TAILIEUCHUNG - Elsevier, Neural Networks In Finance 2005_7

Tham khảo tài liệu 'elsevier, neural networks in finance 2005_7', tài chính - ngân hàng, ngân hàng - tín dụng phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 146 6. Times Series Examples from Industry and Finance aggregate and disaggregated market forecasting with traditional time series as well as with pooled time-series cross-sectional methodologies such as the study by McCarthy 1996 . The structure of the automobile market for new vehicles is recursive. Manufacturers evaluate and forecast the demand for the stock of automobiles the number of retirements and their market share. Adding a dose of strategic planning they decide how much to produce. These decisions occur well before production and distribution take place. Manufacturers are providing a flow of capital goods to augment an existing stock. For their part consumers decide at the time of purchase based on their income price and utility requirements what stock is optimal. To the extent that consumer decisions to expand the stock of the asset coincide with or exceed the amount of production by manufacturers prices will adjust to revise the optimal stock and clear the market. To the extent they fall short the number of retirements of automobiles will increase and the price of new vehicles will fall to clear the market. Chow 1960 Hess 1977 and McCarthy 1996 show how forecasting the demand in the markets is a sufficient proxy to modeling the optimal stock decision. Both the general stability in the underlying market structure and the recursive nature of producer versus consumer decision making have made this market amenable to less complex estimation methods. Since research suggests this is precisely the kind of market in which linear time-series forecasting will perform rather well it is a good place to test the usefulness of the alternative of neural networks for The Data We make use of quantity and price data for automobiles as well as an interest rate and a disposable income as aggregate variables. The quantity variable represents the aggregate production of new vehicles excluding heavy trucks and machinery obtained from the Bureau of .

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