Tradeoff and Forecasting Approach for Social Responsible Investing: Artificial Neural Network

Abstract

The proposed research helps to associate the risk and return relationship of selected social responsible investing funds. The various forecasting methods are used to evaluate the significance of the social responsible investing funds for the investors. In the present study, information criterion model is used to determine risk and return which is a good predictor model for social responsible investing funds. In particular, the article uses the artificial neural network approach which helps to recognize the risk and return patterns of the social responsible investing funds. For the present study, secondary data had been collected in which the net asset values (NAV) of TATA ethical funds and UTI Charitable and Religious Trust and Registered Society (UTI CRTS) funds are collected from June 2013 to June 2016 to assess the risk and return relationship of selected social responsible investing funds for the investors. It was found that there is a positive relationship between risk and return. All the forecasting models regression, autoregressive integrated moving average, information criterion and artificial neural network are appropriate for depicting the significant relationship, impact and errors between risk and return values.