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on Forecasting |
| By: | Anmar Kareem; Alexander Aue |
| Abstract: | This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.06224 |
| By: | Mahdi Goldani |
| Abstract: | The policy environment of countries changes rapidly, influencing macro-level indicators such as the Energy Security Index. However, this index is only reported annually, limiting its responsiveness to short-term fluctuations. To address this gap, the present study introduces a daily proxy for the Energy Security Index and applies it to forecast energy security at a daily frequency.The study employs a two stage approach first, a suitable daily proxy for the annual Energy Security Index is identified by applying six time series similarity measures to key energy related variables. Second, the selected proxy is modeled using the XGBoost algorithm to generate 15 day ahead forecasts, enabling high frequency monitoring of energy security dynamics.As the result of proxy choosing, Volume Brent consistently emerged as the most suitable proxy across the majority of methods. The model demonstrated strong performance, with an R squared of 0.981 on the training set and 0.945 on the test set, and acceptable error metrics . The 15 day forecast of Brent volume indicates short term fluctuations, with a peak around day 4, a decline until day 8, a rise near day 10, and a downward trend toward day 15, accompanied by prediction intervals.By integrating time series similarity measures with machine learning based forecasting, this study provides a novel framework for converting low frequency macroeconomic indicators into high frequency, actionable signals. The approach enables real time monitoring of the Energy Security Index, offering policymakers and analysts a scalable and practical tool to respond more rapidly to fast changing policy and market conditions, especially in data scarce environments. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.05556 |
| By: | So-Yoon Cho; Jin-Young Kim; Kayoung Ban; Hyeng Keun Koo; Hyun-Gyoon Kim |
| Abstract: | Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction. Diffolio employs a denoising network with a hierarchical attention architecture, comprising both asset-level and market-level layers. Furthermore, to better reflect cross-sectional correlations, we introduce a correlation-guided regularizer informed by a stable estimate of the target correlation matrix. This structure effectively extracts salient features not only from historical returns but also from asset-specific and systematic covariates, significantly enhancing the performance of forecasts and portfolios. Experimental results on the daily excess returns of 12 industry portfolios show that Diffolio outperforms various probabilistic forecasting baselines in multivariate forecasting accuracy and portfolio performance. Moreover, in portfolio experiments, portfolios constructed from Diffolio's forecasts show consistently robust performance, thereby outperforming those from benchmarks by achieving higher Sharpe ratios for the mean-variance tangency portfolio and higher certainty equivalents for the growth-optimal portfolio. These results demonstrate the superiority of our proposed Diffolio in terms of not only statistical accuracy but also economic significance. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.07014 |
| By: | Toby Barter; Zheng Gao; Eva Christodoulaki; Jing Chen; John Cartlidge |
| Abstract: | Bond markets respond differently to macroeconomic news compared to equity markets, yet most sentiment models, including FinBERT, are trained primarily on general financial or equity news data. This mismatch is important because bond prices often move in the opposite direction to economic optimism, making general or equity-based sentiment tools potentially misleading. In this paper, we introduce BondBERT, a transformer-based language model fine-tuned on bond-specific news. BondBERT can act as the perception and reasoning component of a financial decision-support agent, providing sentiment signals that integrate with forecasting models. It is a generalisable framework for adapting transformers to low-volatility, domain-inverse sentiment tasks by compiling and cleaning 30, 000 UK bond market articles (2018--2025) for training, validation, and testing. We compare BondBERT's sentiment predictions against FinBERT, FinGPT, and Instruct-FinGPT using event-based correlation, up/down accuracy analyses, and LSTM forecasting across ten UK sovereign bonds. We find that BondBERT consistently produces positive correlations with bond returns, achieves higher alignment and forecasting accuracy than the three baseline models, with lower normalised RMSE and higher information coefficient. These results demonstrate that domain-specific sentiment adaptation better captures fixed income dynamics, bridging a gap between NLP advances and bond market analytics. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.01869 |
