Search

Providing liquidity in over-the-counter markets is a challenging under-taking, in large part because a market maker does not observe where their competitors quote, nor do they typically know how many rivals they compete with or what the trader's overall liquidity demand is. Optimal pricing strategies can be derived in theory assuming full knowledge of the competitive environment, but these results do not translate into practice where information is incomplete and asymmetric. This paper studies whether artificial intelligence, in the form of multi-armed bandit reinforcement learning algorithms, can be used by liquidity providers to dynamically set spreads using only information that is commonly available to them. We also investigate whether collusive effects can arise when competing liquidity providers all employ such algorithms. Our findings are as follows. In a single-agent setup where only one liquidity provider is optimising pricing in an otherwise static environment, all the algorithms considered are able to locate the theoretically optimal pricing policy, albeit they do so quite inefficiently when compared to a model-based approach. In a multi-agent setting where competing liquidity providers simultaneously and independently use algorithms to optimise pricing, we demonstrate that for one class of algorithms (pseudo) collusion cannot arise, while for another it can arise in certain circumstances and we provide examples where it does. The scenarios where collusive effects appear, however, are fragile, sensitive to the specific configuration and exceedingly unlikely to occur in practice. Moreover, with a modest number of competitors, collusive effects that might otherwise arise in some of the most contrived scenarios are largely or entirely eliminated.

Objectives To assess survival of patients with muscle-invasive bladder cancer (MIBC) who underwent radical cystectomy (RC) with or without neo-adjuvant chemotherapy (NAC) according to the pathological response at RC. Methods 965 patients with MIBC (cT2-4aN0M0) who underwent RC with or without NAC were analyzed. Among the collected data were comorbidity, clinical and pathological tumor stage, tumor grade, nodal status (y)pN, and OS. Case–control matching of 412 patients was performed to compare oncological outcomes. Kaplan–Meier curves were created to estimate OS for patients who underwent RC with or without NAC, and for those with complete response (pCR), partial response (pPR), or residual or progressive disease (PD). Results Patients with a pCR or pPR at RC, with or without NAC, had better OS than patients who had PD (both p values p p = 0.023). Conclusions This study showed that patients with MIBC who underwent NAC with RC had a significant better OS than those who underwent RC only. The proportion of patients with a pCR was higher in those who received NAC and RC than in those who were treated by RC only. The favorable OS rate in the NAC and RC cohort was probably attributed to the higher observed pCR rate.

In this paper, we demonstrate that jumps in financial asset prices are not nearly as common as generally thought, and that they account for only a very small proportion of total return variation. We base our investigation on an extensive set of ultra high-frequency equity and foreign exchange rate data recorded at milli-second precision, allowing us to view the price evolution at a microscopic level. We show that both in theory and practice, traditional measures of jump variation based on low-frequency tick data tend to spuriously attribute a burst of volatility to the jump component thereby severely overstating the true variation coming from jumps. Indeed, our estimates based on tick data suggest that the jump variation is an order of magnitude smaller. This finding has a number of important implications for asset pricing and risk management and we illustrate this with a delta hedging example of an option trader that is short gamma. Our econometric analysis is build around a pre-averaging theory that allows us to work at the highest available frequency, where the data are polluted bymicrostructure noise. We extend the theory in a number of directions important for jump estimation and testing. This also reveals that pre-averaging has a built-in robustness property to outliers in high-frequency data, and allows us to show that some of the few remaining jumps at tick frequency are in fact induced by data-cleaning routines aimed at removing the outliers.

The presence of time varying investment opportunity sets has been documented in the context of international asset allocation, and the economic value associated with these is a topic of lively debate in the academic literature. This paper constructs simple, real-time dynamic international asset allocation strategies based on daily data that exploit the return predictability arising from time varying market integration. Our timing strategies outperform the major (US, UK, Japanese and German) country indices and related portfolios, particularly in down markets. The strategies appear to capture much of the economic value of the return predictability implied by market integration and have many of the characteristics of successful timing strategies.

International audience