Search Results - subject_exact:"Forecasting method"

Advanced time series forecasting of electricity generation in Turkey : a comparative study using LSTM models, ARIMA, and PSO : optimized holt trend

Barak, Mensure Zuhal; Karakas, Esra - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015620374

From data to decision : predictive modeling of oil prices using AutoML and SHAP analysis

Belguith, Rihab - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015620429

Projecting inflation tail risks in a small open economy : some evidence from Singapore

Chow, Hwee-kwan; Lee, Jordan - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015624528

Un sistema de proyección de demanda por efectivo en Chile: actualización y propuesta

Leiva, Nicolás; Medel, Carlos A. - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015625299

Beating the "pros" with a semi-structural model of their own inflation forecasts

Alves, Sergio Afonso Lago; Areosa, Waldyr Dutra; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015625414

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Beating the "pros" with a semi-structural model of their own inflation forecasts

Alves, Sergio Afonso Lago; Areosa, Waldyr Dutra; … - 2026 - Last updated: March 10, 2026

Edition: Last updated: March 10, 2026

Book / Working Paper

Forecasting out-of-time credit scoring model risk

Yoshida Jr., Valter T.; Schiozer, Rafael Felipe; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015625453

Trend inflation and inflation expectations in high dimensional vector autoregressions

Louzis, Dimitrios P. - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015625693

The virtue of transparency in sports analytics : an application to NFL quarterbacks

Czasonis, Megan; Kee, Miles; Kritzman, Mark; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015626687

Diagnosing the trend and bootstrapping the forecasting intervals using a semiparametric ARMA

Schulz, Dominik; Feng, Yuanhua; Gries, Thomas; Fritz, Marlon - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015626944

Estimating, forecasting and backtesting a family of exponential and other GARCH models using the fEGarch package

Schulz, Dominik; Feng, Yuanhua; Peitz, Christian; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015627061

Forecasting economic growth with traditional methods and a simple neural network model

Li, Shujie; Feng, Yuanhua - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015627073

A data-driven merit order : learning a fundamental electricity price model

Ghelasi, Paul; Ziel, Florian - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015627447

Monetary fundamentals and exchange rate forecasting in hyperinflation

Alawin, Mohammad - 2026

The Meese-Rogoff puzzle suggests that exchange rate models rarely outperform a random walk in out-of-sample forecasting. This paper re-examines that puzzle in the context of the German hyperinflation, an environment in which monetary forces dominate economic behavior. Using simple bivariate...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628383

Study on the validity of volatility trading

Castillo, Alberto; Mcwilliams, Jose Manuel Mira - 2026

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628389

How effective is mamba-augmented transformer for stock market price forecasting?

Shuvo, Md Shahria Sarker; Adib, Awsaf Tausif; Emon, Md … - 2026

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628622

Mood in the market : forecasting IPO activity with music sentiment and LSTM

Ding, Qinxu; Guan, Chong; Yu, Yinghui - 2026

We examine whether aggregate "music mood" derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628658

Capturing short- and long-term temporal dependencies using Bahdanau-enhanced fused attention model for financial data : an explainable AI approach

Khansama, Rasmi Ranjan; Priyadarshini, Rojalina; Nanda, … - 2026

Prediction of stock closing price plays a critical role in financial planning, risk management, and informed investment decision-making. In this study, we propose a novel model that synergistically amalgamates Bidirectional GRU (BiGRU) with three complementary attention techniques-Top-k Sparse,...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628752

A comparative APARCH volatility study of international markets

Madega, Fhulufhedzani Justice; Tshisikhawe, T. H.; … - 2026

This paper compares the daily return volatility by four leading international indices: JSE Top 40, FTSE 100, Nikkei 225 and S&P/ASX 200. The return series are modelled in ARMA process, where ARMA(1,3) values are taken for JSE Top 40 and S&P/ASX 200, ARMA(0,0) for FTSE 100, and ARMA(1,2) for...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633278

Estimating treatment effects with limited exogeneity : a machine learning approach to selection bias

Sun, Rui; Chen, Shiyi - 2026

This paper presents a novel method for estimating treatment effects in cases where prior knowledge of the exogeneity of the treatment variable is limited. We employ a machine learning technique, double selection via Lasso, to identify a robust set of control variables without requiring prior...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633562

Artificial intelligence applications and financial forecasting accuracy in banking platforms : evidence from Jordan

Alassuli, Abdalla; Eltweri, Ahmed; Thuneibat, Nawaf Samah; … - 2026

The continued digitalisation of banking systems has raised a demand for more reliable data-based decision-making, in particular when referring to financial forecasts as covered by e-banking applications. This research also investigates the usage of AI-based decision-making systems to facilitate...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633618

Learning and subjective beliefs about good and bad inflation ranges

Ghaderi, Mohammad; Seo, Sang Byung; Shaliastovich, Ivan - 2026

We identify desirable/undesirable inflation outcomes under subjective beliefs by comparing surveybased and risk-adjusted distributions of inflation. Intuitively, investors dislike inflation at both extremes, preferring a range in the middle. This "good inflation" region, which investors...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633652

Consumer sentiment and spending in extreme events

Ardakani, Omid M.; Levine, Lindsay R. - 2026

We examine tail dependence between consumer sentiment and spending during crises, focusing on COVID-19 and the Global Financial Crisis. Using copula models on U.S. monthly data from 2003-2024, we quantify extreme co-movements and find asymmetric tail dependence that intensifies during crises:...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633714

From prediction to insight : understanding drivers of UK tourism demand with machine learning

Dimitriadou, Athanasia; Papadimitriou, Theophilos; … - 2026

This study forecasts inbound tourism demand for the United Kingdom, using monthly data from February 1989 to February 2020. In the empirical analysis, we evaluate and compare the performance of five machine learning models (decision trees, random forests, XGBoost, and support vector regression...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633850

