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on Financial Markets |
| By: | Onur Polat (Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Cankaya, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Dhanashree Somani (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Sayar Karmakar (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA) |
| Abstract: | This study examines the predictive power of multi-scale positive and negative speculative bubbles in equity and energy markets for S&P 500 realized variance across horizons from 1 to 24 months. Using a hierarchical modeling framework and machine learning estimators, the analysis evaluates whether stock and oil bubbles provide incremental information beyond macroeconomic variables and financial uncertainty. Applying Clark and West's (2007) tests for nested model comparisons, the results reveal a hierarchy in predictive content that varies by forecast horizon. At the 1-month horizon, neither stock nor oil bubbles improves forecast accuracy. At the 3-month horizon, oil bubbles emerge as the dominant predictor; the Bayesian Regularized Neural Network (BRNN) estimator achieves a statistically significant improvement when oil bubbles are included with stock bubbles, resulting in a 30.7 percent reduction in mean squared error (MSE). At the 6-month horizon, stock bubbles become more important, with both the Gradient Boosting Machine (GBM) and BRNN estimators showing significant improvements. For longer horizons, oil bubbles remain relevant, but their predictive value depends on the estimator: BRNN captures oil bubble effects at 12 months, while GBM does so at 24 months. These findings highlight the importance of horizonspecific model selection and indicate a complex transmission of speculative shocks across asset classes. |
| Keywords: | Stock Market Realized Variance, Stock and Oil Bubbles, Machine Learning, Forecasting |
| JEL: | C22 C53 G10 Q51 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202611 |
| By: | Karmanpartap Singh Sidhu; Junyi Fan; Maryam Pishgar |
| Abstract: | We utilize FinBERT, a domain-specific transformer model, to parse 6.5 million sentences from 16, 428 S&P 500 quarterly earnings call transcripts (2015-2025) and demonstrate that post-earnings stock returns are not equally affected by all speakers in a conference call. Our section-weighted sentiment, with empirically derived speaker weights (Analyst 49%, CFO 30%, Executive 16%, Other 5%), achieves an out-of-sample Spearman IC of 0.142 versus 0.115 in-sample, generates monthly long-short alpha of 2.03% unexplained by the Fama-French five-factor model (t = 6.49), and remains significant after controlling for standardized unexpected earnings (SUE). FinBERT section-weighted sentiment entirely subsumes the Loughran-McDonald dictionary approach (FinBERT t = 5.90; LM t = 0.86 in the combined specification). Signal decay analysis and cumulative abnormal return charts confirm gradual price adjustment consistent with sluggish assimilation of soft information. All results undergo rigorous out-of-sample validation with an explicit temporal split, yielding improved rather than deteriorated predictive power. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.13260 |
| By: | Zhangying Li (Economics and Management School, Wuhan University); O-Chia Chuang (School of Digital Economics, Hubei University of Economics); Rangan Gupta (Department of Economics, University of Pretoria) |
| Abstract: | The onset of the Russia-Ukraine war in 2022 caused significant fluctuations in global energy markets, particularly in natural gas prices, highlighting the growing importance of natural gas for financial market stability. Using a structural econometric framework, we analyze the dynamic effects of natural gas supply shocks compared to three distinct oil shocks popularly used in the energy economics literature using constant and time-varying parameter local projections model, and associated historical decomposition. Our findings reveal that supply shocks of natural gas has replaced oil as the primary driver of stock market volatility, particularly during the 2022 energy crisis. Additionally, natural gas supply shocks are found to perform better in an out-of-sample forecasting exercise compared to oil supply shocks. These results suggest the need for policymakers and investors to incorporate natural gas price dynamics into financial risk management frameworks for Europe. |
| Keywords: | Natural Gas Price Supply Shocks, Oil price Supply Shocks, Stock Price Volatility, Local Projection, Forecasting |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202609 |
| By: | Pu Cheng; Juncheng Liu; Yunshen Long |
| Abstract: | Predicting real-world events from live market signals demands systems that fuse qualitative news with quantitative order-book dynamics under strict temporal discipline -- a challenge existing benchmarks fail to capture. We present \textbf{PolyBench}, a multimodal benchmark derived from Polymarket that records point-in-time cross-sections of 38, 666 binary prediction markets spanning 4, 997 events, synchronously coupling each snapshot with a Central Limit Order Book (CLOB) state and a real-time news stream. Using PolyBench, we evaluate seven state-of-the-art Large Language Models -- spanning open- and closed-source families -- generating 36, 165 predictions under identical, timestamp-locked market states collected between February 6 and 12, 2026. Our multidimensional framework assesses directional accuracy, our proposed Confidence-Weighted Return (CWR), Annualized Percentage Yield (APY), and Sharpe ratio via realistic order-book execution simulation. The results reveal a pronounced performance divergence: only two of seven models achieve positive financial returns -- MiMo-V2-Flash at \textbf{17.6%} CWR and Gemini-3-Flash at 6.2% CWR -- while the remaining five incur losses despite uniformly high stated confidence. These findings highlight the gap between surface-level language fluency and genuine probabilistic reasoning under live market uncertainty, and establish PolyBench as a contamination-proof, financially-grounded evaluation standard for future LLM research. Our dataset and code available at \underline{\href{https://github.com/Poly Bench/PolyBench}{https://github.com/Poly Bench/PolyBench}}. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14199 |
| By: | Maxime L. D. Nicolas; Fran\c{c}ois Sicard; Marion Laboure; Zixin Sun; Anah\'i Rodr\'iguez-Mart\'inez |
| Abstract: | This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118, 000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.08825 |
| By: | Xiang Ao; Jingxuan Zhang; Xinyu Zhao |
| Abstract: | Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the complex temporal dependencies of corporate financial conditions. This paper proposes a dynamic early warning system integrating economic theory with deep temporal networks. Using Chinese A-share panel data (2014-2024), we employ a hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM to capture long- and short-term financial evolutionary patterns. Rolling-window cross-validation demonstrates our model significantly outperforms static baselines like Logistic Regression and XGBoost. Furthermore, utilizing Explainable AI (XAI), we reveal the temporal dynamics of repurchase decisions: prolonged "undervaluation" serves as the long-term underlying motive, while a sharp increase in "cash flow" acts as the decisive short-term trigger. This study provides a robust deep learning paradigm for financial forecasting and offers dynamic empirical support for classic corporate finance hypotheses. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.09650 |