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.
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The theme of “polarization” is a tool by which historians can better understand contemporary American history. It is both a consequence of historical events and a contributing causal factor to the 1960s era onwards. By examining historical events, narratives and social movements, this essay argues that polarization is a part of the American experience, identity and development as a nation and culture. In exploring the multifaceted nature of the concept of polarization as a historical concept and as a lens by which to view contemporary American history, we will gain a deeper understanding of the intricacies of what it means to be America, who is American, what is American about identity and who defines it.
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:
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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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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
The Modern portfolio theory {MPT}, "proposes how rational investors will use diversification to optimize their portfolios, and how an asset should be priced given its risk relative to the market as a whole. The basic concepts of the theory are the efficient frontier, Capital Asset Pricing Model and beta coefficient, the Capital Market Line and the Securities Market Line. MPT models the return of an asset as a random variable and a portfolio as a weighted combination of assets; the return of a portfolio is thus also a random variable and consequently has an expected value and a variance.