We study how generative artificial intelligence (AI) transforms the work of financial analysts. Using the 2023 launch of FactSet's AI platform as a natural experiment, we find that adoption produces markedly richer and more comprehensive reports -- featuring 40% more distinct information sources, 34% broader topical coverage, and 25% greater use of advanced analytical methods -- while also improving timeliness. However, forecast errors rise by 59% as AI-assisted reports convey a more balanced mix of positive and negative information that is harder to synthesize, particularly for analysts facing heavier cognitive demands. Placebo tests using other data vendors confirm that these effects are unique to FactSet's AI integration. Overall, our findings reveal both the productivity gains and cognitive limits of generative AI in financial information production.

Based on the existing literature, this article presents the different ways of choosing the parameters of stochastic volatility models in general, in the context of pricing financial derivative contracts. This includes the use of stochastic volatility inside stochastic local volatility models.

We develop an economic model of decentralised exchanges (DEXs) in which risk-averse liquidity providers (LPs) manage risk in a centralised exchange (CEX) based on preferences, information, and trading costs. Rational, risk-averse LPs anticipate the frictions associated with replication and manage risk primarily by reducing the reserves supplied to the DEX. Greater aversion reduces the equilibrium viability of liquidity provision, resulting in thinner markets and lower trading volumes. Greater uninformed demand supports deeper liquidity, whereas higher fundamental price volatility erodes it. Finally, while moderate anticipated price changes can improve LP performance, larger changes require more intensive trading in the CEX, generate higher replication costs, and induce LPs to reduce liquidity supply.

This article expands Milton Friedman's spending matrix to analyse 'spending efficiency' and 'preference compatibility' across different economic systems against five key outcome criteria. By generalising Friedman's typology, it compares efficiency and freedom as systems shift from laissez-faire capitalism to communism, illustrating a gradual deterioration in their key outcomes. While government intervention is sometimes necessary to address market failures, its role should always be carefully limited to avoid inefficiency and misalignment with individual preferences. The insights may provide guidance for policymakers in designing economic systems and policies that promote both economic prosperity and personal liberty.

This paper presents a novel quantitative approach for comparative economic studies, addressing limitations in current classification methods. Conventional approaches in comparative economics often rely on ad hoc and categorical classifications, leading to subjective judgments and disregarding the continuous nature of the spectrum of economic systems. These can result in subjectivity and significant information loss, particularly for countries with systems near categorical borders. To overcome these shortcomings, the present paper proposes distance-based indices for objective categorization, considering economic foundations and using hard data. Accordingly, the paper introduces institutional similarity indices--Capitalism Similarity Index (CapSI), Communism Similarity Index (ComSI), and Socialism Similarity Index (SocSI)-which reflect countries' positions along the economic system continuum. These indices adhere to mathematical rigor and are grounded in the mathematical fields of real analysis, metric spaces, and distance functions. By classifying 135 countries and creating GIS maps, the practical applicability of the proposed approach is demonstrated. Results show a high explanatory power of the introduced indices, suggesting their beneficial usage in comparative economic studies. The paper advocates for their adoption due to their objectivity and ability to capture structural and institutional nuances without subjective judgments while also considering the continuous nature of the spectrum of economic systems.

Metaheuristic algorithms for cardinality-constrained portfolio optimization require repair operators to map infeasible candidates onto the feasible region. Standard Euclidean projection treats assets as independent and can ignore the covariance structure that governs portfolio risk, potentially producing less diversified portfolios. This paper introduces Covariance-Aware Simplex Projection (CASP), a two-stage repair operator that (i) selects a target number of assets using volatility-normalized scores and (ii) projects the candidate weights using a covariance-aware geometry aligned with tracking-error risk. This provides a portfolio-theoretic foundation for using a covariance-induced distance in repair operators. On S&P 500 data (2020-2024), CASP-Basic delivers materially lower portfolio variance than standard Euclidean repair without relying on return estimates, with improvements that are robust across assets and statistically significant. Ablation results indicate that volatility-normalized selection drives most of the variance reduction, while the covariance-aware projection provides an additional, consistent improvement. We further show that optional return-aware extensions can improve Sharpe ratios, and out-of-sample tests confirm that gains transfer to realized performance. CASP integrates as a drop-in replacement for Euclidean projection in metaheuristic portfolio optimizers.

