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on Financial Markets |
| By: | Carlos Canon; Eddie Gerba; Jozef Barunik |
| Abstract: | This paper studies the impact of funding market frictions on bond prices and market-wide liquidity. Using proprietary transaction-level data on all gilt-backed repo and reverse-repo trades, we demonstrate how the market power of individual dealers and their linkages generate frictions. Specifically, we show that frictions related to market power account for between 0.5 and 1.3 percentage points of bond yield deviation, while the transmission of heterogeneously persistent shocks between dealers accounts for between 2 and 4 percentage points of yield deviation. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.10690 |
| By: | Chia Yean Lim (School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Malaysia Author-2-Name: Wenchuan Sun Author-2-Workplace-Name: School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Malaysia Author-3-Name: Fengqi Guo Author-3-Workplace-Name: CITIC Securities, 150000, Harbin, China Author-4-Name: Sau Loong Ang Author-4-Workplace-Name: Department of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Penang Branch, 11200, Tanjung Bungah, Malaysia Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | " Objective - As the investment environment improves, individuals are increasingly eager to invest their idle funds. Securities companies have become the preferred choice for buying financial products. The current accuracy of stock predictions relies on the comprehensive models used by each securities company, including stock market trading, data, and stock pricing models. However, securities companies have not adequately explored a single suitable model for stock predictions and have rarely assessed the effectiveness of stacking and ensemble methods in improving these predictions. Methodology - This research first explored and proposed the best single-stock prediction model. Next, it combined four individual prediction models to create a stacking model. Findings - The comparison between the single and stacking models demonstrated that the stacking model's prediction accuracy exceeded that of the single model. Therefore, it is recommended that securities companies adopt a stacking-type prediction model to forecast share prices for their investment customers. Novelty - Using a stacking model could improve the accuracy of stock price predictions for investment managers, help users make better decisions, and ultimately enhance the company's earnings by delivering more accurate investment outcomes. Type of Paper - Empirical" |
| Keywords: | Long short-term memory, random forest model, stacking model, stock prediction, support vector machine, XGBoost model. |
| JEL: | F17 F47 |
| Date: | 2026–03–31 |
| URL: | https://d.repec.org/n?u=RePEc:gtr:gatrjs:gjbssr674 |
| By: | William N. Goetzmann; Otto Manninen; James Tyler |
| Abstract: | We examine the historical frequency of stock market booms, crashes, and bubbles in the United States from 1792 to 2024 using aggregate market data and industry-level portfolios. We define a bubble as a large boom followed by a crash that reverses the market’s prior gains. Bubbles are extremely rare. We extend the industry-level analysis of Greenwood, Shleifer, and You (2019) through 2024 and replicate their findings out of sample using Cowles Commission industry data from 1871 to 1938. Booms do not reliably predict crashes, but they do predict higher subsequent volatility, increasing the likelihood of both large gains and large losses. |
| JEL: | G1 G10 G12 G4 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34903 |
| By: | Manan Poddar (Department of Mathematics, London School of Economics) |
| Abstract: | Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to deployment. We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction costs. The ensemble's disagreement at each time step provides a per-time-step confidence measure that is strongly predictive of hedging performance: the learned strategy outperforms the Black-Scholes delta on approximately 80% of paths when model agreement is high, but on fewer than 20% when disagreement is elevated. We propose a CVaR-optimised blending strategy that combines the ensemble's hedge with the classical Black-Scholes delta, weighted by the level of model uncertainty. The blend improves on the Black-Scholes delta by 35-80 basis points in CVaR across several Heston calibrations, and on the theoretically optimal Whalley-Wilmott strategy by 100-250 basis points, with all improvements statistically significant under paired bootstrap tests. The analysis reveals that ensemble uncertainty is driven primarily by option moneyness rather than volatility, and that the uncertainty-performance relationship inverts under weak leverage -- findings with practical implications for the deployment of machine learning in hedging systems. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.10137 |
| By: | William N. Goetzmann; K. Geert Rouwenhorst |
| Abstract: | We use security-level data from the Investors Monthly Manual (IMM) to construct capital-weighted return indexes for the London Stock Exchange over the period 1870–1929. We find a significant and persistent equity risk premium of 3.7% over commercial paper and 4.5% over long-term government bonds, with significant co-movement with GDP growth. Returns decline monotonically with claim seniority: common stocks earn more than preferred shares, which earn more than corporate bonds. Both equity risk premia are highly significant, and the rolling 10-year return spread for stocks minus bonds is positive for every interval in the 60-year sample period. |
| JEL: | G1 G10 G12 G30 G32 N20 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34899 |
| By: | Jing Liu; Maria Grith; Xiaowen Dong; Mihai Cucuringu |
| Abstract: | This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.10559 |
| By: | Ricardo Barahona (BANCO DE ESPAÑA) |
| Abstract: | This paper provides an analysis of Spanish individual pension funds’ financial performance.The evidence presented shows that, like in other investment fund markets around the world, on average individual pension funds do not provide investors with positive risk adjusted returns after subtracting fees. Nevertheless, there is evidence of fund manager skill since, on average, risk adjusted returns before fees are positive and there is persistence in the risk adjusted performance of funds. Additionally, there is evidence that Spanish households respond to fund performance by moving funds to well managed funds. |
| Keywords: | pension funds, defined contribution, mutual funds |
| JEL: | G21 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:bde:opaper:2606e |
| By: | Saumitra N Bhaduri (Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025, India.); Ekta Selarka ((Corresponding author), Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025); Alankrti Aggrwal (Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025) |
| Abstract: | The paper examines the market reaction to the climate policy announcement (COP26) for the Indian listed firms using a novel measure of firm-specific exposure to climate-change developed by Sautner et al. (2023). The findings suggest that, while the overall market reaction is negative, firms with higher climate change exposure experience a significantly muted negative response. In contrast to the prevailing assumption that investors in emerging markets predominantly price exposure to risk, the findings indicate that firms engaging in proactive climate risk management receive favorable response. |
| Keywords: | Event study, Stock market, Climate change, Climate exposure, COP26 |
| JEL: | G14 G28 Q58 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:mad:wpaper:2026-294 |
| By: | Zhenwei Lin (Graduate School of Economics, the University of Tokyo); Masafumi Nakano (GCI Asset Management); Akihiko Takahashi (Faculty of Economics, the University of Tokyo) |
| Abstract: | This paper presents a novel approach to sentiment analysis in the context of investments in the Japanese stock market. Specifically, we begin by creating an original set of keywords derived from news headlines sourced from a Japanese financial news platform. Subsequently, we develop new polarity scores for these keywords, based on market returns, to construct sentiment lexicons. These lexicons are then utilized to guide investment decisions regarding the stocks of companies included in either the TOPIX 500 or the Nikkei 225, which are Japan's representative stock indices. Furthermore, empirical studies validate the effectiveness of our proposed method, which significantly outperforms a ChatGPT-based sentiment analysis approach. This provides strong evidence for the advantage of integrating market data into textual sentiment evaluation to enhance financial investment strategies. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:tky:fseres:2026cf1267 |