Abstract
Conventional database systems lack temporal, object and rule support to model financial database applications. In [CS93a], we described the complexity of financial applications and studied the database requirements of such applications. We argued that next-generation databases are an appropriate platform for developing database applications. In this paper we build upon this research by studying strategies to model entities commonly encountered in financial applications. Specifically, the financial entities discussed in this paper are financial instruments and portfolios. Positions in financial instruments and the trading strategies that give meaning to these positions are also modeled. The paper proposes class definitions to model the structural and dynamic properties of financial entities and the interactions between them. These class definitions describe a generic set of attributes and operators for the financial entities discussed. Examples from the financial domain are used to illustrate the modeling constructs and class definitions proposed.
Part of the research by this author was done while at the University of California at Berkeley and Lawrence Berkeley Laboratory
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. Agrawal and N. H. Gehani. ODE (Object Database and Environment): The Language and Data Model. In Proceedings of ACM SIGMOD International Conference on the Management of Data, pages 36–45, May 1990.
R. Abbott and Hector Garcia-Molina. Scheduling Real-time Transactions. ACM SIGMOD Record, 17(1):71–81, March 1988.
R. J. Bauer and G.E. Liepins. Genetic Algorithms and Computerized Trading Strategies. In D.E. O'Leary and P.R. Watkins, editors, Expert Systems in Finance, pages 89–100. Elsevier Publishers, 1992.
M (ed.) Carey. Special Issue on Extensible Database Systems. Database Engineering, June 1987.
M. Carey. The Architecture of the EXODUS Extensible DBMS. In Stonebraker M., editor, Readings in Database Systems, pages 488–501. Morgan Kaufman, 1990.
J. Clifford and A. Croker. The historical relational data model HRDM and an algebra based on lifespans. In Proceedings of the Third International Conference on Data Engineering, pages 528–537, February 1987.
R. Chandra. Managing Temporal Financial Data in Extensible Databases. Technical report, University of California, 1994.
R. Chandra and A. Segev. Managing Temporal Financial Data in an Extensible Database. In Proceedings of the 19th Int. Conf. on Very Large Databases, Dublin, Ireland, August 1993.
R. Chandra and A. Segev. Performance Optimization of Financial Database Applications. In Proceedings of the 3rd Workshop on Information Technologies and Systems, December 1993.
R. Chandra, A. Segev, and M. Stonebraker. Implementing Calendars and Temporal Rules in Next-Generation Databases. In Proceedings of the 10th Int. Conf. on Data Engineering, February 1994.
S.K. Gadia. The Role of Temporal Elements in a Temporal Database. Database Engineering, 7(2):197–203, 1988.
C.S. Jenson, J. Clifford, S.K. Gadia, A. Segev, and R.T. Snodgrass. A Glossary of Temporal Database Concepts. ACM SIGMOD Record, 21(3):35–43, 1992.
B. Lindsay. A Data Management Extension Architecture. In Proceedings of ACM SIGMOD International Conference on the Management of Data, May 1987.
G. Mankiw. Macroeconomics. Worth Publishers, New York, 1992.
P. Peinl and H. Sammer. High Contention in a Stock Trading Database: A Case Study. In Proceedings of ACM SIGMOD International Conference on the Management of Data, pages 260–268, May 1988.
S. Rozen and D Shasha. Using a Relational Database on Wall Street, The Good, the Bad and the Ugly. Communications of the ACM, 1989.
H. Sammer. Online stock Trading Systems: Study of an application. In Proceedings of Spring COMPCON 87 San Francisco, pages 161–163, 1987.
A. Segev and R. Chandra. A Data Model for Time-Series Analysis. In N. Adam and B. Bhargava, editors, Advanced Database Systems. Lectures Notes in Computer Science Series, Springer Verlag, Nov 1993.
R. Snodgrass. The Temporal Query Language TQuel. ACM TODS, 12(2), 1987.
A. Segev and A. Shoshani. A Logical Modeling of Temporal Databases. In Proceedings of ACM SIGMOD International Conference on the Management of Data, May 1987.
A. Segev and A. Shoshani. The Representation of a Temporal Data Model in the Relational Environment. In M. Rafanelli, J.C. Klensin, and P. Svensson, editors, Lecture Notes in Computer Science No. 339, pages 39–61. Springer-Verlag, 1988.
M.R. Stonebraker. The Implementation of POSTGRES. IEEE Transactions on Knowledge and Data Engineering, 2(1):125–142, March 1990.
B. Stroustrup. The C++ Programming Language. Addison-Wesley Publishing Company, 1985.
J. Voelcker and et. al. How computers helped stampede the stock market. SPECTRUM, Oct 1988.
G.T.J Wuu and U. Dayal. A Uniform Model for Temporal Object-Oriented Databases. In Proceedings of the 8th Int. Conf. on Data Engineering, pages 584–593, February 1992.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chandra, R., Segev, A. (1994). Using next generation databases to develop financial applications. In: Litwin, W., Risch, T. (eds) Applications of Databases. ADB 1994. Lecture Notes in Computer Science, vol 819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58183-9_49
Download citation
DOI: https://doi.org/10.1007/3-540-58183-9_49
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58183-3
Online ISBN: 978-3-540-48473-8
eBook Packages: Springer Book Archive