We study dynamic visual representations as a proxy for investor sentiment about the stock market. Our sentiment index, GIFsentiment, is constructed from millions of posts in the Graphics Interchange Format (GIF) on a leading investment social media platform. GIFsentiment correlates with seasonal mood variations and the severity of COVID lockdowns. It is positively associated with contemporaneous market returns and negatively predicts returns for up to four weeks, even after controlling for other sentiment and attention measures. These effects are stronger among portfolios that are more susceptible to mispricing. GIFsentiment positively predicts trading volume, market volatility, and flows toward equity funds and away from debt funds. Our evidence suggests that GIFsentiment is a proxy for misperceptions that are later corrected.

This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.

This research introduces a novel quantitative methodology tailored for quantitative finance applications, enabling banks, stockbrokers, and investors to predict economic regimes and market signals in emerging markets, specifically Sri Lankan stock indices (S&P SL20 and ASPI) by integrating Environmental, Social, and Governance (ESG) sentiment analysis with macroeconomic indicators and advanced time-series forecasting. Designed to leverage quantitative techniques for enhanced risk assessment, portfolio optimization, and trading strategies in volatile environments, the architecture employs FinBERT, a transformer-based NLP model, to extract sentiment from ESG texts, followed by unsupervised clustering (UMAP/HDBSCAN) to identify 5 latent ESG regimes, validated via PCA. These regimes are mapped to economic conditions using a dense neural network and gradient boosting classifier, achieving 84.04% training and 82.0% validation accuracy. Concurrently, time-series models (SRNN, MLP, LSTM, GRU) forecast daily closing prices, with GRU attaining an R-squared of 0.801 and LSTM delivering 52.78% directional accuracy on intraday data. A strong correlation between S&P SL20 and S&P 500, observed through moving average and volatility trend plots, further bolsters forecasting precision. A rule-based fusion logic merges ESG and time-series outputs for final market signals. By addressing literature gaps that overlook emerging markets and holistic integration, this quant-driven framework combines global correlations and local sentiment analysis to offer scalable, accurate tools for quantitative finance professionals navigating complex markets like Sri Lanka.

This chapter surveys the application of matching theory to school choice, motivated by the shift from neighborhood assignment systems to choice-based models. Since educational choice is not mediated by price, the design of allocation mechanisms is critical. The chapter first reviews theoretical contributions, exploring the fundamental trade-offs between efficiency, stability, and strategy-proofness, and covers design challenges such as tie-breaking, cardinal welfare, and affirmative action. It then transitions to the empirical landscape, focusing on the central challenge of inferring student preferences from application data, especially under strategic mechanisms. We review various estimation approaches and discuss key insights on parental preferences, market design trade-offs, and the effectiveness of school choice policies?

A growing empirical literature suggests that equity-premium predictability is state dependent, with much of the forecasting power concentrated around recessionary periods \parencite{Henkel2011,DanglHalling2012,Devpura2018}. I study U.S. stock return predictability across economic regimes and document strong evidence of time-varying expected returns across both expansionary and contractionary states. I contribute in two ways. First, I introduce a state-switching predictive regression in which the market state is defined in real time using the slope of the yield curve. Relative to the standard one-state predictive regression, the state-switching specification increases both in-sample and out-of-sample performance for the set of popular predictors considered by \textcite{WelchGoyal2008}, improving the out-of-sample performance of most predictors in economically meaningful ways. Second, I propose a new aggregate predictor, the Aligned Economic Index, constructed via partial least squares (PLS). Under the state-switching model, the Aligned Economic Index exhibits statistically and economically significant predictive power in sample and out of sample, and it outperforms widely used benchmark predictors and alternative predictor-combination methods.

In Aarab (2020), I examine U.S. stock return predictability across economic regimes and document evidence of time-varying expected returns across market states in the long run. The analysis introduces a state-switching specification in which the market state is proxied by the slope of the yield curve, and proposes an Aligned Economic Index built from the popular predictors of Welch and Goyal (2008) (augmented with bond and equity premium measures). The Aligned Economic Index under the state-switching model exhibits statistically and economically meaningful in-sample ($R^2 = 5.9\%$) and out-of-sample ($R^2_{\text{oos}} = 4.12\%$) predictive power across both recessions and expansions, while outperforming a range of widely used predictors. In this work, I examine the added value for professional practitioners by computing the economic gains for a mean-variance investor and find substantial added benefit of using the new index under the state switching model across all market states. The Aligned Economic Index can thus be implemented on a consistent real-time basis. These findings are crucial for both academics and practitioners as expansions are much longer-lived than recessions. Finally, I extend the empirical exercises by incorporating data through September 2020 and document sizable gains from using the Aligned Economic Index, relative to more traditional approaches, during the COVID-19 market turbulence.

