Financial statement auditing is conducted under a risk-based evidence approach to obtain reasonable assurance. In practice, auditors often perform additional sampling or related procedures when an initial sample does not provide a sufficient basis for a conclusion. Across jurisdictions, current standards and practice manuals acknowledge such extensions, while the statistical design of sequential audit procedures has not been fully explored. This study formulates audit sampling with additional, sequentially collected items as a sequential testing problem for a finite population under sampling without replacement. We define null and alternative hypotheses in terms of a tolerable deviation rate, specify stopping and decision rules, and formulate exact sequential boundary conditions in terms of finite-population error probabilities. For practical implementation, we calibrate those boundaries by Monte Carlo simulation at least-favorable deviation rates. The exact design yields ex ante control of decision error probabilities, and the simulation-based implementation approximates that design while allowing the computation of expected stopping times. The framework is most naturally suited to attribute auditing and deviation-rate auditing, especially tests of controls, and it can be extended to one-sided, two-stage, and truncated designs.

This research addresses accurate option pricing by employing models beyond the traditional Black-Scholes framework. While Black-Scholes provides a closed-form solution, it is limited by assumptions of constant volatility, no dividends, and continuous price movements. To overcome these limitations, we use Monte Carlo simulation alongside the GARCH model, Heston stochastic volatility model, and Merton jump-diffusion model. The Black-Scholes-Monte Carlo method simulates diverse stock price paths using geometric Brownian motion. The GARCH model forecasts time-varying volatility from historical data. The Heston model incorporates stochastic volatility to capture volatility clustering and skew. The Merton jump-diffusion model adds sudden price jumps via a Poisson process. Results show the Heston model consistently produces estimates closer to market prices, while the Merton model performs well for volatile assets with sudden price movements. The GARCH model provides improved volatility forecasts for future option price prediction. All experiments used live market data from November 2024.

Direct Air Carbon Capture and Storage (DACCS) can mitigate hard-to-abate emissions, e.g. from transport or industry. However, there is a wide variety of cost estimates for DACCS, driven, to a significant extent, by differences in electricity cost. At the same time, there is a notable gap in research that integrates direct air capturing systems into long-term energy system models. We separate direct air capturing, carbon transport, and carbon storage and integrate them into a European capacity expansion model for a fully decarbonised electricity system in 2050. We explore how two dimensions affect the total system costs of DACCS. The first dimension is the availability of CO2 storage locations: In one analysis, storage locations are restricted to offshore storage locations in the North Sea only, i.e. depleted natural gas fields. The alternative analysis comprises suitable storage locations distributed across Europe, including onshore. We find that limiting CO2 storage to North Sea sites increases overall capture costs by approximately 10 %. The second dimension is whether DACCS is analysed as stand-alone or integrated into the electricity system. We differentiate between three alternatives: fully isolated, fully integrated, and retrospectively added to an existing system. We find that neglecting system integration - i.e. treating direct air capture system as a stand-alone technology - increases capture costs by up to 30 %.

The classical tail dependence coefficient (TDC) may fail to capture non-exchangeable features of tail dependence due to its restrictive focus on the diagonal of the underlying copula. To address this limitation, the framework of path-based maximal tail dependence has been proposed, where a path of maximal dependence is derived to capture the most pronounced feature of dependence over all possible paths, and the path-based maximal TDC serves as a natural analogue of the classical TDC along this path. However, the theoretical foundations of path-based tail analyses, in particular the existence and analytical tractability, have remained limited. This paper addresses this issue in several ways. First, we prove the existence of a path of maximal dependence and the path-based maximal TDC when the underlying copula admits a non-degenerate tail copula. Second, we obtain an explicit characterization of the maximal TDC in terms of the tail copula. Third, we show that the first-order asymptotics of a path of maximal dependence is characterized by a one-dimensional optimization involving the tail copula. These results improve the analytical and computational tractability of path-based tail analyses. As an application, we derive the asymptotic behavior of a path of maximal dependence for the bivariate t-copula and the survival Marshall--Olkin copula.