| By: | Cansu Isler |
| Abstract: | Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the Growth-at-Risk (GaR) approach by introducing a novel daily sentiment indicator derived from textual analysis of mandatory corporate disclosures (SEC 10-K and 10-Q reports) to forecast downside risks to economic growth. Using the Loughran--McDonald dictionary and a word-count methodology, I compute firm-level tone growth as the year-over-year difference between positive and negative sentiment expressed in corporate filings. These firm-specific sentiment metrics are aggregated into a weekly tone index, weighted by firms' market capitalizations to capture broader, economy-wide sentiment dynamics. Integrated into a mixed-data sampling (MIDAS) quantile regression framework, this sentiment-based indicator enhances the prediction of GDP growth downturns, outperforming traditional financial market indicators such as the National Financial Conditions Index (NFCI). The findings underscore corporate textual data as a powerful and timely resource for macroeconomic risk assessment and informed policymaking. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.04935 |
| By: | Ardelia L. Amardana (Ca’ Foscari University of Venice); Diana Barro (Ca’ Foscari University of Venice); Marco Corazza (Ca’ Foscari University of Venice) |
| Abstract: | Sustainability in financial markets has gained attention. This study addresses it by enhancing portfolio optimization through as additional inputs alongside price data that can improve stock return prediction. Using LSTM models with RMSProp optimizer performs best in consistency of minimizing prediction errors and given the ability to capture complex pattern between price, greenhouse gas (GHG) emissions and environmental scores (E-Scores). This study uses data from the EURO STOXX 50 between 2016 and 2022, focusing on out-of-sample weekly return predictions in 2022. Four model setups are tested: price-only, and price combined with GHG, E-score, or both. Our findings show that incorporating the E-Score improves price and return predictions in several sectors, whereas some sectors show limited benefit, indicating sustainability information may already be priced in. Additionally, in portfolio optimization shows that models including E-Score gives better performance across different holding periods by setting more effective weightings and aligning closely with our benchmark. This results provides further evidence in the following year 2023 and EURO STOXX 50 ESG performance. |
| Keywords: | Sustainable Indicators; Long Short Term Memory; Return Prediction; Portfolio Optimization |
| JEL: | C45 C53 C63 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:25 |
| By: | Leonardo N. Ferreira; Caio Garzeri; Diogo Guillen; Antônio Lima; Victor Monteiro |
| Abstract: | This paper quantifies the “prediction value” of different forms of central bank communication. Combining traditional econometrics and natural language processing, we test how much forecast-improving information can be extracted from the different layers of the Federal Reserve communication. We find that committee-wise communication (statements and minutes) and speeches by the Chair and the Vice Chair improve interest rate forecasts, suggesting that they provide additional information to understand the policy reaction function. However, individual communication beyond the Vice Chair, such as speeches by board members, other FOMC members, and Federal Reserve Bank presidents not sitting in FOMC, is not forecast improving and sometimes even worsens interest-rate forecasts. Based on our theoretical model, we interpret these results as suggesting that the Fed may have overcommunicated, providing excessive noise-inducing communication for forecasting purposes. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:635 |
| By: | Martins, Igor F. B. Martins (Örebro University School of Business); Virbickaitè, Audronè (CUNEF University, Madrid, Spain); Nguyen, Hoang (Linköping University); Hedibert, Freitas Lopes (Insper Institute of Education and Research) |
| Abstract: | This paper proposes a mixed-frequency stochastic volatility model for intraday returns that captures fast and slow level shifts in the volatility level induced by news from both low-frequency variables and scheduled announcements. A MIDAS component describes slow-moving changes in volatility driven by daily variables, while an announcement component captures fast eventdriven volatility bursts. Using 5-minute crude oil futures returns, we show that accounting for both fast and slow level shifts significantly improves volatility forecasts at intraday and daily horizons. The superior forecasts also translate into higher Sharpe ratios using the volatilitymanaged portfolio strategy. |