The growing economic influence of the BRICS nations requires risk models that capture complex, long-term dynamics. This paper introduces the Bank Risk Interlinkage with Dynamic Graph and Event Simulations (BRIDGES) framework, which analyzes systemic risk based on the level of information complexity (zero-order, first-order, and second-order). BRIDGES utilizes the Dynamic Time Warping (DTW) distance to construct a dynamic network for 551 BRICS banks based on their strategic similarity, using zero-order information such as annual balance sheet data from 2008 to 2024. It then employs first-order information, including trends in risk ratios, to detect shifts in banks' behavior. A Temporal Graph Neural Network (TGNN), as the core of BRIDGES, is deployed to learn network evolutions and detect second-order information, such as anomalous changes in the structural relationships of the bank network. To measure the impact of anomalous changes on network stability, BRIDGES performs Agent-Based Model (ABM) simulations to assess the banking system's resilience to internal financial failure and external geopolitical shocks at the individual country level and across BRICS nations. Simulation results show that the failure of the largest institutions causes more systemic damage than the failure of the financially vulnerable or dynamically anomalous ones, driven by powerful panic effects. Compared to this "too big to fail" scenario, a geopolitical shock with correlated country-wide propagation causes more destructive systemic damage, leading to a near-total systemic collapse. It suggests that the primary threats to BRICS financial stability are second-order panic and large-scale geopolitical shocks, which traditional risk analysis models might not detect.

Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.

Fossil gas is sometimes presented as an enabler of variable solar and wind generation beyond 2050, despite being a primary source of greenhouse gas emissions from methane leakage and combustion. We find that balancing solar and wind generation with pumped hydro energy storage eliminates the need for fossil gas without incurring a cost penalty. However, many existing long-term electricity system plans are biased to rely on fossil gas due to using temporal aggregation methods that either heavily constrain storage cycling behaviour or lose track of the state-of-charge, failing to consider the potential of low-cost long-duration off-river pumped hydro, and ignoring the broad suite of near-optimal energy transition pathways. We show that a temporal aggregation method based on 'segmentation' (fitted chronology) closely resembles the full-series optimisation, captures long-duration storage behaviour (48- and 160-hour durations), and finds a near-optimal 100% renewable electricity solution. We develop a new electricity system model to rapidly evaluate millions of other near-optimal solutions, stressing the importance of modelling pumped hydro sites with a low energy volume cost (<US$50 per kilowatt-hour), long economic lifetime (~75 years), and low real discount rate akin to other natural monopolies (<=3%). Almost every region of the world has access to sufficient 50 - 5000 gigawatt-hour off-river pumped hydro options that enable them to entirely decarbonise their future electricity systems.

In this paper, we demonstrate how multiport network theory can be used as a powerful modeling tool in economics. The critical insight is using the port concept to pair the flow of goods (the electrical current) with the agent's incentive (the voltage) in an economic interaction. By building networks of agents interacting through ports, we create models with multiple levels of abstraction, from the macro level down to the micro level. We are thereby able to model complex macroeconomic systems whose dynamical behavior is emergent from the micro level. Using the LTSpice circuit simulator, we then design and analyze a series of example systems that range in complexity from the textbook Robinson Crusoe economy to a model of an entire economy.

This study analyzes the dynamic interactions among the NASDAQ index, crude oil, gold, and the US dollar using a reduced-order modeling approach. Time-delay embedding and principal component analysis are employed to encode high-dimensional financial dynamics, followed by linear regression in the reduced space. Correlation and lagged regression analyses reveal heterogeneous cross-asset dependencies. Model performance, evaluated using the coefficient of determination ($R^2$), demonstrates that a limited number of principal components is sufficient to capture the dominant dynamics of each asset, with varying complexity across markets.

