Transforming Cars into Computers: Interdisciplinary Opportunities for HCI

Road and highway infrastructures are being transformed in anticipation of self-driving vehicles. During the transition to fully autonomous road networks people and driverless cars will interact with each other in mixed traffic situations. Vehicles are currently equipped with two types of communication devices one auditory (a horn) and the other visual (signalling lights). In many instances, human drivers use these devices in combination with embodied interaction such as eye contact and gesture when communicating with other road users. Hence, horn and signalling devices currently in use may not be enough to communicate with others in traffic settings; especially when driverless vehicles become responsible for the main driving activity. Driverless vehicles require new interaction types that support Human-AV interaction in an easy to understand and intuitive way. With the transformation of cars into computers new opportunities for research present themselves to the HCI community

Cross-sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

"We study the performance of conditional asset pricing models and multifactor models in explaining the German cross-section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time-varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book-to-market sorted portfolios about as well as the Fama-French three-factor model and performs best in terms of the Hansen-Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama-French model - despite its generally good cross-sectional performance - is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time-series predictability of the test portfolio returns." Copyright 2007 The Authors Journal compilation (c) 2007 Blackwell Publishing Ltd.