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Applications of Neural NetworksNeural Networks in Business and EconomyMany problems in business world are essentially about trying to predict the likelihood of different outcomes. The real world is so complex, with so many inter-related variables that predicting these outcomes are often very difficult. Conventional computing has a limited role to play in helping with these tasks but neural networks offer an alternate solution. By feeding in the different factors that affect an outcome over time a network can analyse previous trends and patterns to predict the future. Predicting Stocks with Neural Networks Warren Buffett is one of the most important and successful figures in the financial world. Others have built their own highly successful investment portfolios using his theories on investment and market analysis. Walkrich Investment Advisors used Neural Networks to produce an investment tool WRRAT based loosely on Warren Buffett's ideas to predict stock prices, and determine which stocks are trading below their market value. The results from January 1995 to January 1996 showed that a Portfolio of WRRAT's most under-priced shares saw an average advance of 33%. Another example is the use of neural network software by LBS Capital Management to predict the S&P 500 index. The company uses an expert system to provide instructions to the neural network, which then processes the data accordingly. When tested with hundreds of previous days data the neural network LBS trained predicts the S&P 500 with an accuracy of about 95%. Predicting Currencies with Neural Networks O'Sullivan Investments successfully used many neural networks in order to advise them of market trends. Mr James O'Sullivan produced an article Neural Nets: A Practical Primer, AI In Finance, 1994 outlined some of the networks used. One of the most important factors in producing a successful net is to ask the right kind of question. Rather than simply ask what the projected price of a currency might be, he asks at what price the market is likely to take off in one direction or the other etc. Predicting Natural Gas Prices with Neural Networks Northern Natural Gas is a regulated wholesaler of natural gas. They must develop and file a rate for the gas they sell based on the average cost of the gas. By developed a neural network that use factors such as the quarter of the year, season, temperature last month etc. to predict the following months oil price, the company was better able to plan rates. Predicting bonds with Neural Networks G. R. Pugh & Company does consulting to predict the prices of bonds of public utilities. The company used neural networks to help forecast the following years corporate bond prices and ratings of over 100 public utility companies. The network they used compared very favourably to conventional mathematical analysis. Whereas the network was able to predict a utilities rating (A, B, C) with 95% accuracy, conventional mathematical analysis was only effective 85% of the time. The only difficulties encountered by the network were associated with companies experiencing particularly unusual problems that were not incorporated into the networks inputs. Targeting Direct Mail Marketing with Neural Networks Microsoft used neural networks to maximise the effectiveness of their marketing campaign. Each year the company sent out mail to its registered customers. Most of this mail offered upgrades or new software but the response rate was rather low. The company used a neural network that was fed various variables such as how recently they registered, how many products they have bought etc. to target users more effectively. The results showed an average mailing lead to a 35% cost savings. Credit Scoring with Neural Networks Research conducted by Dr Herbert Jensen PhD demonstrated that "building a neural network capable of analysing the credit worthiness of loan applicants is quite practical and can be done quite easily". The neural network was trained on no more than 100 loan applications to process application data such as occupation, years with employer etc. Despite the relatively small training set the network achieved a 75-80% success rate. This compared well with more traditional scoring methods that resulted in about a 75% success rate. Real Estate Appraisal with Neural Networks Several neural networks have been used to predict the sale prices of homes in order to help appraisers assess, sellers estimate asking prices, and home owners decide on improvements. Richard Borst successfully trained a neural network to appraise real estate in the New York area. His network incorporated almost 20 variables including the square feet of living area, age, etc. He used over 200 sales records from 1988 and 1989 to train the network with about 90% accuracy. Previous | Next | Page 1 2 3 4 Index | Applications | Business and Economy References: © 2008 Marcus bros
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