Foreign Institutional Investors: Fair-Weather Friends or Smart Traders?

We examine a theoretically robust but previously undocumented issue of what drives foreign portfolio investments into emerging markets. Foreign institutional investors (FIIs) are often blamed as fair-weather friends who pull out their investment at the first sign of trouble. Using a bottom-up approach, we explore this possibility. We demonstrate the influence of the firm-specific factors such as size, book to market ratio, the riskiness of the stocks, stock prices, dividend yield, liquidity, leverage, and earnings on the FII ownership. We find no evidence to show foreign investors as fair-weather friends. Instead, they are smart traders who follow a diligent investment strategy. We suggest reforms in corporate governance and improvement in financial fundamentals of the companies to attract FII ownership. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40953-021-00233-3.

Pattern of Local Gravitational Force (PLGF): A Novel Local Image Descriptor.

This paper presents a novel local image descriptor called Pattern of Local Gravitational Force (PLGF). It is inspired by Law of Universal Gravitation. PLGF is a hybrid descriptor, which is a combination of two feature components: one is the Pattern of Local Gravitational Force Magnitude (PLGFM), and another is Pattern of Local Gravitational Force Angle (PLGFA). PLGFM encodes the local gravitational force magnitude, and PLGFA encodes the local gravitational force angle that the center pixel exerts on all other pixels within a local neighborhood. We propose a novel noise resistance and the edge-preserving binary pattern called neighbors to center difference binary pattern (NCDBP) for gravitational force magnitude encoding. Finally, the histograms of the two components are concatenated to construct the PLGF descriptor. Experimental results on the existing face recognition databases, texture database, and biomedical image database show that PLGF is an effective image descriptor, and it outperforms other widely used existing descriptors. Even if in complicated variations like noise, and illumination with smaller databases, a combination of PLGF and convolutional neural network (CNN) performs consistently better than other state-of-the-art techniques.