| Keywords: | Intraday volatility; high-frequency; announcements; MIDAS; oil; sparsity. |
| JEL: | C22 C52 C58 G32 |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_012 |
| By: | James Morley; Jieying Zhang |
| Abstract: | We examine whether inflation is driven by aggregate or sectoral output gaps. The aggregate output gap may not fully capture inflationary pressures because it can obscure sectoral shocks and heterogeneity in propagation to prices. We find that aggregating sectoral output gaps by weights estimated from real-time regressions produces a better fit of the Phillips curve than using the aggregate output gap. We confirm the sectorally-aggregated output gap based on these weights has significant explanatory power for inflation beyond the aggregate output gap and find it performs better in forecasting inflation, although the aggregate output gap retains its own distinct information. |
| Keywords: | sectoral shocks, inflation dynamics, Phillips curve, real-time analysis |
| JEL: | C22 E31 E32 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-58 |
| By: | Pasquale Della Corte (Imperial College Business School; Centre for Economic Policy Research (CEPR)); Can Gao (University of St.Gallen; Swiss Finance Institute; Swisss Institute for Banking and Finance); Daniel P. A. Preve (Singapore Management University); Giorgio Valente (Hong Kong Institute for Monetary and Financial Research (HKIMR)) |
| Abstract: | This paper investigates the long-horizon predictive variance of an international bondstrategy where a U.S. investor holds unhedged positions in constant-maturity long-term foreign bonds funded at domestic short-term interest rates. Using over two centuries of data from major economies, the study finds that predictive variance grows with the investment horizon, driven primarily by uncertainties in interest rate differentials and exchange rate returns, which outweigh mean reversion effects. The analysis, incorporating both observable and unobservable predictors, highlights that unobservable predictors linked to shifts in monetary and exchange rate regimes are the dominant source of long-term risk, offering fresh insights into international bond investment strategies. |
| Keywords: | Currency risk, Long-term bonds, Predictability, Long-term investments |
| JEL: | F31 G12 G15 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2595 |
| By: | Nikolas Anic (Swiss Finance Institute - University of Zurich; Finreon); Andrea Barbon (University of St. Gallen; University of St.Gallen); Ralf Seiz (University of St.Gallen; Finreon); Carlo Zarattini (Concretum Group) |
| Abstract: | This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard longonly momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model's pre-training cutoff. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies. |
| Keywords: | Large Language Models, Momentum Investing, Textual Analysis, News Sentiment, Artificial Intelligence |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2594 |
| By: | Mary A. Burke; Nathaniel R. Nelson |
| Abstract: | Can the Beige Book help to tell us when a recession is coming? This question arises from the Federal Reserve Board of Governors’ description of the publication: “The qualitative nature of the Beige Book creates an opportunity to characterize dynamics and identify emerging trends in the economy that may not be readily apparent in the available economic data.” The report consists of summaries of current economic conditions nationally and in each of the 12 Federal Reserve Districts based on anecdotal information gathered from each District’s business contacts. |
| Keywords: | Beige Book; recession forecasts; financial indicators; Sentiment index |
| JEL: | E3 E37 E58 |
| Date: | 2025–11–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbcq:102052 |
| By: | Zhanyi Jiao; Qiuqi Wang; Yimiao Zhao |
| Abstract: | Backtesting risk measures is a unique and important problem for financial regulators to evaluate risk forecasts reported by financial institutions. As a natural extension to standard (or traditional) backtests, comparative backtests are introduced to evaluate different forecasts against regulatory standard models. Based on recently developed concepts of e-values and e-processes, we focus on how standard and comparative backtests can be manipulated in financial regulation by constructing e-processes. We design a model-free (non-parametric) method for standard backtests of identifiable risk measures and comparative backtests of elicitable risk measures. Our e-backtests are applicable to a wide range of common risk measures including the mean, the variance, the Value-at-Risk, the Expected Shortfall, and the expectile. Our results are illustrated by ample simulation studies and real data analysis. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.05840 |