Contributions in Merlin

The more we anticipate a response to a predictable stimulus, the faster we react. This empirical observation has been confirmed and quantified by many investigators suggesting that the processing of behaviorally relevant stimuli is facilitated by probability-based confidence of anticipation. However, the exact neural mechanisms underlying this phenomenon are largely unknown. Here we show that performance changes related to different levels of expectancy originate in dynamic modulation of delta oscillation phase. Our results obtained in rhythmic auditory target detection tasks indicated significant entrainment of the EEG delta rhythm to the onset of the target tones with increasing phase synchronization at higher levels of predictability. Reaction times correlated with the phase of the delta band oscillation at target onset. The fastest reactions occurred during the delta phase that most commonly coincided with the target event in the high expectancy conditions. These results suggest that low-frequency oscillations play a functional role in human anticipatory mechanisms, presumably by modulating synchronized rhythmic fluctuations in the excitability of large neuronal populations and by facilitating efficient task-related neuronal communication among brain areas responsible for sensory processing and response execution.

Standard statistical methods in the empirical economics and finance literature are mostly applicable to data that is aggregated on equally spaced time points. However, a key characteristic of many economic and financial variables is that they occur randomly and are observed irregularly in time. Since the pathbreaking work of Robert Engle in the last years of the twentieth century, there are new approaches that do not require aggregated data but are able to account for their irregular timing nature. These developments were mainly supported by the increasing availability of high frequency transaction data due to the implementation of electronic order recording systems at stock exchanges all over the world. Typically, financial markets data are irregularly observed along the time axis. As pointed out by some authors, time series analysis of fixed time interval data annihilates the natural timing dependence of transaction data and possibly neglects relevant information. Further, the selection of inappropriate equidistant aggregation schemes and the exclusion of data points might lead to misspecifications. Hence, the inclusion of all events in an empirical analysis provides additional information about the timing relation of transaction variables and allows to revisit old and to analyze new questions delivered by financial markets theory. The statistical modeling framework to account for characteristics of irregularly spaced event data is provided by the theory of point processes. A point process statistically describes the history of events that occur consecutively in time. A process consisting of points at which we simultaneously observe variables that mark the points is conceived as marked point process. This thesis' aim is to present new univariate and multivariate empirical point processes applied in the field of financial and monetary econometrics. In particular, this thesis analyzes the following topics. In the second chapter, we suggest a univariate discrete marked point process model for the federal funds rate target and investigate its point and probability forecast performance. Chapter three presents a model for daily return variation. Total daily return variation is disentangled into a continuous and jump variation component. While daily continuous variation is modeled by an autoregressive conditional time series model, irregularly occurring jumps are conceived as a univariate marked point process. Finally, the fourth chapter introduces a new information share that measures the home and foreign market share in price discovery. For this purpose, a multivariate point process based on high frequency transaction data is used.

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It has long been recognized that there is considerable heterogeneity in individual risk taking behavior, but little is known about the distribution of risk taking types. We present a parsimonious characterization of risk taking behavior by estimating a finite mixture model for three different experimental data sets, two Swiss and one Chinese, over a large number of real gains and losses. We find two major types of individuals: In all three data sets, the choices of roughly 80% of the subjects exhibit significant deviations from linear probability weighting of varying strength, consistent with prospect theory. Twenty percent of the subjects weight probabilities near linearly and behave essentially as expected value maximizers. Moreover, individuals are cleanly assigned to one type with probabilities close to unity. The reliability and robustness of our classification suggest using a mix of preference theories in applied economic modeling.

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Continuous Arterial Spin Labeling (CASL) offers the possibility to quantitatively measure the regional cerebral blood flow (rCBF). We demonstrate, for the first time, the feasibility of interleaving Transcranial Magnetic Stimulation (TMS) with CASL at 3 T. Two different repetitive TMS (rTMS) protocols were applied to the motor cortex in 10 subjects and the effect on rCBF was measured using a CASL sequence with separate RF coils for labeling the inflowing blood. Each subject was investigated, using a block design, under 7 different conditions: continuous 2 Hz rTMS (3 intensities: 100%, 110% and 120% resting motor threshold MT), short 10 Hz rTMS trains at 110% MT (8 pulses per train; 3 different numbers of trains per block with 2, 4 and 12 s intervals between trains) and volitional movement (acoustically triggered by 50% MT stimuli). We show robust rCBF increases in motor and premotor areas due to rTMS, even at the lowest stimulation intensity of 100% MT. RCBF exhibited a linear positive dependency on stimulation intensity (for continuous 2 Hz rTMS) and the number of 10 Hz trains in the stimulated M1/S1 as well as in premotor and supplementary motor areas. Interestingly, the 2 different rTMS protocols yielded markedly different rCBF activation time courses, which did not correlate with the electromyographic recordings of the muscle responses. In future, this novel combination of TMS with ASL will offer the possibility to investigate the immediate and after-effects of rTMS stimulation on rCBF, which previously was only possible using PET.

Performance evaluation in research is more and more based on numbers of publications, citations, and impact factors. In the wake of New Public Management output control has been introduced into research governance without taking into account the conditions necessary for this kind of control to work efficiently. It is argued that to evaluate research by output control is counterproductive. It induces to substitute the “taste for science” by a “taste for publication”. Instead, input control by careful selection and socialization serves as an alternative.

Although corporate acquisitions are ubiquitous a large number of M&A fails. One explanation for such failure is the hubris hypothesis for corporate takeovers. A decision maker affected with hubris (or overconfidence) will overestimate his abilities in raising potential synergies and is likely to make investment decisions destroying shareholder wealth. The growing literature on CEO hubris proposes various ways to measure hubris. We present these indicators and discuss possible advantages and drawbacks.

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