Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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for very helpful discussions on this topic. We also thank Yi-Ting Yeh for helping simulations in the paper. C.-M.

We set out in this study to examine (a) whether "socially responsible investment" (SRI) portfolios can outperform less-SRI portfolios in the emerging Asian stock markets and (b) whether investors within these emerging markets achieve awareness of SRI through publicly available news. On the basis of 2009-2013 data, we find that SRI portfolios tend to perform better in Japan. However, firms in the emerging Asian markets do not earn rewards for superior corporate social responsibility (CSR) practices. We also find that investors in the emerging Asian markets are indeed aware of SRI through public CSR news releases; in particular, investors in these markets reward high environmental-, social-, and governance-rated firms for their good CSR practices advertised through such news releases, relative to those with no news releases. KEYWORDScorporate social responsibility, individual investors, news advertisement effect, socially responsible investment

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