Who saw it coming? : historical experience and the 2021 inflation forecast failure

Stevanović, Dalibor - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015633927

AI-driven bankruptcy prediction in manufacturing SMEs : comparing machine learning techniques with logistic regression

Letkovský, Stanislav; Jenčová, Sylvia; … - 2026

Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015634019

Introducing BISTRO: a foundational model for unconditional and conditional forecasting of macroeconomic time series

Koyuncu, Batuhan; Kwon, Byeungchun; Lombardi, Marco; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015613838

Toward a Standard for Landslide Data : Bridging Gaps in Landslide Susceptibility Modeling and Early Warning Systems

Niyokwiringirwa, Priscilla; Gall, Tjark; Jha, Abhas K. - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015613912

Interpretable multi-model framework for early warning of SME loan delinquency

Akhmetova, Ardak; Shayakhmetova, Assem; Abdurakhmanov, … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614113

Adaptive LASSO-MGARCH for multivariate volatility forecasting

Xu, Yongdeng; Lyu, Juyi; Lu, Wenna - 2026

This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with an application to green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614300

Bayesian causal inference for credit default risk

Pitso, Sello Dalton; Michael, Taryn - 2026

Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer's credit limit truly raises the likelihood of default remains...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614384

On the predictability of the effects of data centers electricity demand shocks

Crampes, Claude; Estache, Antonio - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015614453

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On the predictability of the effects of data centers electricity demand shocks

Crampes, Claude; Estache, Antonio - 2026

Book / Working Paper

The credit gap as a real-time early warning indicator of financial imbalances : a South African perspective

Msiska, Wongani; Sikhosana, Ayanda; Vermeulen, Cobus - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615329

Measuring economic outlook in the news

Beck, Elliot; Eckert, Franziska; Kühne, Linus; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615555

Who shirks at work? : an application of machine learning to time use data

Gimenez-Nadal, José Ignacio; Molina, José Alberto; … - 2026

Worker productivity depends not only on hours worked, but also on how work time is actually used, and time-use evidence shows that non-work at work is non-trivial. This paper provides a data-driven characterization of shirking, and studies which observable characteristics best predict shirking...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015616882

Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting

Das, Abhinav; Schlüter, Stephan; Schneider, Lorenz - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635119

Graph neural network model for cable tunnel cost prediction under high-dimensional construction data

Fang, Ming; Lu, Handong; Lai, Yifeng - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635256

A search-then-forecast transformer framework for mid-term stock price prediction : an empirical case study on the Chinese A-share market

Jieni, Lou - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635513

Demand for dollars : evidence from survey expectations

Ballensiefen, Benedikt; Somogyi, Fabricius; Winterberg, … - 2026 - This version: 26 April 2026

We study the determinants of US dollar demand across market participants and traded instruments using survey-based exchange rate and macroeconomic expectations. Leveraging granular foreign exchange trading data, we show that forward-looking expectations accurately predict both currency returns...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635515

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Demand for dollars : evidence from survey expectations

Ballensiefen, Benedikt; Somogyi, Fabricius; Winterberg, … - 2026

Book / Working Paper

A new model of trend inflation using disaggregates, survey expectations, and uncertainty

Tallman, Ellis W.; Zaman, Saeed - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619060

Improving the sampling strategy for the community innovation survey using machine learning algorithms

Klingwort, Jonas; Berkel, Kees van; Brakel, Jan A. van den - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619569

Unified inference for predictive mean and quantile regressions via empirical likelihood

Cai, Zongwu; Chen, Yifeng; Hong, Seok Young; Tsvetanov, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619835

A robust inference for predictive expectile regression : an IVX-based approach

Cai, Zongwu; Long, Wei - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015619847

Tourism demand forecasting in normal and crisis times : combining bootstrap-aggregating and bayesian approaches

Liu, Xinyang; Liu, Anyu; Chen, Jason Li; Li, Gang; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015604461

Forecasting and managing price volatility in salmon production : a hybrid system using conformal prediction and dynamic hedging

Luna, Manuel; Pérez-Mon, Olaya; Becker, João Luiz - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015604682

Effect of inflation on insurers' main financial indicators with panel data in the US P&C insurance industry

Dionne, Georges; Desjardins, Denise - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015604941

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Effect of inflation on insurers' main financial indicators with panel data in the US P&C insurance industry

Dionne, Georges; Desjardins, Denise - 2025

Book / Working Paper

Makers and takers : the economics of the Kalshi prediction market

Bürgi, Constantin; Deng, Wanying; Whelan, Karl - 2026 - Original version: September 2025, this version: January 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605098

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Makers and takers : the economics of the Kalshi prediction market

Bürgi, Constantin; Deng, Wanying; Whelan, Karl - 2025

Book / Working Paper

Makers and takers : the economics of the Kalshi prediction market

Bürgi, Constantin; Deng, Wanying; Whelan, Karl - 2025

Book / Working Paper

An overview of short-term wind power forecasting : multi-scale decomposition and multi-model deep learning fusion

Mei, Yan; Che, Jinxing; Sun, Qian; Dong, Wei - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605194

Fiscal monitoring with VARs

Cimadomo, Jacopo; Giannone, Domenico; Lenza, Michele; … - 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605598

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Fiscal Monitoring with VARs

Cimadomo, Jacopo; Giannone, Domenico; Lenza, Michele; … - 2026

Book / Working Paper

Machine learning mutual fund flows

Fausch, Jürg; Frigg, Moreno; Ruenzi, Stefan; Weigert, … - 2026 - This draft: May 03, 2025

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605608

Hard to process : atypical firms and the cross-section of expected stock returns

Weibels, Sebastian - 2026 - Current version: January 2026

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015605627