Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13761
Title: Neural network forecasts of Singapore property stock returns using accounting ratios
Authors: LIU JIAFENG
Keywords: Neural Network, Singapore, Property Stock, Accounting Ratios, Logit Regression, OLS Regression
Issue Date: 17-Jan-2004
Source: LIU JIAFENG (2004-01-17). Neural network forecasts of Singapore property stock returns using accounting ratios. ScholarBank@NUS Repository.
Abstract: The return of property stocks is one of the main research areas in property stock performance. This work have tried to compare the forecast of Singapore property stock returns by neural networks with that by traditional regressions using accounting ratios as input variables. Based on the results, this work supports: accounting ratios can serve as leading indicators of stock returns in the next year; classification models (logit regression models and logit neural networks) can outperform point estimation models (OLS regression models and OLS neural networks) for problems at hand; logit neural networks can outperform all other three alternatives. This work is the first to use neural network to examine the performance of Singapore property stocks.
URI: http://scholarbank.nus.edu.sg/handle/10635/13761
Appears in Collections:Master's Theses (Open)

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