On the limits of measuring the bulge and disk properties of local and high-redshift massive galaxies

A considerable fraction of the massive quiescent galaxies at \emph{z} $\approx$ 2, which are known to be much more compact than galaxies of comparable mass today, appear to have a disk. How well can we measure the bulge and disk properties of these systems? We simulate two-component model galaxies in order to systematically quantify the effects of non-homology in structures and the methods employed. We employ empirical scaling relations to produce realistic-looking local galaxies with a uniform and wide range of bulge-to-total ratios ($B/T$), and then rescale them to mimic the signal-to-noise ratios and sizes of observed galaxies at \emph{z} $\approx$ 2. This provides the most complete set of simulations to date for which we can examine the robustness of two-component decomposition of compact disk galaxies at different $B/T$. We confirm that the size of these massive, compact galaxies can be measured robustly using a single S\'{e}rsic fit. We can measure $B/T$ accurately without imposing any constraints on the light profile shape of the bulge, but, due to the small angular sizes of bulges at high redshift, their detailed properties can only be recovered for galaxies with $B/T$ \gax\ 0.2. The disk component, by contrast, can be measured with little difficulty

Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.Comment: This is the full version of a paper in the ACM Conference on Economics and Computation (ACM-EC), 201

Low-Cost Learning via Active Data Procurement

We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent's cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showing how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint $B$, we give robust risk (predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an active approach, we can often guarantee to do significantly better by leveraging correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with $T$ arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the same order.Comment: Full version of EC 2015 paper. Color recommended for figures but nonessential. 36 pages, of which 12 appendi

Green accounting: Developing versus developed economies

Abstract. Businesses and corporates have started to formulate strategies to opt for environment-friendly and green operations. The same has also conceived the idea of green accounting that emphasizes taking environmental factors into account of corporate financial consideration and reports. Less to date, have made a comparative review for those progresses of green accounting in the developed versus developing economies, and to offer insights for academics and practitioners. This article offered a compact discussion of this issue and provides suggestions to theory and practices.Keywords. Green accounting, Developing economies, Developed economies.JEL. C23, F62, N17

Performance Evaluation for 59 Listed Electronic Corporations in Taiwan

Most previous studies concerning company performance evaluation focus merely on operational efficiency. Operational effectiveness, however, which might directly influence the survival of a company is usually ignored. As a result, this paper presents a study which uses an innovative two-stage data envelopment analysis (DEA) model that separates efficiency and effectiveness to evaluate the performance of 59 Listed corporations of the electronics industry in Taiwan. The empirical result of this paper is that a company with better efficiency doesn’t always mean that it has better effectiveness. There is no apparent correlation between these two indicators

The Role of Investor Sentiment in Asset Pricing

This thesis investigates various roles that investor sentiment may play in asset pricing. The empirical analysis consists of three main parts based on the role of investor sentiment in the stock markets. The first part discusses the role of investor sentiment as conditioning information. It aims to examine its ability to explain the dynamic nature of the expected returns for individual stocks and its explanatory power capture the financial market anomalies such as the size, value, liquidity, and effects. The second part focuses on the role of investor sentiment as a risk factor. The purpose is to construct a risk factor on the basis of investor sentiment and test whether this proposed sentiment factor is priced and helps to explain the aforementioned financial market anomalies. The third part explores the role of investor sentiment in different international stock markets. It attempts to assess the extent to which investor sentiment affects the stock market volatility and returns of different regions. The results suggest that investor sentiment exhibits explanatory power for cross section of stock returns in the U.S. market. Acting as conditioning information or a risk factor, investor sentiment can generally capture the size and value effects. Furthermore, it can also capture the momentum effect under certain model specifications. The thesis shows that investors require compensation for bearing noise traders; in other words, investor sentiment is a priced factor. At the market level, the impacts of investor sentiment on stock volatility and returns vary across countries. For some countries investor sentiment affects both volatility and returns while for the others investor sentiment has less influence on stock price behaviour. Overall, the findings of the thesis provide empirical evidence that overlooking the role of investor sentiment in classical finance theory could lead to an imperfect picture of describing the stock price behaviour