1,720,976 research outputs found
It takes all sorts: the complexity of prediction markets
No full textPrediction markets represent a great tool to harness the wisdom of the crowd and, for this reason, they are used to provide accurate forecasts on great variety of events. However, current models of prediction markets do not capture their full complexity, and fail to give satisfactory explanations of the price formation process and mispricing anomalies. This thesis consists of six separate, yet interconnected papers that address these gaps.The first three papers analyse the favourite-longshot bias, a well known empirical regularity whereby contracts (or bets) on likely events are underpriced, whereas contracts on unlikely events are overpriced. The favourite-longshot bias has been widely observed especially in sports betting markets but, in contrast with other pricing anomalies, it did not disappear over time. In the first paper, we propose the first model that can explain the favourite-longshot bias and other related phenomena in different contexts. To achieve this, we introduce an agent-model in which market participants possess heterogeneous beliefs and risk attitudes, and find that such a model can accurately explain betting markets mispricing. Moreover, we shed new light on the role bookmakers have in generating mispricing, by considering two different strategies bookmakers can adopt to set prices and show that, in contrast to previous results, bookmakers are more likely to be risk minimisers (i.e., balancing the books only depending on demand) than profit maximisers. The second paper builds on the heterogeneous agents model to investigate the impact of transaction costs on mispricing. Our results suggest that transaction costs alone cannot create mispricing, as suggested by previous work, but significantly amplify its magnitude if mispricing exists already. In the third paper, we provide an analysis of the favourite-longshot bias in political prediction market exchanges, and characterise its temporal behaviour. We find that, on average, mispricing is negatively correlated with duration, i.e., the longer the market, the smaller the favourite-lonsghot bias, but, surprisingly, we find that duration is strongly, and positively correlated to the magnitude of the favourite-longshot bias in the last days of trading, and argue that this is caused by herding dynamics.The second part of the thesis continues the analysis of prediction market exchanges. Specifically, the fourth and fifth paper provide a comprehensive list of empirical regularities (or stylised facts) that we find in prediction market. This list comprises stylised facts on price changes, volume, and calendar effects. Overall, we find that prediction markets behave differently than financial markets, but share some common characteristics, especially regarding price changes, with emerging financial markets. In the sixth and last paper, we build on this work to introduce a model that can replicate the statistical properties of prediction markets. To achieve this, we propose a model in which agents belong to a social network, and can interact with each others by exchanging their opinions about the probability of a specific event to occur. We find that such a model is particularly suitable to explain prediction markets dynamics, and that it qualitatively reproduces the empirical properties of price changes even in the worst case scenario, suggesting strong robustness
The temporal evolution of mispricing in prediction markets
No full textWe analyze mispricing in prediction markets, a powerful forecasting tool that harnesses the wisdom of the crowd. We show that prediction market prices exhibit mispricing, and we quantify its temporal evolution. Our results suggest that level of the FLB, averaged over the entire time period, decreases with market duration, but this changes when considering only the last trading days. In that case, we find FLB to be positively correlated with duration. We argue that this type of temporal dynamics of mispricing we observe is consistent with herding behavior
Effects of time horizons on Influence maximization in the voter dynamics
No full textIn this paper we analyze influence maximization in the voter model with an active strategic and a passive influencing party in non-stationary settings. We thus explore the dependence of optimal influence allocation on the time horizons of the strategic influencer. We find that on undirected heterogeneous networks, for short time horizons, influence is maximized when targeting low degree nodes, while for long time horizons influence maximization is achieved when controlling hub nodes. Furthermore, we show that for short and intermediate time scales influence maximization can exploit knowledge of (transient) opinion configurations. More in detail, we find two rules. First, nodes with states differing from the strategic influencer’s goal should be targeted. Second, if only few nodes are initially aligned with the strategic influencer, nodes subject to opposing influence should be avoided, but when many nodes are aligned, an optimal influencer should shadow opposing influence
Statistical properties of volumes and calendar effects in prediction markets
Full text linkPrediction markets have proven to be an exceptional tool for harnessing the "wisdom of the crowd", consequently making accurate forecasts about future events. Motivated by the lack of quantitative means of validations for models of prediction markets, in this paper we analyze the statistical properties of volume as well as the seasonal regularities (i.e., calendar effects) shown by volume and price. To accomplish this, we use a set of 3385 prediction market time series provided by PredictIt. We find that volume, with the exception of its seasonal regularities, possesses different properties than what is observed in financial markets. Moreover, price does not seem to exhibit any calendar effect. These findings suggest a significant difference between prediction and financial markets, and offer evidence for the need of studying prediction markets in more detail.<br/
The stylized facts of prediction markets: analysis of price changes
No full textPrediction markets are a powerful tool to make accurate predictions about the outcome of an event and, for this reason, they attract the interest of researchers and practitioners alike. To date, there exist no means of validation for quantitative models of prediction markets. To address this shortcoming, in this paper we compile a list of empirical regularities (stylized facts) of price changes we find by analyzing daily price changes from 3385 prediction markets on political events, a dataset provided by PredictIt. We find that price changes in prediction markets show characteristics similar to emerging markets, with some small differences
Transmission errors and influence maximization in the voter model
Full text linkIn this paper we analyze the effects of mistakes in opinion propagation in the voter model on strategic influence maximization. We provide numerical results and analytical arguments to show that generally two regimes exist for optimal opinion control: a regime of low transmission errors in which influence maximizers should focus on hub nodes and a large-error regime in which influence maximizers should focus on low-degree nodes. We also develop a degree-based mean-field theory and apply it to random networks with bimodal degree distribution, finding that analytical results for the dependence of regimes on parameters qualitatively agree with numerical results for scale-free networks. We generally find that the regime of optimal hub control is the larger, the more heterogeneous the social network and the smaller the more resources both available to the influencers
The impact of transaction costs on state-contingent claims mispricing
No full textWe analyze the impact that transaction costs have on asset mispricing in state-contingent claims markets. In particular, we examine betting markets,in which, it has been argued, transaction costs cause the favorite-longshotbias, a pricing anomaly analogous to the volatility smile in options markets.By using a heterogeneous agents model, we prove that transaction costs alone cannot cause mispricing. Also, we run agent-based simulations to characterize the response of market prices to increments in transaction costs. We find that transaction costs have a significant impact on market inefficiency, by amplifying existing mispricing both directly, influencing market prices, and indirectly, inducing a non-linear response from the agent
Resisting influence: How the strength of predispositions to resist control can change strategies for optimal opinion control in the voter model
Full text linkIn this paper we investigate influence maximization, or optimal opinion control, in a modified version of the two-state voter dynamics in which a native state and a controlled or influenced state are accounted for. We include agent predispositions to resist influence in the form of a probability with which agents spontaneously switch back to the native state when in the controlled state. We argue that in contrast to the original voter model, optimal control in this setting depends on : For low strength of predispositions optimal control should focus on hub nodes, but for large optimal control can be achieved by focusing on the lowest degree nodes. We investigate this transition between hub and low-degree node control for heterogeneous undirected networks and give analytical and numerical arguments for the existence of two control regimes
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