- 學位論文
企業社會責任指數之研究—灰色關聯分析與類神經網路之應用
Application of Grey Relational Analysis and Artificial Neural Networks on Corporate Social Responsibility (CSR) Indices
摘要
本研究運用灰色關聯分析(GRA)研究企業社會責任(CSR)指標的指數報酬率以及波動率，並同時利用三種類型的類神經網路模型，分別為：倒傳遞類神經網路(ANN)、遞歸類神經網路(RNN)以及輻射基底函數類神經網路(RBFNN)，捕捉CSR指數的非線性趨勢，以獲得更好的預測精準度。本提案將根據灰色關聯分析等級(GRGs). 的排名去找出哪一項類型經網路擁有較強的預測能力。本研究首先應用灰色關聯分析等級模型分析六個股價波動變量，其中美元指數、貿易指數、CRB指數、布倫特原油期貨指數的影響力最強。再者，運用更強效的預測工具─類神經網路模型─根據平均絕對誤差和均方根誤差的最低值預測CSR指數報酬率。最後，本研究將會將六個股價變量依照其灰色關聯分析等級分為較高等級以及較低等及兩部分來做灰色關聯分析的穩健性檢測。此項研究結果顯示BPN具有最好的預測能力，即使將數據分為10％、33%及50％時結果也是如此。然而RNN和RBFNN模型的預測能力較小。另一方面，GRG顯示高GRG變量較低GRG變量對於應變量更有影響力，但是GRG相對於六個變量的預測能力相比仍然較差。總體而言，此六項變量使用BPN模型，。金融市場參與者和基金經理人有更大的機會實現更準確的預測。
並列摘要
This research examines return and volatility predictability of Corporate Social Responsibility (CSR) Indices through the grey relational analysis (GRA), and also applies three types of artificial neural networks (ANN) model, namely, back-propagation perceptron, recurrent neural network, and radial basis function neural network to capture nonlinear tendencies of CSR indices for a better forecasting accuracy. The research will find which ANN model has stronger predictive power compared with the other models, based on the ranking of the grey relational grades (GRGs). This research aims to first apply the GRA model in determining which among the 6 variables of stock and volatility indices, US dollar index, Trade index, CRB index, and Brent crude oil futures index have the strongest influence based on their relevant ranks. Then, the relatively more powerful forecasting tool, ANN models, will be used to predict CSR index returns, based on the lowest values of mean absolute error and root mean square error. And lastly, to check the robustness of GRA results, this paper divides the six variables in half depending on their relevant ranks based on their GRGs between those with high GRGs and low GRGs. This study will try to suggest to financial market players in determining appropriate ANN models in trying to forecast CSR index returns. The result in this paper showed the comparison of three ANN models, BPN had the best predicting power compared with RNN and RBFNN, we also learned that RNN and RBFNN model also got good performance with predicting accuracy. This paper also separated the data to 10%, 33% and 50% testing data level to test the proficiency of the available forecasting information in the time-series of the predictors. The result with CSR indices showed 66.6% of the data from the BPN model had the lowest values of MAEs compared with RNN model had only 33.3%. The predicting power of BPN model also showed with Non-CSR indices, 60% of the non-CSR were best by BPN model, 30% by RNN model and only 10% by RBFNN model. Traders, investors and fund manager can rely on BPN predicting power with large or even small data set. The result also suggests the predicting power of RNN and RBFNN model with a small set of data. Overall, with the best forecasting ability by using BPN model, we can say that, traders and fund managers have stronger chance of achieving more accurate forecasting. The GRAs table showed the dominance of High GRG of the influence toward CSR and Non-CSR indices. In summary, traders and fund managers can benefit by combining all six variables to get better forecasting accuracy.