Authors
Affiliations
1 Business School, Vellore Institute of Technology University, Vellore, IN
2 Department of Management Studies, Indian Institute of Technology Madras, IN
1 Business School, Vellore Institute of Technology University, Vellore, IN
2 Department of Management Studies, Indian Institute of Technology Madras, IN