Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

Preferences can change as a consequence of making a hard decision whereby the value of chosen options increases and the value of rejected options decreases. Such choice-induced preference changes have been associated with brain areas detecting choice conflict (anterior cingulate cortex, ACC), updating stimulus value (dorsolateral prefrontal cortex, dlPFC) and supporting memory of stimulus value (hippocampus and ventromedial prefrontal cortex, vmPFC). Here we investigated whether resting-state neuronal activity within these regions is associated with the magnitude of individuals' preference updates. We fitted a dynamic causal model (DCM) to resting-state neuronal activity in the spectral domain (spDCM) and estimated the causal connectivity among core regions involved in preference formation following hard choices. The extent of individuals' choice-induced preference changes were found to be associated with a diminished resting-state excitation between the left dlPFC and the vmPFC, whereas preference consistency was related to a higher resting-state excitation from the ACC to the left hippocampus and vmPFC. Our results point to a model of preference formation during which the dynamic network configurations between left dlPFC, ACC, vmPFC and left hippocampus at rest are linked to preference change or stability.

This paper uses transaction data to estimate how single stock circuit breakers on the London Stock Exchange affect other stocks that remain in continuous trading. This “spillover” effect is estimated by calculating the effect of a trading halt on the market quality of stocks that remain in continuous trading and comparing this with the effect of a stock whose absolute returns are of a magnitude nearly sufficient to trigger a trading halt but do not do so. Market quality is measured using a combination of trading costs, volatility and volume. In the two-month period we study, characterized by a relatively volatile trading environment, we find that circuit breakers lead to a significant improvement in the liquidity, and reduction in the volatility, of stocks that remain in continuous trading. This suggests that — at least over the period covered by our data — single stock circuit breakers can play an important role in reducing the spillover of poor market quality across stocks.

Stock and options markets can disagree about a stock’s value because of informed trading in options and/or price pressure in the stock. The predictability of stock returns based on this cross-market discrepancy in values is especially strong when accompanied by stock price pressure, and it does not depend on trading in options. We argue that option-implied prices provide an anchor for fundamental stock values that helps to distinguish stock price movements resulting from pressure versus news. Overall, our results are consistent with stock price pressure being the primary driver of the option price-based stock return predictability.

Brain-scanning (fMRI) evidence is presented that activity in certain sub-cortical structures of the human brain correlate with changes in expected reward, and with risk. Risk is measured by variance of payoff, as in Markowitz’ theory. These brain structures form part of the dopaminergic system (which consists of the neurons that emit a crucial chemical, namely, dopamine, and the areas to which the dopamine neurons project). The dopaminergic system has been known to regulate reward expectation. We show that it is involved in risk perception as well. As such, our findings support for the human brain what recently had been discovered in the primate brain (using single-neuron analysis instead of fMRI).

We show that benchmark-linked convex incentives can lead risk-averse money managers aware of mispricing to overinvest in overpriced securities. In the model, the managers’ risk-seeking behavior varies in response to the interaction of mispricing with convexity and benchmarking concerns. Convexity effects can exacerbate the manager’s overinvestment in overvalued nonbenchmark securities. In contrast, they potentially offset the benchmarking effects studied in the literature, leading to underinvestment in overpriced benchmark securities. Under correlated mispricing across assets, our model rationalizes positive positions in nonbenchmark, negative risk premium (i.e., “bubble”) securities and “pairs trading” in two overvalued securities. Our findings help explain several empirical puzzles.

Employee matching grant schemes are coordination mechanisms that reduce free-riding by socially conscious employee-donors. Matching schemes coupled with lower take-home pay than offered by non-matching firms will survive capital and labor market competition if employee type is not observable and socially conscious employees are more productive or value working together. Matching can enhance employee welfare and raise more for charity without reducing profits. We document that matching firms have higher labor productivity and are more likely to be ranked as one of the “100 Best” employers. The result is robust to managerial entrenchment concerns and is not confined to the high-tech sector.

Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.

This paper finds evidence that the market for follow-on capital discourages risk taking by venture capital fund managers. The amount of follow-on capital raised by venture capitalists is concave with respect to current fund performance. In addition, managers with less consistent performance are slower, and less likely, to raise a follow-on fund. Venture capitalists adjust their investment strategy to balance fundraising incentives against the incentive to pursue risk provided by carried interest. The findings are consistent with models of career concerns, where an agent's compensation is designed to (partially) offset the implicit incentives created by future employment opportunities.

We examine the impact of rivals’ competitive activities on firms’ quantity-of-capital constraints in 60 countries. Prior work shows that competition increases the costs of debt and equity, which reduce the economic profit from investment. Capital constraints, however, may prevent firms from exploiting all positive NPV projects. Using unique survey data and several econometric techniques, we address endogeneity problems that affect both capital constraints and rivals’ competitive activities. We find that rivals’ competitive activities are positively associated with firms’ capital constraints and are more strongly correlated with capital constraints than banking sector competition. We also show that quantity-of-capital constraints are negatively related to firm growth, incremental to the cost of capital.

Studies of bond return predictability find a puzzling disparity between strong statistical evidence of return predictability and the failure to convert return forecasts into economic gains. We show that resolving this puzzle requires accounting for important features of bond return models such as volatility dynamics and unspanned macro factors. A three-factor model comprising a forward spread, a weighted combination of forward rates, and a macro factor generates notable gains in out-of-sample forecast accuracy compared with a model based on the expectations hypothesis. Such gains in predictive accuracy translate into higher risk-adjusted portfolio returns after accounting for estimation error and model uncertainty. Consistent with models featuring unspanned macro factors, our forecasts of future bond excess returns are strongly negatively correlated with survey forecasts of short rates.

We employ a novel brokerage account data set to investigate which individual investors are the most attentive, how investors allocate their attention, and the relation between investor attention and performance. Attention is positively related to investment performance, at both the portfolio return level and the individual trades level. We provide evidence that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks whose positive performance persists for up to six months. Finally, we show that paying attention is particularly profitable when trading stocks with high uncertainty, but for which a lot of public information is available.