Essays on Liquidity & Market Rules

The thesis is composed by three chapters. The first chapter, "Time Series Reversal: a payment cycle friction", introduces a novel aggregate reversal strategy that exploits monthly calendar effects. Specifically, I show that the end-of-the-month return of the S&P500 negatively correlates with one-month ahead returns. Contrary to the cross-sectional findings, strategies based on the novel aggregate pattern are extremely cost-effective, easy to implement, cyclical, and do not require short-selling. This novel pattern is consistent with pension funds’ liquidity trading to meet pension payment obligations. The second chapter, "Optimal Tick Size," proposes a model of a limit order book to determine the optimal tick size that maximizes the welfare of market participants. When investors arrive sequentially and supply liquidity by undercutting or queuing behind existing orders, the optimal tick size is a positive function of the asset value and a negative function of trading activity. We use the introduction of MiFID II to empirically show that the new tick size regime based on price and trading activity benefited market participants. Our results suggest that both the European tick size regime and (partially) the 2022 SEC proposal dominate Reg. NMS Rule 612. The last chapter, "Manipulation-Free Trading Mechanisms: Auction Design Approach,"proposes new financial market mechanisms through an auction design approach. We first introduce a simultaneous mechanism as an alternative to dark pools. This mechanism endogenously separates buyers from sellers by using the median price as the clearing outcome, thereby avoiding the transparency and manipulation issues inherent in traditional dark pools. Next, we study efficient mechanisms under information uncertainty and learning. We propose a sequential trading mechanism that progressively announces signals, effectively overcoming the issues of manipulation and speed races associated with standard Limit Order Books

Adoption of artificial intelligence applications in clinical practice: insights from a survey of healthcare organizations in Lombardy, Italy

Background: Artificial intelligence (AI) offers transformative potential in healthcare, yet its adoption is hindered by cultural, organizational, and technological barriers, and little is known about their actual use in clinical practice. The aim of this study was to explore current trends in the adoption of AI applications across healthcare organizations in Lombardy, Italy. Methods: This is a survey study that targeted public and private healthcare organizations in Lombardy and conducted between December 2023 and February 2024, with follow-ups between May and June 2024. It included three sections with up to 22 questions: mapping of clinical AI applications, organizational governance of AI, and perceived adoption barriers. Results: Among the 46 responding organizations, 56 AI applications were identified. Most applications focused on analyzing images or structured health data, and supported diagnostic, prognostic, or treatment optimization activities. Routinely used applications were Conformité Européenne-marked, with radiology being the main clinical area of use. Three distinct approaches emerged. While most organizations (57%) have not yet adopted AI applications, among adopters, 13% are developing AI tools, while 30% exclusively purchase commercial solutions. Conclusions: There is considerable variability in both the types and stages of AI applications adopted in clinical practice by healthcare organizations in Lombardy. In terms of functions, most implementations support diagnostic and prognostic tasks, with strong emphasis on imaging-based tools. Regarding innovation strategies, varying approaches, ranging from exclusively purchasing AI applications to hybrid models that include in-house development, were observed. These findings support broader ecosystem efforts to understand and guide AI implementation in healthcare

Financing infrastructure with Public–Private Partnerships

The paper presents the different alternatives available to finance infrastructure via PPPs in emerging market economie

Rules, discretion, and corruption in procurement: evidence from Italian Government contracting

The benefits of bureaucratic discretion depend on the extent to which it is used forpublic benefit versus exploited for private gain. We study the relationship between discretion and corruption in Italian government procurement auctions, using a confidential database of firms and procurement officials investigated for corruption by Italian enforcement authorities. We show that discretionary procedure auctions (those awarded based onnegotiated rather than open bidding) are associated with corruption only when accompanied by limits to competition. We further show that, while these “corruptible” discretionary auctions are chosen more often by officials who are themselves investigated for corruption,they are used less often in procurement administrations in which at least one official is investigated for corruption. These findings fit with a framework in which more discretion leads to greater efficiency as well as more opportunities for theft, and a central monitor manages this trade-off by limiting discretion for high-corruption procedures and locales. Overall, our results suggest that competition may allow procurement authorities to extractthe benefits of discretion while limiting the resultant risks of abuse

Trust, family firms, and M&A quality

This paper examines the effect of trust on the quality of M&A across family and non-family firms. We find that family firms are associated with better M&A quality than non-family firms and that M&A deals involving high trust are of better quality. When we consider the association of trust, family firms and their interaction, we find that trust is the channel/mechanism through which family firms are associated with better M&A quality. Collectively, these results suggest that trust enables family firms to build long-term relationships with employees, suppliers and customers, and potentially mitigate the Type I agency problems

The future role of government in cybersecurity

In this chapter, we argue that the current government’s narrow perspectives on essential services must be broadened to consider the whole set of critical inter-organisational, network-based, and cross-sectoral relationships that deliver value. We argue the root of this limited view is the origin of much critical infrastructure and cybersecurity policy: the computer science and engineering view of computer security. this chapter reviews the current trends and prevailing approaches of governments to essential services cybersecurity. Second, it explores the challenges that arise from them. Then, it proposes a value-informed cybersecurity theory that can generate new insights and models for cybersecurity framing that are more ‘fit-for-purpose’ for the contemporary, complex reality of essential services. It concludes by drawing a research agenda for Public Administration scholars

