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The purpose of this paper is to examine the effect of the FDI decision on domestic investment in the case of Taiwanese manufacturing firms. In addition, we also consider the deferral effect of the FDI decision and the role of firm size. To this end, this paper takes advantage of an endogenous switching model from which consistent estimators are obtained after correcting for the self-selection problem. The empirical results show that the effect of these manufacturing firms' FDI decisions on domestic investment is significant within the firms. Furthermore, a crowding-out effect of FDI on domestic investment is found when Taiwanese firms engage in defensive FDI. Finally, FDI is found to have a positive influence on the domestic investment of the larger firms, while the influence is negative in the case of the smaller firms.

The purpose of the present paper is to examine the interdependence between the overseas and domestic R&D activities of Taiwanese multinational enterprises (MNEs). Because domestic (overseas) R&D activities may enter the overseas (domestic) R&D determination equation as an endogenous right-hand side variable, the traditional estimation approach will be invalid due to the endogeneity problem. Therefore, it is proposed that the two R&D decisions be estimated using a simultaneous Tobit model. The results in this paper indicate that Taiwanese MNEs that engage in higher levels of domestic R&D activities will engage in overseas R&D activities. Interestingly, it is also found that Taiwanese MNEs increase their overseas R&D activities in developed countries instead of their domestic R&D activities, whereas Taiwanese MNEs that engage in overseas R&D activities in less-developed countries will raise their domestic R&D activities. As for the other common determinants of the overseas and domestic R&D activities, firm size and the capital-labor ratio are found to be the main factors. ASIAN ECONOMIC JOURNAL2. Pisano (1990) examines the R&D boundaries decision of 92 biotechnology R&D projects based on the transaction-cost concept. Pisano finds that small-numbers-bargaining plays a significant role in the decision between in-house and external R&D.

Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.

The purpose of this study is to examine the influence of overseas R&D on firm productivity. In particular, we further consider the moderating effect of internal technological capability. To provide more empirical evidence, this analysis takes advantage of a longitudinal dataset covering the 2009-2013 period and the system-GMM approach is employed in the empirical analysis to control for the problem of endogeneity. The empirical results show that overseas R&D has a significant influence on their firm productivity. Moreover, internal technological capability is found to play a significant moderating role in strengthening the influence of overseas R&D on firm productivity.