This study proposes a novel attention-based deep neural network (AttDNN) model specifically designed for predicting mergers and acquisitions (M&A). The model extends existing deep learning frameworks by incorporating M&A-specific features, regularization layers, and an attention mechanism that...

imely assessment of regional economic activity in Ukraine is severely constrained by institutional and data-related limitations. Official regional gross regional product (GRP) statistics are available only at low frequency, are published with substantial delays, and, in the post-2022 period, are...

The stability of global energy markets is fundamental to effective energy strategy, influencing national policy and corporate investment decisions. Extreme volatility, such as during the COVID-19 pandemic, exposes the limitations of conventional risk assessment tools and undermines strategic...

Forecasting hotel occupancy during external shocks is particularly challenging due to their disruptive effects. This study develops a forecasting framework that integrates multisource data using a time-varying parameter state-space model (TVP-SSM). In this framework, search engine data (SED) are...

We design a Bayesian Mixed-Frequency vector autoregression (VAR) model for fiscal monitoring, i.e., to nowcast the government deficit-to-GDP ratio in real time and provide a narrative for its dynamics. The model incorporates both monthly cash and quarterly accrual fiscal indicators, together...

Since 2021, Kalshi has operated as the only federally licensed prediction market in the United States. Using transaction-level data on over 300,000 contracts, we provide the first systematic evidence on its pricing. Kalshi's contract prices are informative and improve in accuracy as markets...

Forecasts that minimize mean squared forecast error (MSE) often exhibit excessive volatility, limiting their practical applicability. We address this accuracy smoothness trade-off by introducing a Multivariate Smooth Sign Accuracy (M-SSA) framework, which extracts smoothed components from...

This study introduces a novel machine learning-based methodology for detecting and forecasting the strength of weak signals in the labor market, using Greece as a case study and utilizing Eurostat time series data (2000-2023). Weak signals, conceptualized as subtle anomalies within otherwise...

Accurate wind power forecasting is crucial for the effective scheduling and scientific management of wind energy, enhancing the safety and reliability of power grids. To handle the inherent intermittency of wind energy, forecasting methodologies have evolved from traditional statistical models...

Inflation dynamics reflect forces operating at different cycles, from short-lived shocks to longterm structural trends. We introduce the sum-of-the-cycles (SOC) method, which exploits this multifrequency structure of inflation for forecasting. SOC decomposes inflation into cyclical components,...

The yield curve is widely regarded as a powerful descriptor of the economy and market expectations. A common approach to its statistical representation relies on a small number of factors summarizing the curve, which can then be used to forecast real economic activity. We argue that optimal...

Purpose - This study aims to forecast the stock market behavior of BRICS nations (Brazil, Russia, India, China and South Africa) using advanced machine learning models. The focus is on identifying market trends, predicting future index prices and analyzing returns. Design/methodology/approach -...

The diverse rating scales used by brokerage firms pose significant challenges for aggregating analyst recommendations in financial research. We develop a momentum-based normalization framework that transforms heterogeneous rating changes into standardized sentiment signals using firm-relative,...

This study examines quantitative investment strategies for Brazilian equity funds, integrating traditional financial performance indicators with machine learning techniques to enhance fund selection. The main objective was to construct and validate predictive models for fund selection. The...

We introduce a simple yet powerful method for enhancing mutual fund performance prediction by combining individual predictors into a composite predictor. This composite approach integrates information from 19 well-established return-based and portfolio holdings-based predictors from the...

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

We study the determinants of US dollar demand across market participants and traded instruments using survey-based exchange rate and macroeconomic expectations. Leveraging granular FX trading data and forward looking expectations, we present three results. First, currency investors increase...

Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality using an autoencoder (ATYP). The model learns...

We test whether forecast bias affects individual investors' stock trading by combining bias measures from laboratory experiments with administrative trade data. Forecast bias is positively associated with past excess returns of purchased stocks: Compared to contrarians, extrapolators purchase...

Recidivism is a persistent challenge for criminal justice systems worldwide, yet evidence from Latin America remains scarce. This study addresses that gap through three contributions. First, it reviews the individual, institutional, and contextual determinants of recidivism, with special...

Professional inflation forecasts contain valuable information but exhibit information frictions. We extract improved forecasts by explicitly modeling these frictions using the US Survey of Professional Forecasters data, and find that forecast rigidity increases systematically with horizon,...

Background: The transition toward Industry 4.0 and Supply Chain 5.0 requires performance measurement frameworks that integrate efficiency, digitalization, and sustainability indicators. Although the SCOR® 4.0 model provides standardized metrics, it lacks predictive capabilities under complex...

Background: This study examines the degree to which cognitive supply chain management (CSCM) indirectly enhances supply chain risk management (SCRM), addressing the lack of specific empirical research concerning the underlying mechanisms of this relationship. Specifically, this study tests the...

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex...

This paper analyzes three forecasting methods for commodity spot prices and applies them to copper prices. The first method uses futures prices from either LME or COMEX. The second method uses analysts' consensus expectations, reported by Bloomberg. The third method jointly uses futures and...

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets-Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)-using daily closes from 8 November 1991-30 January 2025 for IPC and NYSE, and 3 May 1993-30 January 2025 for IBOVESPA. Multi-window...

Public support for policy interventions depends on citizens' beliefs about their likely ef-fects. We examine how individuals form such beliefs by studying their predictions of experimental outcomes in a policy-relevant setting, and why their predictions differ from expert benchmarks. We elicit...

We investigate whether satellite observations of nitrogen dioxide (NO₂) - a short-lived pollutant primarily emitted by fossil fuel combustion - can improve the forecasting of oil demand. After retrieving, cleaning, and aggregating daily satellite data, we integrate NO₂ into a range of...

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated...

Interpretability analysis methods, such as LIME and SHAP, are widely employed to explain the predictions of artificial intelligence models; however, they primarily function as post hoc tools and do not directly quantify the intrinsic interpretability of the models. Although it is commonly...

We introduce a novel methodology, "parametric tilting," for incorporating external information into econometric model-based density forecasts. Unlike traditional entropic tilting, which can generate unrealistic or unstable distributions under certain conditions, parametric tilting ensures more...

The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study...

Landslides claim more than 4,000 lives annually and lead to approximately US$20 billion in economic losses. However, landslide hazard, risk assessment, and early warning systems remain constrained by fragmented, inconsistent, and incomplete data. This study addresses the global data gap by...

This paper describes the Quarterly Macro Forecasting Framework (QMFF), which is a novel approach to macroeconomic policy analysis and forecasting. At the core of this framework is a Quarterly Projection Model, with main behavioral equations quantified as deviations of real variables from their...