Topics in scalable Bayesian posterior estimation

As the complexity and dimensionality of data continue to increase, it is becoming fundamental to develop advanced strategies for statistical inference and to explore their computational properties (Bishop, 2006). This thesis considers Bayesian models, known for their ability to frame prediction and uncertainty within a coherent probabilistic framework. However, achieving accurate estimates of posterior quantities within these models generally requires innovative techniques to accommodate the challenges of modern data analysis. We aim at developing algorithms for exact and approximate posterior estimation exhibiting linear computational cost in the number of parameters, for asymptotic settings where both the numbers of parameters and observations grow to infinity. Such performances are substantially unattainable for state of the art gradient based sampling methods, and are achieved only leveraging the hidden probabilistic structure of the models under consideration. The first and second chapters of this document focus on couplings, a relatively simple probabilistic construction whose potential for unbiased estimation has been recently spotlighted thanks to the work of (Glynn and Rhee, 2014; Jacob et al., 2020). After a brief review on couplings and their applications for unbiased sampling and estimation in Chapter 1, we present in Chapter 2 theoretical results bounding the computational effort required by the coupling construction of Jacob et al. (2020) for certain Gibbs samplers, proving its scalability in a wide range of applications, spanning from crossed random effect to sparse graphical models. Unbiased estimation via couplings therefore presents a promising way to enhance the precision and accuracy of statistical inference, offering insights beyond traditional estimation approaches. Turning to Chapter 3, we cover topics related to variational inference. Variational inference has captured significant attention in the past decades: essentially, it translates the probabilistic problem of finding the posterior distribution as an optimization task (Blei et al., 2017). This chapter not only presents its theoretical foundations but also explores practical implementation and provides results on scalability of the mean field variational approximation for certain large scale hierarchical models. More in detail, assuming the data is randomly generated from a specific distribution, we characterize the rate at which the iterates produced by the coordinate ascent variational inference (CAVI) algorithm converge to a variational minimizer for large scale hierarchical models, proving dimension-free convergence under warm start assumptions. Our work builds upon (Ascolani and Zanella, 2024), highlighting the effectiveness of CAVI in efficiently approximating posterior quantities for models where Gibbs sampling has proved to be effective, given the inherent similarities between these coordinate-wise schemes (Tan and Nott, 2014). Chapter 4 contains some recent advances developed during my visiting period at Warwick University with professor Gareth Roberts. Specifically, we study some properties of Catalytic couplings (Breyer and Roberts, 2001), a coupling procedure well suited for settings where only unnormalized distributions are available and able to couple multiple chains at once. In summary, this dissertation aims at presenting efficient methods in the realm of Bayesian posterior estimation for models with sparse dependencies, such as hierarchical and crossed models, leveraging their probabilistic structure to obtain linear cost estimates. By investigating coupling methods and variational inference, we aim at helping bridge the gap between state-of-the-art statistical procedures and the understanding of their computational properties

Small states and a more nuanced understanding of central government coordination

This article discusses which important answers small states offer to the question of central government coordination. It identifies a gap in the mainstream literature, which focuses predominantly on larger countries, and emphasizes formal coordination mechanisms and coordination problems. By reviewing recent studies on government coordination in various small states, the article explores how coordination practices vary in smaller states: first, compared to larger states, they often experience lower coordination needs. Secondly, less complex government structures and closer personal relations facilitate coordination through informal mechanisms. This contribution challenges the universality of coordination problems and highlights small states’ unique practices that offer valuable insights into efficient coordination strategies adaptable by larger governments. Future research could explore how size intersects with other factors, such as development and colonial legacies, in shaping coordination practices

Essays on the Public Policy of Mobility.

This Ph.D. thesis explores the relationship between public policy and individual mobility across space and time, with a particular focus on vulnerable populations. The three chapters aim not only to assess the effects of public policies on citizens but also to uncover the mechanisms driving these effects. Although the primary context is Brazil, the insights offered are universal. The thesis begins with an examination of historical policies, specifically the Brazilian Nationalization Campaign of the 1930s and 1940s, which sought to assimilate immigrants. It investigates how these policies influenced the human capital development of immigrants and their descendants throughout the twentieth century. The second chapter shifts to contemporary issues, analyzing the impact of poverty alleviation programs, particularly the Conditional Cash Transfer program Bolsa Família, on the social mobility of low-income households in Brazil. It tracks labor market outcomes for children who were beneficiaries in 2005 and examines their continued reliance on social programs in adulthood (2015-2019). The final chapter looks to the future, addressing the challenges posed by climate change to vulnerable populations. It investigates how social benefits, particularly the Bolsa Família program, contribute to the resilience strategies of vulnerable agricultural producers in Brazil between 2015 and 2020