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

Constructing Control Process for Wafer Defects Using Data Mining Technique

The wafer defects influence the yield of a wafer. The integrated circuits (IC) manufacturers usually use a Poisson distribution based c-chart to monitor the lot-to-lot wafer defects. As the wafer size increases, defects on wafer tend to cluster. When the c-chart is used, the clustered defects frequently cause erroneous results. The main objective of this study is to develop a hierarchical adaptive control process to monitor the clustered defects effectively and detect the wafer-to-wafer variation and lot-to-lot variation simultaneously using data mining technique

Integrating SPC and EPC for Multivariate Autocorrelated Process

Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling’s T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process

Process Chart for Controlling Wafer Defects using Fuzzy Theory

Manufacturers of integrated circuits (IC) frequently utilize c-charts to monitor wafer defects. The clustering of wafer defects increases with the surface area of the wafers. The clustering of defects causes the Poisson-based c-chart to show many false alarms. Although Neyman-based c-chart has been developed to reduce the number of false alarms, it has some shortcomings in practical use. This study presents a process control chart that applies Fuzzy theory and the engineering experience to monitor the clustered defects on a wafer. The proposed method is simpler and more efficient than that of the Neyman-based c-chart. A case study of an IC company in Taiwan demonstrates the effectiveness of the proposed method