When are prediction market prices most informative?

Prediction markets are a popular platform for the elicitation of incentivised crowd predictions. This paper examines the variation in the information contained in prediction market prices by studying Intrade prices on U.S. elections around the release of opinion polls. We find that poll releases stimulate an immediate uptick in trading activity. However, much of this activity involves relatively inexperienced traders, meaning that the price efficiency declines in the immediate aftermath of a poll release, and does not recover until more experienced traders enter the market in the following hours. More generally, this suggests that information releases do not necessarily improve prediction market forecasts, but instead may attract noise traders who temporarily reduce the price efficiency.     

Why are Older Pensioners Poorer?

We show that older male pensioners have substantially lower incomes than younger pensioners. There are a number of possible reasons for this including under-indexation of private pensions and the running down of income producing assets. In fact we find that cohort differences more than account for the lower incomes of older pensioners in the sense that the mean income of older pensioners is actually higher than the mean income of the same cohort of pensioners when they were younger. We explore a number of possible reasons for this and conclude that it is driven by differential mortality between richer and poorer pensioners. We show how this manifests itself in a long time series of cross-sectional datasets.     

A test of the relationship between person–environment fit and cross-cultural adjustment among self-initiated expatriates

Understanding an expatriate's cross-cultural adjustment to the various aspects of their host environment and organization has been a focal point of research for several decades. Person–environment (PE) fit refers to the degree of fit an individual has with various dimensions of their host environment, and past research suggests that successful PE fit positively influences adjustment. However, the strength of these relationships remains under-researched. Drawing upon data gathered from 369 self-initiated expatriate doctors working in Ireland, we examine the strength of the relationship between PE fit and cross-cultural adjustment. Our results suggest that dimensions of PE fit influence cross-cultural adjustment to varying degrees. Thus, person–job needs-supplies fit was the only fit dimension to influence interaction adjustment, while person–job demands abilities influenced both work adjustment and, to a lesser extent, interaction adjustment. Person–organization fit influenced work adjustment, while person–supervisor fit had no relationship with adjustment. These findings have implications for organizations when recruiting and supporting self-initiated expatriates.     

Why Cities? A Response

Why do cities exist? Geographers Allen Scott and Michael Storper recently put the question before the field of urban studies and provided a clear and concise answer in terms of economies of agglomeration and the urban land nexus. I argue that two other basic elements must be added to this duo: the spatial concentration of economic surplus by ruling classes and states and the creation of a built environment or urban landscape. In addition, I take issue with Scott and Storper's neglect of the problem of scale in urban theory and their overly tidy sense of what constitutes a scientific approach to complex phenomena like cities.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

Why are the Pentagon and the CIA in Hollywood?

The CIA and the U.S. Department of Defense are both engaged in programs to influence the content of movies produced by Hollywood studios. Although they claim their only purpose in these ventures is to guarantee the accuracy of how military and intelligence activities are conducted, it is clear that their agenda goes beyond that goal. Their true aims include ensuring that movies project a positive image of the relevant government agencies. However, the CIA and DoD differ in their understanding of what constitutes good publicity. Films scrutinized and supported by military agencies are primarily evaluated according to whether they provide a vehicle for showing the technical and organizational competence of the Pentagon. To that end, films based on comic book characters or extraterrestrial invaders are viewed positively because they show the war‐fighting capacity of the military without having to name any actual enemy. The CIA, by contrast, prefers to support films that enable citizens to develop a stronger sense of patriotism in a world of moral ambiguities. As a result, the CIA is much less fearful of revealing the dark side of its undertakings, as long as a given movie presents a story that shows the value of the CIA in protecting the security of the nation.     

Why are the unemployed in worse health? The causal effect of unemployment on health

We analyse the effect of unemployment on health using information from the German Socio-Economic Panel of the years 1991-2008. To establish a causal effect we rely on fixed-effects methods and plant closures as exogenous entries into unemployment. Although unemployment is negatively correlated with health, we do not find a negative effect of unemployment due to plant closure on health across several health measures (health satisfaction, mental health, and hospital visits). For this subgroup of the unemployed, unemployment does not seem to be harmful and selection effects of ill individuals into unemployment are likely to contribute to the observed overall correlation between poor health and unemployment.     

Disequilibrium models of single family housing prices and transactions: The case of Chicago, 1972–1976

This paper reports a first application of Fair-Jaffee type short-side models of disequilibrium to the intraurban single family housing market in which the housing stock is divided into distinct geographic community areas. Data on single family dwelling transactions and prices in the city of Chicago between 1972–1976 is used to estimate four versions of the Fair-Jaffee type model. The approach allows the simultaneous estimation of the price elasticity of the demand for and offer of existing single family dwellings. The demand elasticity estimate of about −0.5 agrees with other estimates in the literature obtained from equilibrium models. The price elasticity of offer has not been previously estimated for single family dwellings. It is found to be around 2.1. Elasticities are also computed with respect to the mortgage interest rate and the intensity of transactions in the dwelling's community area. The disequilibrium models appear substantially superior to equilibrium specifications. Simulations with the estimated models predict that, in the absence of external shocks, transaction prices and quantities stabilize within 2 years.     

