C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526   Pub Date: 06/01/95

Chapter 14
Application to Financial Forecasting

Introduction

In Chapters 7 and 13, the backpropagation simulator was developed. In this chapter, you will use the simulator to tackle a complex problem in financial forecasting. The application of neural networks to financial forecasting and modeling has been very popular over the last few years. Financial journals and magazines frequently mention the use of neural networks, and commercial tools and simulators are quite widespread.

This chapter gives you an overview of typical steps used in creating financial forecasting models. Many of the steps will be simplified, and so the results will not, unfortunately, be good enough for real life application. However, this chapter will hopefully serve as an introduction to the field with some added pointers for further reading and resources for those who want more detailed information.

Who Trades with Neural Networks?

There has been a great amount of interest on Wall Street for neural networks. Bradford Lewis runs two Fidelity funds in part with the use of neural networks. Also, LBS Capital Management (Peoria, Illinois) manages part of its portfolio with neural networks. According to Barron’s (February 27, 1995), LBS’s $150 million fund beat the averages by three percentage points a year since 1992. Each weekend, neural networks are retrained with the latest technical and fundamental data including P/E ratios, earnings results and interest rates. Another of LBS’s models has done worse than the S&P 500 for the past five years however. In the book Virtual Trading, Jeffrey Katz states that most of the successful neural network systems are proprietary and not publicly heard of. Clients who use neural networks usually don’t want anyone else to know what they are doing, for fear of losing their competitive edge. Firms put in many person-years of engineering design with a lot of CPU cycles to achieve practical and profitable results. Let’s look at the process:

Developing a Forecasting Model

There are many steps in building a forecasting model, as listed below.

1.  Decide on what your target is and develop a neural network (following these steps) for each target.

2.  Determine the time frame that you wish to forecast.

3.  Gather information about the problem domain.

4.  Gather the needed data and get a feel for each inputs relationship to the target.

5.  Process the data to highlight features for the network to discern.

6.  Transform the data as appropriate.

7.  Scale and bias the data for the network, as needed.

8.  Reduce the dimensionality of the input data as much as possible.

9.  Design a network architecture (topology, # layers, size of layers, parameters, learning paradigm).

10.  Go through the train/test/redesign loop for a network.

11.  Eliminate correlated inputs as much as possible, while in step 10.

12.  Deploy your network on new data and test it and refine it as necessary.

Once you develop a forecasting model, you then must integrate this into your trading system. A neural network can be designed to predict direction, or magnitude, or maybe just turning points in a particular market or something else. Avner Mandelman of Cereus Investments (Los Altos Hills, California) uses a long-range trained neural network to tell him when the market is making a top or bottom (Barron’s, December 14, 1992).

Now let’s expand on the twelve aspects of model building:

The Target and the Timeframe

What should the output of your neural network forecast? Let’s say you want to predict the stock market. Do you want to predict the S&P 500? Or, do you want to predict the direction of the S&P 500 perhaps? You could predict the volatility of the S&P 500 too (maybe if you’re an options player). Further, like Mr. Mandelman, you could only want to predict tops and bottoms, say, for the Dow Jones Industrial Average. You need to decide on the market or markets and also on your specific objectives.

Another crucial decision is the timeframe you want to predict forward. It is easier to create neural network models for longer term predictions than it is for shorter term predictions. You can see a lot of market noise, or seemingly random, chaotic variations at smaller and smaller timescale resolutions that might explain this. Another reason is that the macroeconomic forces that fundamentally move market over long periods, move slowly. The U.S. dollar makes multiyear trends, shaped by economic policy of governments around the world. For a given error tolerance, a one-year forecast, or one-month forecast will take less effort with a neural network than a one-day forecast will.

Domain Expertise

So far we’ve talked about the target and the timeframe. Now one other important aspect of model building is knowledge of the domain. If you want to create an effective predictive model of the weather, then you need to know or be able to guess about the factors that influence weather. The same holds true for the stock market or other financial market. In order to create a real tradable Treasury bond trading system, you need to have a good idea of what really drives the market and works— i.e., talk to a Tbond trader and encapsulate his domain expertise!

Gather the Data

Once you know the factors that influence the target output, you can gather raw data. If you are predicting the S&P 500, then you may consider Treasury yields, 3-month T-bill yields, and earnings as some of the factors. Once you have the data, you can do scatter plots to see if there is some correlation between the input and the target output. If you are not satisfied with the plot, you may consider a different input in its place.