by Jeannette Lawrence 12-20-89
Experts use charts, their pet indicators, and even intuition to navigate through the massive amounts of financial information available. Some study a few companies that appear to be good long-term investments. Some try to predict the future economy or stock market in general, but with the great number of influences involved, this seems at best an Olympian task. Who can absorb years of data for 30 indicators, 500 stocks, the political climate, and other influences, as well as keep track of current values? There is even new scientific evidence that massive systems such as the U.S. economy or the weather are not predictable very far into the future (due to the effects of chaos).
To assist people in making forecasts for particular markets, there are more than 250 computer programs available. Traditionally, these systems are expensive, require complex programming, use surveys of financial experts to define the "game rules", and are still limited in their ability to think like people. Even when a problem is solved, engineering a design change can be a monumental task.
Now neural networks are being used on personal computers to make financial predictions. You can purchase a neural network program that is easy to use and runs on a PC for less than $200.
They can be given subjective information as well as statistics and are not limited to any particular financial theory. They learn from experience instead of following equations or rules. They can be asked to consider hundreds of different influences, more than most people can digest. They won't be overwhelmed by decades of statistics. You can use a neural network in place of, or in addition to, traditional methods.
Using a neural network for advice means you don't have to decipher complex wave forms to find a trend. The network will determine which influences correlate to each other, if there are patterns, filtering out the noise, and picking up overall trends. You can ask the network what the price of a certain mutual fund is likely to be in the near future, if a certain stock is currently a "good buy", or a number of other things. It's up to you. You decide what you want the network to learn and what kind of information it needs to be given in order to arrive at a conclusion.
Neural network programs are a new kind of computing tool which simulate the structure and operation of the human brain. They mimic many of the brain's most powerful abilities, including pattern recognition, association, and the ability to generalize by observing examples. Neural networks create their own model of the problem through a training process, so no programming is required. A trained network provides answers with lightning speed, in less than a second. You can retrain a network to use new, updated information in minutes.
In this article you'll get a glimpse of how neural networks work and look at some sample neural networks which predict the future corporate bond ratings of companies and which predict the future price of selected mutual funds. Other common uses for neural networks include medical diagnostic systems, insurance claim evaluations, sports event predictions, loan risk evaluations, pattern recognition, and business analysis and decision making.
One of the most puzzling things about people is how they use their brains to think. The brain is composed of hundreds of billions of nerve cells (neurons) which are massively connected to each other. Recently biologists have learned that it is the way the cells are connected which provides us with intelligence, rather than what is in the cells. Neural networks simulate the structure and operation of the brain's neurons and connections.
A new neural network starts out with a "blank mind". The network is taught about a specific problem, such as predicting a stock's price, using a technique called training. Training a neural network is like teaching a small child to recognize the letters of the alphabet. You show him a picture of the letter "A" and ask him what letter he's looking at. If he guesses right, you say so and go on to the next letter. If he doesn't guess right, you tell him that he is looking at an "A". Next, you show him a "B" and repeat the process. You would do this for all the letters of the alphabet, then start over. Eventually he will learn to recognize all of the letters correctly.
A new neural network is shown some data and it guesses what the result should be. At first the guesses are gibberish. When the network is wrong, it is corrected. The next time it sees that data, it will guess more accurately. The network is shown lots of data, over and over until is learns all the data and results. Like a person, a trained neural network can generalize, making a reasonable guess when given data which is different than any it has seen before. You decide what information to provide and the network finds the patterns, trends, and hidden relationships.
Just how does correcting the network cause it to learn? It's all in the connections between the neurons. The connections allow the neurons to communicate with each other and form answers. When the network makes a wrong guess, an adjustment is made to the way neurons are connected, thus it is able to learn. With most commercially available neural network programs (such as BrainMaker, used in the examples below) the network is created and trained by the program itself; all you have to do is provide the data and the expected results for training.
Using a very simple example, here are the steps involved in designing a neural network. The first thing you do is decide what result you want the network to provide for you and what information it will use to arrive at the result. For example, suppose you want to make a network which will predict the price of the Dow Jones Industrial Average (DOW) on a month to month average basis, one month in advance. The information to provide the network might include the Consumer Price Index (CPI), the price of crude oil, the inflation rate, the prime interest rate, the Gross National Product (GNP), and other indicators.
