News sensitivity and the cross-section of stock returns

The paper is the first one outside the high-frequency domain to use sentiment-signed news to directly compare news and no-news stock returns. This is done by estimating whether returns on positive, neutral and negative news days are significantly different from the average daily return for a large sample of US stocks over the period from January 2003 to August 2010. The general results show that positive news days indeed have above-average returns and negative news days returns are below average, while the neutral news days are economically barely distinguishable from the average. The market also proves to be fast and accurate at pricing new information, as there are no signs of drift shortly after news days. On the contrary, a directionally correct and statistically significant movement can be found on the day before the news day. The cross-sectional analysis reveals significant differences in the strength of market reactions between stocks ranked on size, book-to-market or news coverage. The general results however hold across all subsamples and are also not driven by earnings announcements or past stock returns. Moreover, the average news sensitivity is itself a priced source of risk. A portfolio of stocks with high sensitivity to news outperforms a portfolio of stocks with low sensitivity by a statistically and economically significant 0.84% per month. This news premium seems to primarily relate to the high impact of news in situations of general uncertainty

Do Firms Walk the Climate Talk?

Firms talk more about the climate on earnings conference calls when climate matters are more material for a firm, when there is greater shareholder pressure or when it is better prepared for climate-related disclosure. However, there is also large unexplained variation in climate talk. In a global sample, we find that climate talk is negatively related to the change in CO2 emissions in the years after the call. However, this does not hold in the US, individualistic cultures and cultures characterized by short-term horizons. In those settings, investors also react negatively to climate talk. Overall, these results suggest that firms walk the climate talk on average, but the credibility of such talk varies across firms

CEO Clarity

A key task for CEOs is to communicate with analysts and investors about their companies' past performance and prospects in quarterly earnings conference calls. Some CEOs speak fuzzily, frequently using words such as "approximately", "probably", and "maybe." Others rarely use such tentative words. That is, they speak clearly. We show that CEO clarity is a matter of personal style; it is not driven by fundamental uncertainty in the companies' business activity. Analysts and the stock market respond more strongly to earnings news conveyed by clear CEOs. Past performance does not explain the style of a newly appointed CEO. However, when a firm does appoint a more clear-talking CEO, Tobin's Q increases and analyst recommendations become more favorable. Overall, investors and analysts appear to value clear talk

Multi-item capacitated lot-sizing problems with setup times and pricing decisions

We study a multi-item capacitated lot-sizing problem with setup times and pricing (CLSTP) over a finite and discrete planning horizon. In this class of problems, the demand for each independent item in each time period is affected by pricing decisions. The corresponding demands are then satisfied through production in a single capacitated facility or from inventory, and the goal is to set prices and determine a production plan that maximizes total profit. In contrast with many traditional lot-sizing problems with fixed demands, we cannot, without loss of generality, restrict ourselves to instances without initial inventories, which greatly complicates the analysis of the CLSTP. We develop two alternative Dantzig–Wolfe decomposition formulations of the problem, and propose to solve their relaxations using column generation and the overall problem using branch-and-price. The associated pricing problem is studied under both dynamic and static pricing strategies. Through a computational study, we analyze both the efficacy of our algorithms and the benefits of allowing item prices to vary over time. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65027/1/20394_ftp.pd

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Measuring economic uncertainty and its impact on the stock market

This paper proposes a novel measure of economic uncertainty based on the frequency of internet searches. The theoretical motivation is offered by findings in economic psychology that agents respond to increased uncertainty by intensifying their information search. The main advantages of using internet searches are broad reach, timeliness and the fact that they reflect actions, rather than words, which however are not directly related to the stock market. The search-based uncertainty measure compares well against a peer group of alternative indicators and is shown to have a significant relationship with aggregate stock returns and volatility