Abnormal local temperatures (“ALTs”) are defined as the extent to which current temperatures are more uncomfortable than historical norms in firms’ operating locations, encompassing both hotter and colder conditions. This paper analyzes the asset pricing implications of extreme ALTs in the United States. A long-short portfolio constructed from firms with low minus high ALTs within an industry generates a monthly risk-adjusted return exceeding 0.4% from 2000 to 2022. The negative return–extreme ALTs relationship cannot be explained by existing systematic risks, investors’ preferences, mood, political leaning, emissions, or other climate-related variables. Firms encountering extreme ALTs exhibit worsened operating performance, attributable to lower labor productivity, employee reduction, and direct output impact. Extreme ALTs also relate to lower earnings surprises and diminished institutional investors’ holdings. The predictability of stock returns in the presence of extreme ALTs is primarily due to the mispricing of negative cash-flow news rather than the updating of discount-rate news.

A significant number of procurements in both public and private sectors have adopted the practice of splitting the award among multiple bidders in an auction, as an alternative to the traditional one-winner-take-all auction. This aims to encourage participation from small firms and reduce dependency on a single supplier. One prominent example is China's national drug procurement split-award auction, where the societal importance of drug prices underscores the need for a thorough examination of the rationale for using this auction format. However, there is limited theoretical and empirical guidance available in the literature. This paper investigates the competitive impact of split-award auctions on key outcomes, such as participation and procurement costs. Theoretically, I demonstrate that split-award auctions consistently boost participation but only decrease the expected procurement cost if no participation would have occurred in an otherwise winner-take-all auction. Empirically, I estimate the direction and magnitude of the effects using drug procurement data. The findings reveal that split-award auctions moderately increase average participation by 0.85 bidders (17%) but significantly raise the unit expected procurement cost by 4 CNY (38%). The reallocation of production to more expensive bidders and an increase in bidders' markups contribute equally to the rise of the expected procurement cost.

Recommender systems play a vital role in driving the long-term value for online platforms. However, developing recommender systems for multi-sided platforms faces two prominent challenges. First, different sides have different and possibly conflicting utilities. Recommending in this context entails jointly optimizing multiple objectives. Second, many platforms adopt hierarchical homepages, where items can either be individual products or groups of products. Off-the-shelf recommendation algorithms are not applicable in these settings. To address these challenges, we propose MOHR, a novel multi-objective hierarchical recommender. By combining machine learning, probabilistic hierarchical aggregation, and multi-objective optimization, MOHR efficiently solves the multi-objective ranking problem in a hierarchical setting through an innovative formulation of probabilistic consumer behavior modeling and constrained optimization. We implemented MOHR on one of the world’s largest food delivery platforms, and demonstrate that long-term profit maximization can be achieved through a multi-objective approach as we proposed, outperforming existing single-score based approaches. Moreover, the MOHR framework offers managers a mathematically principled tool to make quantifiable and interpretable trade-offs across multiple objectives for long-term profit optimization. Online experiments showed significant improvements in consumer conversion, retention, and gross bookings, resulting in a $1.5 million weekly increase in revenue. As a result, MOHR has been deployed globally as the recommender system for the food delivery platform’s app homepage.