It's best to give the network lots of information. If you are unsure if there is a relationship, provide the data (for example between how the good the weather is over the U.S. and the DOW). The neural network will figure out if the information is important and will learn to ignore anything irrelevant. Sometimes a possibly irrelevant piece of information can allow the network to make distinctions which we are not aware of. If there's no correlation, the network will just ignore the information. Mathematical models aren't this flexible.
If you're unsure about which economic theory to follow, don't worry. Some people are technical analysts (they believe the future is predictable based on history and current trends), some people are fundamentalists (the future is predictable based on principles of the system), and some people are monetartists (stability and growth are determined by supply of money controlled by the FED). There is no reason to limit a neural network to any one of these theories. You can have your inputs include the price of supplies this month, the price last month, and 3 months ago, the consumer price index this month, the price last month, and 3 months ago, the inflation rate this month, the rate last month, and 3 months ago, the DOW this month, the DOW last month, and 3 months ago, the unemployment rate, the political climate, and more. People rarely learn all these things, because it's just too much to keep track of, but neural networks do not get overwhelmed by detail.
A simple DOW predictor network might look like this:
Which month it is
Consumer Price Index for this month
Price of crude oil this month
Dow Jones average this month
DOW this month
Consumer Price Index this month
Price of crude oil last month
Inflation rate last month
last DOW month
last Consumer Price Index month
Price of crude oil 3 months ago
Inflation rate 3 months ago
DOW 3 months ago
Overall U.S. weather 3 months ago
next month's Inflation rate
This is a simple example. A better design would have information from more periods in the past (last year, e.g.) and a greater variety of data. The data is collected for a substantial period of time, say the last 15 years. For the network to learn properly, you need historical data for each month for each kind of data for the last 15 years. Part of you data collection could look like this:
|Mon||CPI||CPI-1||CPI-3||Oil||Oil-1||Oil-3||Dow||Dow-1||Dow-3 etc.||Dow Ave (output)|
Note that these are fictitious values shown for illustration purposes only. In the example above, CPI is a certain month's consumer price index, CPI-1 is the index one month before, CPI-3 is the the index 3 months before, etc.
You can add traditional mathematical methods to neural networks. For example, to a trend-analyzing network you can add information based upon moving averages. Creating moving averages helps build networks that depend on current numbers and past numbers, but ignore extremely short small changes. Assume you want to predict how the price of a stock will move, but in a general sort of way in a bigger time frame. Based on what the average stock price has been from week to week during this month and last, the network can predict what the average stock price is going to be each week for the next month. Some programs automate this task for you.
After you have your data ready (including the output: DOW average for the next month), the program will create and train the new network for you. With some programs, you can watch the training on the display, edit and test the network using pop-up menus, print out the results, graph trends, etc. You can set the level of accuracy that you need from the network. After the network is trained, you can give the network current information and get a prediction of next month's Dow Jones average.
Nicholas Murray Butler (an American educator and author) said, "An expert is one who knows more and more about less and less." A neural network is most expert when it is trained for a particular task, such as the future price of a certain stock or a group of related stocks (such as all U.S. automobile manufacturers). It is very difficult to train a network to predict for many diverse kinds of stocks, since the stocks will react differently to various influences. It would be a massive network that may have trouble learning so many different relationships.
Creating a neural network financial expert can be quite helpful, even for experts. In this section, two working financial applications are described.
G. R. Pugh & Co. of Cranford, New Jersey, does consulting to the Public Utility industry. He maintains databases with financial and business information on the companies, advises with business forecasts and credit risk assessments and predicts the financial and operating health of these companies. Some projections have been as far as 10 years into the future.
His expertise is also used by the brokerage industry. He advises clients on the selection of good corporate bonds. His clients need to know more accurately which bonds represent good investments for their customers. Both increases and decreases provide the potential for profitable investment. G. R. Pugh and Company has been using a BrainMaker neural network trained on three to four years of historical data with an XT-compatible PC to help predict the next year's corporate bond ratings of 115 public utilities companies. "An XT is more than sufficient; it's a FAST program," company president George Pugh notes. Learning to use the program and create a neural network from scratch took only 2 days. The network trained itself in about four hours.