We estimate the effect of cigarette price and tax increases on smoking rates using Eurobarometer survey data from 27 European Union countries between 2012 and 2020. Following a difference-in-differences approach, we compare individuals exposed to large price and tax increases with those in stable price and tax environments. Estimation is based on a difference-in-differences estimator with double machine learning, which relaxes the functional form assumptions typically imposed by parametric approaches such as two-way fixed effects. Our results indicate that tax increases reduce smoking rates among individuals who smoke at least once per month and among daily smokers. The reduction is primarily driven by individuals aged 15-24. We examine the sensitivity of our findings to functional form assumptions and treatment definitions. While estimates are robust to alternative functional form assumptions, they are sensitive to whether the treatment is defined as binary or continuous.

We present an open-source Python framework for modelling cascading physical climate risk in a spatial supply-chain economy. The framework integrates geospatial flood hazards with an agent-based model of firms and households, enabling simulation of both direct asset losses and indirect disruptions propagated through economic networks. Firms adapt endogenously through two channels: capital hardening, which reduces direct damage, and backup-supplier search, which mitigates input disruptions. In an illustrative global network, capital hardening reduces direct losses by 26%, while backup-supplier search reduces supplier disruption by 48%, with both partially stabilizing production and consumption. Notably, firms that are never directly flooded still bear a substantial share of disruption, highlighting the importance of indirect cascade effects. The framework provides a reproducible platform for analyzing systemic physical climate risk and adaptation in economic networks.

We estimate the impact of environmental law enforcement on violence in the Brazilian Amazon. The introduction of the Real-Time Deforestation Detection System (DETER), which enabled the government to monitor deforestation in real time and issue fines for illegal clearing, significantly reduced homicides in the region. To identify causal effects, we exploit exogenous variation in satellite monitoring generated by cloud cover as an instrument for enforcement intensity. Our estimates imply that the expansion of state presence through DETER prevented approximately 1,477 homicides per year, a 15\% reduction in homicides. These results show that a replicable environmental enforcement policy produces social benefits.

Recent studies document strong empirical support for multifactor models that aim to explain the cross-sectional variation in corporate bond expected excess returns. We revisit these findings and provide evidence that common factor pricing in corporate bonds is exceedingly difficult to establish. Based on portfolio- and bond-level analyses, we demonstrate that previously proposed bond risk factors, with traded liquidity as the only marginal exception, do not have any incremental explanatory power over the corporate bond market factor. Consequently, this implies that the bond CAPM is not dominated by either traded- or nontraded-factor models in pairwise and multiple model comparison tests.

This paper introduces a novel generative framework for synthesising forward-looking, càdlàg stochastic trajectories that are sequentially consistent with time-evolving path-law proxies, thereby incorporating anticipated structural breaks, regime shifts, and non-autonomous dynamics. By framing path synthesis as a sequential matching problem on restricted Skorokhod manifolds, we develop the \textit{Anticipatory Neural Jump-Diffusion} (ANJD) flow, a generative mechanism that effectively inverts the time-extended Marcus-sense signature. Central to this approach is the Anticipatory Variance-Normalised Signature Geometry (AVNSG), a time-evolving precision operator that performs dynamic spectral whitening on the signature manifold to ensure contractivity during volatile regime shifts and discrete aleatoric shocks. We provide a rigorous theoretical analysis demonstrating that the joint generative flow constitutes an infinitesimal steepest descent direction for the Maximum Mean Discrepancy functional relative to a moving target proxy. Furthermore, we establish statistical generalisation bounds within the restricted path-space and analyse the Rademacher complexity of the whitened signature functionals to characterise the expressive power of the model under heavy-tailed innovations. The framework is implemented via a scalable numerical scheme involving Nyström-compressed score-matching and an anticipatory hybrid Euler-Maruyama-Marcus integration scheme. Our results demonstrate that the proposed method captures the non-commutative moments and high-order stochastic texture of complex, discontinuous path-laws with high computational efficiency.

Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early outperformance within YC batches. Using the YC W26 batch as a case study (196 startups), we measure outperformance with a Pre-Demo Day Score, a KPI combining publicly available traction signals and web visibility. This short-term metric enables rapid evaluation of forecasting models. As a baseline, we take Google mentions prior to the YC W26 application deadline, a simple proxy for prior brand recognition, recovering 6 of 11 top performers at YC Demo Day (55% recall). YC Bench provides a live benchmark for studying startup success forecasting, with iteration cycles measured in months rather than years. Code and Data are available on GitHub: https://github.com/benstaf/ycbench

