Impact investing typically involves ranking and selecting assets based on a non-financial impact factor, such as the environmental, social, and governance (ESG) score, the amount of carbon emissions, and the prospect of developing a disease-curing drug. We develop a framework for constructing optimal impact portfolios and quantifying their financial performance. Under general bivariate distributions of the impact factor and residual returns in excess of other factors, we demonstrate that the construction and performance of optimal impact portfolios depend only on two quantities: the dependence structure (copula) between the impact factor and residual returns, and the marginal distribution of residual returns. When the impact factor and residual returns are jointly normally distributed, the performance of optimal impact portfolios depends on the correlation between the two, and variations in this correlation over time contribute negatively to performance. More generally, we explicitly derive the optimal portfolio weights under two widely-used copulas---the Gaussian copula and the Archimedean copula family. The optimal weights depend on the tail dependence characteristics of the copula. In addition, when the marginal distribution of residual returns is skewed or heavy-tailed, assets with the most extreme impact factors should have lower weights than non-extreme assets due to their high risk. Overall, these results provide a recipe for constructing and quantifying the performance of optimal impact portfolios for any impact factor with arbitrary dependence structures with asset returns.
Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item ratings while omitting the underlying causes behind the ratings. For example, the widely-used Yahoo!R3 dataset contains little information on the causes of the user-movie ratings. A solution could be to conduct surveys and require the users to provide such information. In practice, the user surveys can hardly avoid compliance issues and sparse user responses, which greatly hinders the exploration of causality-based recommendation. To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios. To illustrate the use of our framework, we construct a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the movies as well as their descriptive tags and rating information collected from a famous movie rating website. Using the collected data and the causal graph, the user-item-ratings and their corresponding user-item-tags are automatically generated, which provides the reasons (selected tags) why the user rates the items. Descriptive statistics and baseline results regarding the CTAR dataset are also reported. The proposed data generation framework is not limited to recommendation, and the released APIs can be used to generate customized datasets for other research tasks.
This paper investigates corporate history as a specific source of firm fixed effects by comparing firms born in one of the NBER recession periods with other firms. We find strong empirical evidence that firms born in recession have stronger operating performance, and perform particular better in the stock market during the recession periods. We also find that a significant extent of the heterogeneity in corporate innovation, investment, financing, organizational, and risk taking policies can be attributed to firm birth years. Our findings suggest that the otherwise unavailable creative destruction opportunities and the adverse founding conditions may have imprinted their marks on firms. These imprinted marks have a long-lasting effect on firms' approach toward decision making, leading to large variation in firm performance.
This paper identifies changes in trade barrier as a pricing factor for domestic firms in importing countries. I first build a dynamic stochastic general equilibrium with international trade. In the model, an exogenous shock that decreases trade barriers of the importing country has a negative effect on the cash flows of domestic companies in that country. The investors of the domestic firms exposed to the sudden reduction in trade barriers require positive risk premia to compensate for the displacement risk. The effect of displacement risk is strongest when the importing industry has high transportation cost, and when the importing industry is more concentrated. Using data of U.S. industry-level import tax to measure changes in trade barriers, I find that (i) industries with more severe tariff reduction have higher average returns; (ii) this effect of tariff changes on stock returns is largest for industries with high freight and insurance costs and industries with high Herfindahl index.
In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we present vidar, a bit sequence array where each bit represents whether the accumulation of photons has reached a threshold, to record and reconstruct the scene radiance at any moment. By employing only consumer-level CMOS sensors and integrated circuits, we have developed a vidar camera that is 1,000× faster than conventional cameras. By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1,000× faster than human vision. We demonstrate the utility of the vidar camera and the super vision system in an assistant referee and target pointing system. Our study is expected to fundamentally revolutionize the image and video concepts and related industries, including photography, movies, and visual media, and to unseal a new spiking neural network-enabled speed-free machine vision era.
Event cameras as bioinspired vision sensors have shown great advantages in high dynamic range and high temporal resolution in vision tasks. Asynchronous spikes from event cameras can be depicted using the marked spatiotemporal point processes (MSTPPs). However, how to measure the distance between asynchronous spikes in the MSTPPs still remains an open issue. To address this problem, we propose a general asynchronous spatiotemporal spike metric considering both spatiotemporal structural properties and polarity attributes for event cameras. Technically, the conditional probability density function is first introduced to describe the spatiotemporal distribution and polarity prior in the MSTPPs. Besides, a spatiotemporal Gaussian kernel is defined to capture the spatiotemporal structure, which transforms discrete spikes into the continuous function in a reproducing kernel Hilbert space (RKHS). Finally, the distance between asynchronous spikes can be quantified by the inner product in the RKHS. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art methods and achieves significant improvement in computational efficiency. Especially, it is able to better depict the changes involving spatiotemporal structural properties and polarity attributes.