This paper reports viability tests of prediction markets with highly granular, monthly UK rainfall and temperature joint outcome spaces. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure.

This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past five years. The markets had horizons of 2 to 12 months. the predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size.

Normative decision theory proves inadequate for modeling human responses to the social-engineering campaigns of Advanced Persistent Threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social-engineering attack vectors, which operate through emotions and peripheral-route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria.We denote the set of possible choice criteria by C. Thus the proposed model reduces to conventional Expected Utility theory when |C_EU| = 1, whilst Dual-Process (thinking fast vs. thinking slow) decision making corresponds to a model with |C_DP| = 2. We consider a more general case with C >= 2, which necessitates careful consideration of how, for a particular choice-taskinstance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decision maker as well as upon the context and the framing of choice options. Whereas existing Signal Detection Theory (SDT) models of phishing detection commingle the different peripheral-route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. Anumber of implications follow immediately from this formulation, ranging from the conditional nature of security-breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the `stepping-stone’ penetration pattern of APT attacks, which has confounded modeling approaches based on normative rationality.

Forward-looking information about climate risks is critical for decision makers, but the provision and accuracy of such information is limited. Innovative prediction-market designs could provide a mechanism to enhance applied climate research in an incentive-compatible way.

The creation of a new business is an act of entrepreneurship. It is also a financial undertaking. Hence it is admissible to apply the apparatus of behavioral finance to study the determinants of business formation. Our results show that aggregate US business formation, nationally and regionally, is jointly predicted by economic fundamentals and sentiment. There is evidence of both ‘pull’ and ‘push’ motives for entrepreneurship. Yet this simple structure does not survive decomposition by payroll propensity. High-payroll-propensity entrepreneurs respond primarily to pull-motive fundamentals, with sentiment accounting for a small fraction of explained variance. Low-payroll-propensity entrepreneurs, on the other hand, respond to both sentiment and fundamentals, representing both pull and push motives, with sentiment accounting for a large fraction of explained variance. Low-payroll-propensity business formation is twice as volatile as high-payroll-propensity entrepreneurship, and similarly to noise-based decision making in behavioral finance, it is substantially driven by sentiment.

We construct a sentiment indicator as the first principal component of thirteen emotion metrics derived from the lyrics and composition of music-chart singles. This indicator performs well, dominating the Michigan Index of Consumer Sentiment and bettering the Baker-Wurgler index in long-horizon regression tests as well as in out-of-sample forecasting tests. The music-sentiment indicator captures both signal and noise. The part associated with fundamentals predicts more distant market returns positively. The second part is orthogonal to fundamentals, and predicts one-month-ahead market returns negatively. This is evidence of noise trading explained by the emotive content of popular music.

Investor sentiment’s effect on asset prices has been studied extensively to date, without delivering consistent results across samples and datasets. We investigate the asset-pricing impacts of eight widely cited investor-sentiment indicators (one direct, six indirect, one composite), within a unified long-horizon regression framework, predicting real NYSE-index returns over horizon lengths of 1, 3, 12, 24, 36, and 48 months. Results reveal that three of the non-composite indicators have consistent predictive power: the Michigan Index of Consumer Sentiment (MICS), IPO volume (NIPO), and the dividend premium (PDND). This finding has implications for the widely cited Baker-Wurgler first principal component (SFPC) composite indicator, which extracts information from the full set of six indirect indicators. As the diffusion-index literature shows, this type of wide-net approach is likely to impound idiosyncratic noise into the composite summary indicator, exacerbating forecasting errors. Therefore we create a new `targeted’ composite indicator from the first principal component of the three indicators that perform well in long-horizon regressions, i.e. MICS, NIPO, and PDND. The resulting targeted composite indicator outperforms SFPC in a market-returns prediction horse race. Whereas SFPC primarily predicts Equally Weighted Returns (EWR) rather than Value Weighted Returns (VWR), our new sentiment indicator performs better than SFPC in predicting both VWR and EWR. This improved performance is due in part to a reduction in overfitting, and in part to incorporation of the direct sentiment indicator MICS.

There is not a uniform view of the link between cyber risk and systemic risk: some assume a direct link whereas others query the connection. Beyond nation states, the vast majority of independent cyber attackers are currently unlikely to have the capability to systemically impact the financial sector. The financial sector has a large number of environmental features which are conducive to a systemic cyber compromise. There are no current examples of systemic cyber risk crystallising and impacting the real economy but this does not prove an absence of risk. We conclude there is a credible case to link cyber risk to systemic risk in the financial sector. Recommendations for future consideration include: further development of the intelligence-led approach to cyber security; policy responses that seek to cut through sectoral, geographical and public/private boundaries; organisations should accept that compromises are likely to happen and therefore prioritise response and recovery activities; undertake further studies to better understand the relationship between data integrity and authenticity, trust in financial services and the potential for real-economy impact via a cyber attack; a specific focus on risks associated with third-party dependencies.

Fintech business models based on distributed ledgers – and their smart-contract variants in particular – offer the prospect of democratizing access to faster, anywhere-accessible, lower cost, reliable-and-secure high-quality financial services. In addition to holding great, economically transformative promise, these business models pose new, little-studied risks and transaction costs. However, these risks and transaction costs are not evident during the demonstration and testing phases of development, when adopters and users are drawn from the community of developers themselves, as well as from among non-programmer fintech evangelists. Hence, when the new risks and transaction costs become manifest – as the fintech business models are rolled out across the wider economy – the consequences may also appear to be new and surprising. The present study represents an effort to get ahead of these developments by delineating risks and transaction costs inherent in distributed-ledger- and smart-contracts-based fintech business models. The analysis focuses on code risk and moral-hazard risk, as well as on mixed-economy risks and the unintended consequences of replicating bricks-and-mortar-generation contract forms within the ultra-low transaction-cost environment of fintech.

Certain classes of system-level risk depend partly on decentralized lay decision making. For instance, an organization’s network security risk depends partly on its employees’ responses to phishing attacks. On a larger scale, the risk within a financial system depends partly on households’ responses to mortgage sales pitches. Behavioral economics shows that lay decision makers typically depart in systematic ways from the normative rationality of Expected Utility (EU), and instead display heuristics and biases as captured in the more descriptively accurate Prospect Theory (PT). In turn psychological studies show that successful deception ploys eschew direct logical argumentation and instead employ peripheral-route persuasion, manipulation of visceral emotions, urgency, and familiar contextual cues. The detection of phishing emails and inappropriate mortgage contracts may be framed as a binary classification task. Signal Detection Theory (SDT) offers the standard normative solution, formulated as an optimal cutoff threshold, for distinguishing between good/bad emails or mortgages. In this paper we extend SDT behaviorally by re-deriving the optimal cutoff threshold under PT. Furthermore we incorporate the psychology of deception into determination of SDT’s discriminability parameter. With the neo-additive probability weighting function, the optimal cutoff threshold under PT is rendered unique under well-behaved sampling distributions, tractable in computation, and transparent in interpretation. The PT-based cutoff threshold is (i) independent of loss aversion and (ii) more conservative than the classical SDT cutoff threshold. Independently of any possible misalignment between individual-level and system-level misclassification costs, decentralized behavioral decision makers are biased toward under-detection, and system-level risk is consequently greater than in analyses predicated upon normative rationality.