This study investigates how entrepreneurial orientation (EO) affects governance of SaaS platforms in SMEs, including strategy alignment and long-term governance performance. This study uses SaaS as a hybrid governance model to examine how transaction cost variables affect strategic alignment and how EO moderates these associations. The research uses multi-study design. Study 1 examined 180 UK and US entrepreneurs' survey data using PLS-SEM with reflecting constructs. Study 2 used a quasi-experimental approach using a secondary dataset from 238 European start-ups to operationalize variables using industry-based indicators. The study found an inverted U-shaped association between human asset specificity, SaaS usage frequency, and SMEs' strategic objectives. Risk-taking deepens the link between human asset distinctiveness and SaaS strategic alignment, while proactiveness strengthens the link to long-term success. Both studies show that SaaS strategic alignment has an inverted U-shaped connection with long-term performance, suggesting that excessive SaaS dependence may harm governance-enabled strategic outcomes. This paper introduces SaaS as a hybrid governance paradigm and examines its strategic influence on SMEs, utilizing transaction cost theory and EO perspectives. It shows the non-linear effects of SaaS adoption on strategic alignment and performance, emphasizing entrepreneurial decision-making in digital technology adoption.

Economic complexity - a group of dimensionality-reduction methods that apply network science to trade data - represented a paradigm shift in development economics towards materializing the once-intangible concept of capabilities as inferrable and quantifiable. Measures such as the Economic Complexity Index (ECI) and the Product Space have proven their worth as robust estimators of an economy's subsequent growth; less obvious, however, is how they have come to be so. Despite ECI drawing its micro-foundations from a combinatorial model of capabilities, where a set of homogeneous capabilities combine to form products and the economies which can produce them, such a model is consistent with neither the fact that distinct product classes draw on distinct capabilities, nor the interrelations between different products in the Product Space which so much of economic complexity is based upon. In this paper, we extend the combinatorial model of economic complexity through two innovations: an underlying network which governs the relatedness between capabilities, and a production function which trades the original binary specialization function for a fine-grained, product-level output function. Using country-product trade data across 216 countries, 5000 products and two decades, we show that this model is able to accurately replicate both the characteristic topology of the Product Space and the complexity distribution of countries' export baskets. In particular, the model bridges the gap between the ECI and capabilities by transforming measures of economic complexity into direct measures of the capabilities held by an economy - a transformation shown to both improve the informativeness of the Economic Complexity Index in predicting economic growth and enable an interpretation of economic complexity as a proxy for productive structure in the form of capability substitutability.

Traditional threshold-based stock networks suffer from subjective parameter selection and inherent limitations: they constrain relationships to binary representations, failing to capture both correlation strength and negative dependencies. To address this, we introduce statistically validated correlation networks that retain only statistically significant correlations via a rigorous t-test of Pearson coefficients. We then propose a novel structure termed the largest strong correlation balanced module (LSCBM), defined as the maximum-size group of stocks with structural balance (i.e., positive edge-sign products for all triplets) and strong pairwise correlations. This balance condition ensures stable relationships, thus facilitating potential hedging opportunities through negative edges. Theoretically, within a random signed graph model, we establish LSCBM's asymptotic existence, size scaling, and multiplicity under various parameter regimes. To detect LSCBM efficiently, we develop MaxBalanceCore, a heuristic algorithm that leverages network sparsity. Simulations validate its efficiency, demonstrating scalability to networks of up to 10,000 nodes within tens of seconds. Empirical analysis demonstrates that LSCBM identifies core market subsystems that dynamically reorganize in response to economic shifts and crises. In the Chinese stock market (2013-2024), LSCBM's size surges during high-stress periods (e.g., the 2015 crash) and contracts during stable or fragmented regimes, while its composition rotates annually across dominant sectors (e.g., Industrials and Financials).

Can large language models (LLMs) learn a decision maker's preferences from observed choices and generate preference-consistent recommendations in new situations? We propose a portable Simulate-Recommend-Evaluate framework that tests preference learning from revealed-choice data by comparing LLM recommendations with optimal choices implied by known preference primitives. We apply the framework to choice under uncertainty using the disappointment aversion model. Recommendation accuracy improves as models observe more choices, but learning is heterogeneous across preference types and LLMs: GPT learns risk aversion better than disappointment aversion, Gemini performs best in high disappointment-aversion regions, and Claude shows the broadest effective learning across parameter regions.