Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages appendix

Home Country Bias: Does Domestic Experience Help Investors Enter Foreign Markets?

This paper investigates whether investors' domestic experience helps them enter foreign markets. We show that investors first invest in domestic securities and only some time later they invest abroad in foreign securities. We also show that investors who trade more often in the domestic market start to invest abroad earlier. Our findings suggest that the experience investors acquire while they trade in the domestic market is a key reason why active investors enter the foreign market earlier. A reason is that highly educated investors as well as investors with more financial knowledge, arguably those for whom learning by trading is the least important, do not need to trade as much in the domestic market before they start investing in foreign securities. Another reason is that investors who start investing in foreign securities are able to improve on their performance afterwards. This improvement in performance constitutes further evidence that the home country bias is costly, thereby confirming that there are gains for investors from investing abroad.Learning, home country bias, duration analysis.

Active Learning for Hidden Attributes in Networks

In many networks, vertices have hidden attributes, or types, that are correlated with the networks topology. If the topology is known but these attributes are not, and if learning the attributes is costly, we need a method for choosing which vertex to query in order to learn as much as possible about the attributes of the other vertices. We assume the network is generated by a stochastic block model, but we make no assumptions about its assortativity or disassortativity. We choose which vertex to query using two methods: 1) maximizing the mutual information between its attributes and those of the others (a well-known approach in active learning) and 2) maximizing the average agreement between two independent samples of the conditional Gibbs distribution. Experimental results show that both these methods do much better than simple heuristics. They also consistently identify certain vertices as important by querying them early on

On the Size of the Active Management Industry

We argue that active management’s popularity is not puzzling despite the industry’s poor track record. Our explanation features decreasing returns to scale: As the industry’s size increases, every manager’s ability to outperform passive benchmarks declines. The poor track record occurred before the growth of indexing modestly reduced the share of active management to its current size. At this size, better performance is expected by investors who believe in decreasing returns to scale. Such beliefs persist because persistence in industry size causes learning about returns to scale to be slow. The industry should shrink only moderately if its underperformance continues.

Flickr: A case study of Web2.0

The “photosharing” site Flickr is one of the most commonly cited examples used to define Web2.0. This paper explores where Flickr’s real novelty lies, examining its functionality and its place in the world of amateur photography. The paper draws on a wide range of sources including published interviews with its developers, user opinions expressed in forums, telephone interviews and content analysis of user profiles and activity. Flickr’s development path passes from an innovative social game to a relatively familiar model of a website, itself developed through intense user participation but later stabilising with the reassertion of a commercial relationship to the membership. The broader context of the impact of Flickr is examined by looking at the institutions of amateur photography and particularly the code of pictorialism promoted by the clubs and industry during the C20th. The nature of Flickr as a benign space is premised on the way the democratic potential of photography is controlled by such institutions. Several optimistic views of the impact of Flickr such as its facilitation of citizen journalism, “vernacular creativity” and in learning as an “affinity space” are evaluated. The limits of these claims are identified in the way that the system is designed to satisfy commercial purposes, continuing digital divides in access and the low interactivity and criticality on Flickr. Flickr is an interesting source of change, but can only to be understood in the perspective of long term development of the hobby and wider social processes

Institutional Herding in Bond Markets

Recent research has shown that institutional herding is a relevant phenomenon in stock markets. Do institutional investors also follow each other in bond markets? This paper focuses on the German bond market and uses data from 57 German mutual funds that invest mainly in DM-denominated bonds, which represents 71% of the total market volume. Due to the variety and large number of bonds that exist, we do not expect mutual funds to herd with regard to separate bonds. We believe instead that bonds with the same characteristics such as interest rate, maturity, collateral, or issuer are considered to be equivalent by institutional investors. Consequently, we construct "bond groups" consisting of similar bonds and analyze herding at a "bond group" level. Our results indicate that there is strong evidence of herding, albeit it is weaker than in stock markets. Further analysis suggests that mutual funds do not place an equal weight on different bond characteristics. Nominal interest rates appear to be most important in the bond selection process. --Mutual Funds,Herding,Imitation,Coordination,Behavioral Finance