Betting and Belief: Prediction Markets and Attribution of Climate Change

Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and a hypothesis advanced by prominent skeptics. We conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect traders' beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the "true" climate model, suggesting that a climate market could be useful for building public consensus.Comment: All code and data for the model is available at http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016 Winter Simulation Conference. IEEE Pres

Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters

Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment.Agent based models; nonlinear expectations; survey data

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW) algorithm. We obtain theoretical guarantees on the optimization error $\epsilon$ and communication cost that do not depend on the total number of combining elements. We further show that the communication cost of dFW is optimal by deriving a lower-bound on the communication cost required to construct an $\epsilon$-approximate solution. We validate our theoretical analysis with empirical studies on synthetic and real-world data, which demonstrate that dFW outperforms both baselines and competing methods. We also study the performance of dFW when the conditions of our analysis are relaxed, and show that dFW is fairly robust.Comment: Extended version of the SIAM Data Mining 2015 pape

Learning from experience in the stock market

We study the dynamics of a Lucas-tree model with finitely lived agents who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic boom-and-bust fluctuations around the rational expectations equilibrium. This heterogeneous-agents economy can be approximated by a representative-agent model with constant-gain learning, where the gain parameter is related to the survival rate. JEL Classification: G12, D83, D84assett pricing, bubbles, Heterogeneous Agents, Learning from experience, OLG

A systematic comparison of professional exchange rate forecasts with judgmental forecasts of novices : Are there substantial differences?

The study at hand deals with the expectations of professional analysts and novices in the context of foreign exchange markets. We analyze the respective forecasting accuracy and our results indicate that there exist substantial differences between professional forecasts and judgmental forecasts of novices. In search of reasonable explanations for the astonishing result, we evaluate the nature of professional and experimental expectations in more detail and find that while professional exchange rate forecasts seem to be biased predictors for the future exchange rates, judgmental forecasts appear to be unbiased. Furthermore, professional forecasters consistently expect a reversal of forgoing exchange rate changes whereas novices expect a continuation of current movements in the short-run and are reversed in the long-run. --Foreign exchange market,forecasting,behavioral finance,anchoring heuristics,judgment,expertise,expectation formation