Standard forecast efficiency tests interpret violations as evidence of behavioral bias. We show theoretically and empirically that rational forecasters using optimal regularization systematically violate these tests. Machine learning forecasts show near zero bias at one year horizon, but strong overreaction at two years, consistent with predictions from a model of regularization and measurement noise. We provide three complementary tests: experimental variation in regularization parameters, cross-sectional heterogeneity in firm signal quality, and quasi-experimental evidence from ML adoption around 2013. Technically trained analysts shift sharply toward overreaction post-2013. Our findings suggest reported violations may reflect statistical sophistication rather than cognitive failure.

This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing with a common risk factor. We provide simple and interpretable conditions for conditional default probabilities that imply a comparison of credit portfolio losses in convex order. In the case of threshold models, the ranking of portfolio losses is based on a pointwise comparison of the underlying copulas. Our setting includes as special case the well-known Gaussian copula model but allows for general tail dependencies, which are crucial for modeling credit portfolio risks. Moreover, our results extend the classical parameterized models, such as the industry models CreditMetrics and KMV Portfolio Manager, to a robust setting where individual parameters or the copula modeling the dependence structure can be ambiguous. A simulation study and a real data example under model uncertainty offer evidence supporting the effectiveness of our approach.

Blockchain's economic value lies in enabling financial and economic transactions that do not require trusted, centralized intermediaries. In practice, however, transactions must pass through several intermediaries before being included on-chain. We study empirically whether this process undermines blockchain's stated benefits by assembling a novel dataset of 15,097 non-winning Ethereum blocks--blocks proposed by builders but not ultimately selected for inclusion. We show that 21% of user transactions are delayed: although proposed in some candidate blocks, they are not included in the winning block. Approximately 30% of these delayed transactions are exclusive to a single losing builder, indicating that transaction routing materially affect inclusion outcomes. We further document substantial heterogeneity in execution quality: both the probability of successful execution and the execution price of users' swaps vary across candidate blocks. Finally, we study two arbitrage bots trading between decentralized (DEX) and centralized exchanges (CEX). We document intense competition for the same arbitrage opportunities and estimate that these bots trade USDC/WETH and USDT/WETH on centralized exchanges at prices approximately 2.8 basis points more favorable than contemporaneous Binance prices.

Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. First, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end-to-end data extraction and machine learning credit scoring. Second, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Third, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Finally, we will release the anonymised bank transaction dataset to facilitate further research on MSME financial inclusion within Malaysia's emerging economy.

We study a \emph{QDisCoCirc}-inspired, chunked diagram-to-circuit quantum natural language processing (QNLP) model for three-class sentiment classification of financial texts. In our classical simulations, we keep the Hilbert-space dimension manageable by decomposing each sentence into short contiguous chunks. Each chunk is mapped to a shallow quantum circuit, and the resulting Bloch vectors are used as a sequence of quantum tokens. Simple averaging of chunk vectors ignores word order and syntactic roles. We therefore add a small Transformer encoder over the raw Bloch-vector sequence and attach a CCG-based type embedding to each chunk. This hybrid design preserves physically interpretable semantic axes of quantum tokens while allowing the classical side to model word order and long-range dependencies. The sequence model improves test macro-F1 over the averaging baseline and chunk-level attribution further shows that evidential mass concentrates on a small number of chunks, that type embeddings are used more reliably for correctly predicted sentences. For real-world quantum language processing applications in finance, future key challenges include circuit designs that avoid chunking and the design of inter-chunk fusion layers.

We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), a partially interpretable neural network that replicates the reasoning processes of sell-side analysts by capturing how dispersed investor beliefs are compressed into asset prices through a consensus formation process. By modeling this "bottleneck" to summarize firm- and macro-level information, CB-APM not only predicts future risk premiums of U.S. equities but also links belief aggregation to expected returns in a structurally interpretable manner. The model improves long-horizon return forecasts and outperforms standard deep learning approaches in both predictive accuracy and explanatory power. Comprehensive portfolio analyses show that CB-APM's out-of-sample predictions translate into economically meaningful payoffs, with monotonic return differentials and stable long-short performance across regularization settings. Empirically, CB-APM leverages consensus as a regularizer to amplify long-horizon predictability and yields interpretable consensus-based components that clarify how information is priced in returns. Moreover, regression and Gibbons-Ross-Shanken (GRS)-based pricing diagnostics reveal that the learned consensus representations capture priced variation only partially spanned by traditional factor models, demonstrating that CB-APM uncovers belief-driven structure in expected returns beyond the canonical factor space. Overall, CB-APM provides an interpretable and empirically grounded framework for understanding belief-driven return dynamics.