Which are the determinants of green purchase behaviour? A study of Italian consumers

Green purchase behaviour is receiving a growing attention in the academic community, as understanding it is crucial for the growing number of companies developing and marketing green products. In order to provide a broader and novel picture of the phenomenon, this study extends the widely used Theory of Planned Behaviour (TPB) model in several ways, through a large survey of Italian consumers. First, three dimensions of green purchase behaviour are considered, namely, the willingness to pay a premium price, the green purchase frequency and the green purchase satisfaction. Second, several antecedents are considered simultaneously. Third, new (consumer creativity) or so far marginally studied (materialism and green practices) antecedents are included. Fourth, the mediating roles of green purchase satisfaction and willingness to pay a premium price in the link between the considered antecedents and the frequency of green purchase are investigated. Results show that the three dimensions of green purchase behaviour have different antecedents, so highlighting that green purchase behaviour is a multi-faceted phenomenon that should not be studied as a single general concept. Personal norms and value for money emerged to be very relevant predictors. The significant effects of creativity, materialism and green practices provide evidence that extending the TPB model with these three antecedents is useful to more deeply understand green purchase behaviour. Green purchase satisfaction is the strongest predictor of purchase frequency and mediates the effects of personal norms and value for money.     

Which factors capitalize into house prices? A Bayesian averaging approach

This paper investigates the robustness of 33 community-specific explanatory variables for house prices in the Swiss metropolitan area of Zurich using Bayesian model averaging. The analysis suggests a new way to perform hedonic variable selection and provides a minimal list of variables which may serve as a priori constraints when predicting house prices or estimating the effect of other community-specific characteristics in a metropolitan area in a highly developed country. In the context analyzed, the main variables which capitalize with a high posterior probability are location-specific real estate characteristics, municipal taxes and expenditure for culture, health and social well-being. Demographic as well as other socio-economic controls seem to be of minor importance.     

What are the determinants of financial well-being? A Bayesian LASSO approach

The financial well-being (FWB) of individuals is a topic that is becoming increasingly important across a multitude of disciplines. In this study, we use the 2016 National Financial Well-Being Survey administered by the Consumer Financial Protection Bureau to assess the determinants of an individual's FWB. We identify 144 potential covariates that could explain variation in the FWB score of individuals. The statistical methodology of choice is the Bayesian LASSO, which is a covariate selection algorithm that also allows for the importance ranking of covariates. Out of the 144 potential covariates, we find that 26 have 95% credible intervals that do not contain zero. Broadly speaking, the results show that objective measures of financial competency and psychological and sociological factors contribute the bulk of the explanatory power that help explain an individual's FWB score.     

Why should I share my knowledge? A multiple foci of commitment perspective

Knowledge‐intensive firms need to leverage their individual knowledge assets via knowledge sharing to create collective knowledge resources. This process is, however, in the control of the knowledge worker. We explore this personal and emotive quality of knowledge sharing by asking: ‘How does employee commitment impact on knowledge sharing?’ We study professional service firms operating in cross‐boundary environments and examine the impact of commitment to the organisation, profession, team and client on knowledge sharing. The article contributes directly to our understanding of the interrelationship between (a) the types and foci of commitment and (b) bidirectional knowledge sharing.     

Moments structure of ? 1-stochastic volatility models

We consider Taylor??s stochastic volatility model (SVM) when the innovations of the hidden log-volatility process have a Laplace distribution (? ₁ exponential density), rather than the standard Gaussian distribution (? ₂) usually employed. Recently many investigations have employed ? ₁ metric to allow better modeling of the abrupt changes of regime observed in financial time series. However, the estimation of SVM is known to be difficult because it is a non-linear with an hidden markov process. Moreover, an additional difficulty yielded by the use of ? ₁ metric is the not differentiability of the likelihood function. An alternative consists in using a generalized or efficient method-of-moments (GMM/EMM) estimation. For this purpose, we derive here the moments and autocovariance function of such ? ₁-based stochastic volatility models.     

How does changing age distribution impact stock prices? A nonparametric approach

This paper examines whether variations in demographic structure have influenced stock prices. The study employs a nonparametric approach based on the Fourier Flexible Form representation, which relates variations in the entire age distribution to the normalized stock price under a flexible functional form. The main findings of this paper are that there is a significant impact from prime working‐age consumers on the stock price, and that this impact is robust for all G5 countries (France, Germany, Japan, the UK and the USA). These findings survive many robust tests, and are consistent with the predictions from the life‐cycle models. Copyright © 2009 John Wiley & Sons, Ltd.     

How useful are signals? A micro-structure analysis

We analyze how the market processes a signaling event by studying a sample of self-tender offers, events often viewed as signals of firm value. By examining changes in the degree of informed trading, we find asymmetric information costs fall at announcement, remain low throughout the event, and increase at offer expiration. By 1 month following expiration, informed trading returns to a level not significantly different from that prior to the offer. Higher risk firms have significantly larger declines in information asymmetry during the offer. Increases in information asymmetry persist 1 month following expiration for firms with lower pre-offer informed trading.

Bootstrapping the Box–Pierce Q test: A robust test of uncorrelatedness

This paper describes a test of the null hypothesis that the first K autocorrelations of a covariance stationary time series are zero in the presence of statistical dependence. The test is based on the Box–Pierce Q statistic with bootstrap-based P-values. The bootstrap is implemented using a double blocks-of-blocks procedure with prewhitening. The finite sample performance of the bootstrap Q   test is investigated by simulation. In our experiments, the performance is satisfactory for samples of n=500$n=500$. At this sample size, the differences between the empirical and nominal rejection probabilities are essentially eliminated.