Neural Networks in Investments
I. ABSTRACT
Investment managers often find themselves overwhelmed with the large amount of data obtained from the financial markets. Most of the data available is numeric and noisy in nature, making the decision-making process harder. These decisions usually rely on the integration of statistical measures that attempt to compress much of the data and qualitative depictions such as graphs and bar charts with news events and other pertinent information. Investment decisions usually involve non-linear relationships among the various components of the data. Computers in general, are very adept at dealing with large amounts of numeric information. However, some algorithms are crucial in analyzing and combining disparate information that can impact security prices. Artificial Intelligence based methods uses clever algorithms and rules of thumb (heuristics) in the decision-making process. Neural Network and expert systems applications have been successfully deployed in the domain of Finance, and in the area of investment management.
This paper discusses the basics and the theory behind neural networks and provides an introduction to an application area of neural networks in the domain of Finance. The application areas of Neural Networks discussed in the paper are corporate finance, financial institutions, and the professional investor. The purpose of the second paper will be to discuss the specifics of each of these applications.
II. INTRODUCTION
Neural network computing is an information processing method that was developed from research to make computers that could imitate the way people learned. The field initially grew from 1930s ideas about how biological systems like the human brain works...
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...in of finance is essential for further development.
V. REFERENCES
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This assignment is concerned with your understanding of the key issues relative to portfolio analysis and investment. In completing this assignment you are to limit your scope to the US stock markets only. Use the Cybrary, the Internet, and course resources to write a 2-page essay which you will use with new clients of your financial planning business which addresses the following issues and/or practices:
Michie, Donald and Johnston, Rory. The Knowledge Machine. Artificial Intelligence and the Future of Man. William Morrow and Company, Inc., NY., 1985.
Howells, Peter., Bain, Keith 2000, Financial Markets and Institutions, 3rd edn, Henry King Ltd., Great Britain.
Crevier, D. (1999). AI: The tumultuous history of the search for Artificial Intelligence. Basic Books: New York.
An important field in computer science today is artificial intelligence. The novel approaches that computer scientists use in this field are looked to for answers to many of the problems that have not been solved through traditional approaches to software engineering thus far. One of the concepts studied and implemented for a variety of tasks in artificial intelligence today is neural networks; they have proven successful in offering an approach to some problems in the field, but they also have some failings.
Since the 1980's there have been renewed research efforts dedicated to neural networks. The present interest is largely due to the difficult problems confronted by artificial intelligence, and due to the deeper understanding of how the brain works, the recent developments in theoretical models, technologies and algorithms. One motivation of neural network research is the desire to build a new breed of powerful computers to solve a variety of problems that have proved to be very difficult with conventional computers. Another motivation is the desire to develop cognitive models that can serve as an alternative way to artificial intelligence. Human brain functions have not yet been successfully simulated in an AI system. Some existing neural network, on the other hand, have shown potential for these abilities. Using self-organization capabilities, neural networks are able to acquire and organize knowledge through learning in response to external stimuli. This paper addresses many techniques used in neural networks and possible applications in artificial intelligence. Some generic information about hybrid intelligent systems is also provided.
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This paper will provide an introductory level discussion of neural networks within the field of artificial intelligence. This discussion will briefly cover the history of the neural network as well as recent advances within this field. In addition, several real world applications of neural networks will be discussed.
Johnson, G., Scholes, K., Johnson, G. and Whittington, R. 2011. Exploring strategy. Harlow: Financial Times Prentice Hall.
“Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent.”AI has improved a lot in the last century, and is still advancing today. Different approaches in AI are used for different situations. Whether we notice it or not, AI is an important part of our lives. Artificial Intelligence
Decision support systems (DSS) and expert systems (ES) play critical role in solving various financial and business problems, where data processing for deriving new information, yielding possible solutions or their alternatives is a significant part of relevant computations. Section 3.1 gives a brief introduction to DSS and ES, discusses their goals and main differences from standard information systems (IS). Section 3.2 reviews main types and taxonomies of DSS, while relating them to financial risk oriented problems. Section 3.3 discusses recent developments in DSS for financial problems, related to credit risk, while Section 3.4 enlists a number of requirements for modern DSS dedicated to banking decisions. Further, we discuss the development of novel DSS based on AI techniques, described in
This paper will define and discuss five financial theories and how they impact business decisions made by financial managers. The theories will be the Modern Portfolio Theory, Tobin Separation Theorem, Equilibrium Theory, Arbitrage Pricing Theory (APT), and the Efficient Markets Hypothesis.
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