Explore the vast range of titles available.

We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first-day return and long-run underperformance of IPO stocks.

We develop a framework for continuous search for information on a choice set of multiple alternatives and apply it to consumer search in a product market. When a consumer considers purchasing a product in a product category, the consumer can gather information sequentially on several products. At each moment, the consumer can choose which product to gather more information on and whether to stop gathering information and purchase one of the products or to exit the market with no purchase. Given costly information gathering, consumers end up not gathering complete information on all the products and need to make decisions under imperfect information. Under the assumption of constant informativeness of search, we solve for the optimal search, switch, and purchase or exit behavior in such a setting, which is characterized by an optimal consideration set and a purchase threshold structure. This paper shows that a product is only considered for search or purchase if it has a sufficiently high expected utility. Given multiple products in the consumer’s consideration set, the consumer only stops searching for information and purchases a product if the difference between the expected utilities of the top two products is greater than some threshold. Comparative statics show that negative information correlation among products widens the purchase threshold, and so does an increase in the number of the choices. Under our rational consumer model, we show that choice overload can occur when consumers search or evaluate multiple alternatives before making a purchase decision. We also find that it is optimal for a monopolistic seller of multiple products to facilitate information search for low-valuation consumers and obfuscate information for those with high valuations. This paper was accepted by Eric Anderson, marketing .

This paper surveys recent results in pursuit-evasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work.

A number of labor markets and student placement systems can be modeled as many-to-one matching markets. We analyze the scope for manipulation in many-to-one matching markets under the student-optimal stable mechanism when the number of participants is large. Under some regularity conditions, we show that the fraction of participants with incentives to misrepresent their preferences when others are truthful approaches zero as the market becomes large. With an additional condition, truthful reporting by every participant is an approximate equilibrium under the student-optimal stable mechanism in large markets.

We study decisions that involve choosing between different numbers of options under time pressure using eye-tracking to monitor the search process of the subjects. We find that subjects are quite adept at optimizing within the set of items that they see, that the initial search process is random in value, that subjects use a stopping rule to terminate the search process that combines features of optimal search and satisficing, and that subjects search more often in certain focal regions of the display, which leads to choice biases. (JEL C91, D12, M31)

We analyze the incentives to divert search for an information intermediary who enables buyers (consumers) to search affiliated sellers (stores). We identify two original motives for diverting search (i.e., inducing consumers to search more than they would like): (i) trading off higher total consumer traffic for higher revenues per consumer visit; and (ii) influencing stores' choices of strategic variables (e.g., pricing). We characterize the conditions under which there would be no role for search diversion as a strategic instrument for the intermediary, thereby showing that it occurs even when the contracting space is significantly enriched. We then discuss several applications related to online and brick-and-mortar intermediaries.

I discuss the failure of the canonical search and matching model to match the cyclical volatility in the job finding rate. I show that job creation in the model is influenced by wages in new matches. I summarize microeconometric evidence and find that wages in new matches are volatile and consistent with the model's key predictions. Therefore, explanations of the unemployment volatility puzzle have to preserve the cyclical volatility of wages. I discuss a modification of the model, based on fixed matching costs, that can increase cyclical unemployment volatility and is consistent with wage flexibility in new matches.

Assortative matching between workers and firms provides evidence of the complementarities or substitutes in production. The presence of complementarities is important for policies that aim to achieve the optimal allocation of resources, e.g. unemployment insurance. We argue that using wage data alone, it is virtually impossible to identify whether assortative matching is positive or negative. Even though we cannot identify the sign of the sorting, we can identify the strength, i.e. the magnitude of the cross-partial and the associated welfare loss. We first show that the wage for a given worker is non-monotonic in the type of his employer. This is due to the fact that in a sorting model, wages reflect the opportunity cost of mismatch. We analytically show that this non-monotonicity prevents standard firm fixed effects to correlate with the true type of the firm. We then propose an alternative procedure that measures the strength of sorting in the presence of search frictions. Knowing the strength of sorting facilitates the measurement of the output loss due to mismatch.

In this article, we introduce an innovative hybrid quantum search algorithm, the Robust Non-oracle Quantum Search (RNQS), which is specifically designed to efficiently identify the minimum value within a large set of random numbers. Distinct from the Grover’s algorithm, the proposed RNQS algorithm circumvents the need for an oracle function that describes the true solution state, a feature often impractical for data science applications. Building on existing non-oracular quantum search algorithms, RNQS enhances robustness while substantially reducing running time. The superior properties of RNQS have been demonstrated through careful analysis and extensive empirical experiments. Our findings underscore the potential of the RNQS algorithm as an effective and efficient solution to combinatorial optimization problems in the realm of quantum computing.

Web personalization can achieve two business goals: increased advertising revenue and increased sales revenue. The realization of the two goals is related to two kinds of user behavior: item sampling and item selection. Prior research does not provide a model of attitude formation toward a personalization agent nor of how attitudes relate to these two behaviors. This limits our understanding of how web personalization can be managed to increase advertising revenues and/or sales revenues. To fill this gap, the current research develops and tests a theoretical model of user attitudes and behaviors toward a personalization agent. The model is based on an integration of two theories: the elaboration likelihood model (ELM) and consumer search theory (CST). In the integrated model, a user’s attitude toward a personalization agent is influenced by both the number of items he/she has sampled so far (from CST) and the degree to which he/she cognitively processes each one (from ELM). In turn, attitude is modeled to influence both behaviors—that is, item selection and any further item sampling. We conducted a lab study and a field study to test six hypotheses. This research extends the theory on web personalization by providing a more complete picture of how sampling and processing of personalized recommendations influence a user’s attitude and behavior toward the personalization agent. For online merchants, this research highlights the trade-off between item sampling and item selection and provides practical guidance on how to steer users toward the attitudes and behaviors that will realize their business goals.