Forecasting the Consumption for Electricity in Taiwan

This paper use linear regression and non-linear artificial neural network (ANN) model to analyze how the four economic factors: national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI), affect Taiwan’s electricity consumption, furthermore, develop an economic forecasting model. Both models agree with that POP and NI are of the most influence on electricity consumption, whereas GDP of the least. Then, we compare the out-of-sample forecasting capabilities of the two models. The comparing result indicates that the linear model is obviously of higher bias value than that of ANN model, and of weaker ability of forecasting capability on peaks or bottoms. This probably results from: 1) linear regression model is built on the logarithm function of electricity consumption, and ANN is built on the original data; 2) ANN model is capable of catching sophisticated non-linear integrating effects. Consequently, ANN model is the more appropriate between the two to be applied to building an economic forecasting model of Taiwan’s electricity consumption

Estimating the Capital Structure of High Tech and Traditional Corporations\u27 Capital Structure: Artificial Neural Networks vs. Multiple Linear Regressions

This study adopted multiple linear regression models and artificial neural networks (ANNs) to analyze the important determinants of capital structures of the high tech and traditional corporations in Taiwan, respectively. The ten independent variables (determinants) employed herein included seven corporation feature variables and three external macro-economic variables. The following conclusions were reached: 1) From the root MSE, the ANN model achieved a better fit than the regression model. 2) The capital structure of high tech corporations does not differ significantly from that of traditional corporations, but differences do exist in the determinants of the capital structure. 3) Macro-economic variables more significantly affect the sensitivity of the capital structure of high tech corporations than traditional corporations. 4) Business risk has positive/negative impacts on capital structure of high tech/traditional corporations, respectively. 5) Six features of corporations have the same impacts on both high tech and traditional corporations, namely: firm size (+), growth opportunities (+), profitability (-), collateral value (+), non-debt tax shield (-), and dividend policy (-). In optimizing capital structure, the following policy implications can be dra wn for any company based on the results of this study: l Larger corporations can borrow more than small corporations, and thus enjoy the benefit of greater financial leverage. l Corporations with higher growth opportunities need to borrow more to meet their capital needs. l Corporations with higher profitability need to borrow less to meet their capital needs. l Corporations with higher collateral value (fixed assets) can borrow more than those with lower collateral value. l Increased non-debt tax shield will lower the tax benefits of financial leverage and hence reduce incentives for borrowing. l Corporations with higher cash dividend payments generally borrow less than corporations with lower cash dividend payments. Managers can apply the analytical results above to optimize capital structure and maximize firm value