14-09-2013, 04:49 PM
An Artificial Neural Networks Primer with Financial Applications Examples in Financial Distress Predictions and Foreign Exchange Hybrid Trading System
An Artificial Neural Networks Primer.pdf (Size: 442 KB / Downloads: 44)
Introduction
There can be little doubt that the greatest challenge facing managers and researchers in
the field of finance is the presence of uncertainty. Indeed risk, which arises from
uncertainty, is fundamental to modern finance theory and, since its emergence as a
separate discipline, much of the intellectual resources of the field have been devoted
to risk analysis. The presence of risk, however, not only complicates decision
financial making, it creates opportunities for reward for those who can analyze and
manage risk effectively.
By and large, the evolution of commercial risk management technology has been
characterized by computer technology lagging behind the theoretical advances of the
field. As computers have become more powerful, they have permitted better testing
and application of financial concepts. Large-scale implementation of Markowitz’s
seminal ideas on portfolio management, for example, was held up for almost twenty
years until sufficient computational speed and capacity were developed. Similarly,
despite the overwhelming need from a conceptual viewpoint, daily marking to market
of investment portfolios has only become a feature of professional funds management
in the past decade or so, following advances in computer hardware and software.
Artificial Intelligence
AI has been described as software that behaves in some limited ways like a human
being. The word artificial comes from the Latin root word facere arte which means
“make something” thus AI translates loosely to man made intelligence. AI has been
defined in many ways. Winston [1984] suggests one definition of AI as the study of
ideas that enable computers to be intelligent. Rich and Knight [1991] define AI as the
study of how to make computers do things which, at the moment, people do better.
Artificial Intelligence in Finance
Expert System
Financial analysis falls into the Expert Task Domain of AI as classified by Rich and
Knight [1991]. Thus, it is not surprising that the most used AI methods in the financial
field have been expert systems. An expert system is a program that is developed by a
programmer, known as a knowledge engineer, who may have no domain knowledge
of the task at hand with the help of a domain ‘expert’ who may not have any
programming expertise. The system is developed by trying to capture the human
expert’s knowledge into a set of programming rules that assist in decision making.
Hence expert systems are often described as rule-based systems. Expert Systems have
been used in medical diagnosis problems, fraud detection, prospecting and mineral
detection, etc. The biggest limitation of expert systems is that they require full
information about outcomes and therefore deal poorly with uncertainty.
Artificial Neural Networks in Finance
From the range of AI techniques, the one that deals best with uncertainty is the
Artificial Neural Network (ANN). Dealing with uncertainty in finance primarily
involves recognition of patterns in data and using these patterns to predict future
events. Accurate prediction of economic events, such as interest rate changes and
currency movements currently ranks as one of the most difficult exercises in finance;
it also ranks as one of the most critical for financial survival. ANNs handle these
problems better than other AI techniques because they deal well with large noisy data
sets. Unlike expert systems, however, ANNs are not transparent, thus making them
difficult to interpret.
Artificial Neural Networks
Artificial Neural Network (ANN) models were inspired by the biological sciences
which study how the neuroanatomy of living animals have developed in solving
problems. According to Nelson and Illingworth [1990], ANNs are also called:
• Parallel distributed processing models
• Connectivist/connectionism models
• Adaptive systems
• Self-organizing systems
• Neurocomputing
• Neuromorphic systems
ANNs consist of many interconnected processors known as neurons1 that perform
summing function. Information is stored in the weights on the connections. More
detailed discussion on the technical aspects of ANNs is given in Chapter 2 and 3.
An ANN mimics the human brain’s biological neural network. The biological neural
network is the mechanism through which a living organism’s nervous system
functions, enabling complex tasks to be performed instinctively. The central
processing unit of that nervous system is known as a "neuron". The human brain has
around 10 to 100 billion neurons, each connected to many others by "synapses". The
human brain has around 100 trillion synapses. These connections control the human
body and its thought processes. In short, they attempt to replicate the learning
processes of the human brain. The first ANN theories were expounded by researchers
attempting to explain human behavior and the thinking process by modeling the
human brain. To this day, many of the prominent researchers in the ANN field consist
of researchers with background in psychology.