Mr. Pugh announced that his network has been more successful than discriminant analysis methods he has used, and even a little better than a person could do. "Discriminant analysis methods are good for getting the direction of lively issues, but neural networks pick up the subtle interactions much better," he explains. The network categorizes the ratings with 100% accuracy within a broad category and 95% accuracy within a subcategory. The mathematical method of discriminant analysis was only 85% accurate within a broad category. (Bonds are rated much like report cards, with broad category ratings such as A, B, C, etc. A subcateogry could be A+, for example.)
According to Mr. Pugh, "BrainMaker was able to pick up some of the interplays in the inputs that statistical analysis couldn't get." The network makes a significant contribution to his analysis. "The network allows me to pick up things that are not obvious with typical analysis."
Moreover, nearly all of the network's difficulties were found to be associated with companies that were experiencing a particularly unusual problem (such as regulatory risk) or had an atypical business relationship (such as being involved in a large sale and lease-back transaction). Ratings also tend to be subjective; financial items are not the only things considered by the rating companies. These influences were not represented in the training facts and makes predictions difficult.
The trained network forecasts next year's Standard & Poor's and Moody's corporate bond ratings (both are industry standards) from the previous year's S & P and Moody's ratings and 23 other measures of each company's financial strength, such as income, sales, returns on equity, 5-year growth in sales, and measures of investment, construction, and debt load. Each of these factors is assigned to its own input neuron, and each company's ratings for next year are the outputs of the network.
Mr. Pugh advocates using a neural network as a tool that allows you to go beyond discriminant analysis. He believes neural networks are particularly useful when there is a high correlation between data, but the network does not lose accuracy when there is "fuzziness" in the data. "It is also able to pick out the trends, and seems to compute a decision more the way people do." He has plans for several other financial applications in the wings.
Dr. Judith Lipmanson of CHI Associates in Bethesda, Maryland, publishes technical business documents and newsletters for in-house use at technical and advisory firms. She also is a technical analyst who uses a neural network to predict next week's price of 10 selected mutual funds for personal use.
For the past several months, she has been using a BrainMaker neural network on a 386-based IBM-compatible AT. The network gets updated with new data every week, and takes only minutes to retrain from scratch on a 386-based IBM-compatible AT.
Results have been good. Currently, the network is producing outputs which are about 70% accurate. Although the network is not perfectly accurate in its predictions, she has found that the neural network make predictions which are useful.
Dr. Lipmanson's network relies on historically-available numerical data of the kind typically found in back-issues of the Wall Street Journal. These indicators include such factors as the DOW Industrial, DOW Utilities, DOW Transportation and Standard & Poor's 500 weekly averages. Several years worth of data was gathered for the four initial conditions (the inputs) and the ten results (the outputs). The results were shifted by a period of one week and the information was used to train the network.
The DOW Industrial
The Dow Utility
S & P 500
Fund # 1 next week
Fund # 2 next week
Fund # 3 next week
Fund # 4 next week
Fund # 5 next week
Fund # 6 next week
Fund # 7 next week
Fund # 8 next week
Fund # 9 next week
Fund # 10 next week
She collects the closing weekly averages on Friday and uses the new data to predict prices of the 10 mutual funds for the next week. Making forecasts with a trained network requires only a few seconds, and the network can be readily updated with new information as it arises.
A similar network could be trained to predict prices a day or a month in advance (or, in fact, all of these) simply by giving the network new output neurons and revised training data which reflects the new time periods to be predicted.
The majority of financial applications are simply variations on this basic style. Often additional inputs are used which give the network historical information, such as what the DOW was last week. The design of this network, although simple, is effective.
A neural network is a new kind of computing tool that is not limited by equations or rules. Neural networks function by finding correlations and patterns in the data which you provide. These patterns become a part of the network during training. A separate network is needed for each problem you want to solve, but many networks follow the same basic format.
The networks described above were created with the BrainMaker Neural Network System. BrainMaker is available from California Scientific, and includes the data manipulation program NetMaker, "Introduction to Neural Networks"and a 422-page User's Guide. The price is $195.00. A separate network is needed for each problem you want to solve, but many networks follow the same basic format.