Browsing by Subject "Decision Science"

There are currently 22 states with full or partial disclosure requirements for their electricity suppliers. These requirements differ significantly across states, in terms of the specific information content, structure, and presentation, but all have the potential of increasing customers' awareness about the links between their electricity consumption and air emissions, and perhaps create incentives for utilities to reduce those emissions or for customers to reduce their consumption or to switch to a different electricity supplier.

How effective this policy has been is still unclear. The main criterion for effective communication strategies is that they include relevant information for the readers in a usable form. Information needs as well as the ability to process and apply it vary significantly across individuals. However, people are limited in their information-processing capabilities. Policy makers therefore face the trade-off between a large amount of potentially relevant information that ideally needs to be included on the label on the one hand, and decrease in usability as more information is included on the label, on the other.

This paper examines the design, readability and usability of sample labels from 18 different states with information disclosure requirements. The labels are compared and rated according to how they balance the two main dimensions of label design, information content and usability, demonstrating the difficult trade-off between the two. In addition to this, the labels are analyzed along several key aspects: information load, focus on environmental impact, comparability, understandability, and materiality of information. As part of the analysis, measures for these different aspects of label effectiveness are created. The main finding of the analysis is that there are difficult trade-offs between information content and label usability. However, this trade-off can partially be avoided by carefully designing the labels, easing the cognitive burden of users while still conveying relevant information to the decision maker.

The results of the analysis can help evaluate the various existing disclosure policies, and offer approaches to improve upon them. It will also be shown that while preferred levels of information content are incommensurate with maximum usability, certain structures and form elements succeed at making more complex information content easier to use, improving the overall performance of the labels.

Trading has always been considered more of an art than a science. With the rise of quantitative finance, high-frequency trading, and the demoralized equity market, there is more than ever a need to understand why specific strategies make a profit and others do not. This work focuses on the why part and tries to distill down the art component of quantitative strategy development to more of a science discipline. At the same time, it tries to lay down the theoretical groundwork for further research. Bayesian statistics is well suited for such a problem because of the inherited uncertainty quantification and its synergy with typical decision science.

To the author's best knowledge, most current works focus on a particular strategy and fail to realize that a strategy consists of various moving, intertwined, yet crucial components that require sophisticated statistical methods to disentangle and correctly attribute the "effect" to each element. Without first entangling, the analysis can quickly fail to uncover the valid underlying profit driver and get lost in the weeds.

We used the first chapter to introduce the most crucial concept of gambling, Kelly's Criterion. We highlighted its connection with the well-known log utility function of money and the theory of utility maximization in optimal decision-making. Further theories were also developed and extended around the criterion to make it suited to the equity market. The second and third chapters each dive deeply into a particular area of quantitative finance. Even though people treat these two areas separately in practice, they follow the same underlying principle. The second chapter focuses on portfolio optimization. Starting with the classical mean-variance portfolio, we extend it with the Kelly Criterion and prove two things: the latter guarantees positive growth. At the same time, the former does not, and a trade-off exists between the Sharpe ratio and the growth rate. The third chapter leaps to pairs trading through the lens of Bayesian statistics and the generative stochastic model. Such formulation offers insight into the profit generation structure and the more statistically "correct" way to conduct such trades.