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on Financial Markets |
| By: | Ali Hassan (Aix-Marseille Univ., CNRS, AMSE, Marseille, France); Jean-Baptiste Hasse (Aix-Marseille Univ., CNRS, AMSE, Marseille, France & UCLouvain, LIDAM-LFIN, Louvain-La-Neuve, Belgium); Christelle Lecourt (Aix-Marseille Univ., CNRS, AMSE, Marseille, France) |
| Abstract: | In this paper, we examine the determinants of investor money flows in sustainable mutual funds. Owing to differences in preferences, we posit that ESG investors are more sensitive to mutual fund financial attributes than impact investors are. Using a dataset of 840 actively managed European sustainable equity funds for the period 2018–2025, we find that fund flows are significantly more sensitive to past performance for ESG funds than for impact funds. Our empirical results are in line with impact investor specificity among sustainable investors: the first invest for ESG values, whereas the latter invest with ESG values. Our findings are robust to alternative sustainable classifications, geographical investment areas, investor types and time sampling. |
| Keywords: | Impact investing; Mutual funds; Investor behavior; Cash flows |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2521 |
| By: | Amit Goyal (University of Lausanne; Swiss Finance Institute); Yoshio Nozawa (University of Toronto); Yancheng Qiu (The University of Sydney) |
| Abstract: | Passive funds' increased ownership of corporate bonds reduces the demand to borrow these bonds, thereby easing short-selling constraints in the corporate bond market. This finding contrasts with evidence in the equity market, where passive ownership increases borrowing demand. The difference arises because, in the bond market, short sellers are mainly dealers rather than speculative customers. Since passive ownership compresses credit spreads, the higher bond valuation reduces the buying pressure of active investors and consequently diminishes the need for dealers to borrow bonds for market-making activities. Our results caution against extending the findings and implications in the equity short-selling literature to corporate bonds. |
| Keywords: | Short Sales, Corporate Bonds, Securities Lending |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp25100 |
| By: | Juan C. King; Jose M. Amigo |
| Abstract: | The aim of this paper is the analysis and selection of stock trading systems that combine different models with data of different nature, such as financial and microeconomic information. Specifically, based on previous work by the authors and applying advanced techniques of Machine Learning and Deep Learning, our objective is to formulate trading algorithms for the stock market with empirically tested statistical advantages, thus improving results published in the literature. Our approach integrates Long Short-Term Memory (LSTM) networks with algorithms based on decision trees, such as Random Forest and Gradient Boosting. While the former analyze price patterns of financial assets, the latter are fed with economic data of companies. Numerical simulations of algorithmic trading with data from international companies and 10-weekday predictions confirm that an approach based on both fundamental and technical variables can outperform the usual approaches, which do not combine those two types of variables. In doing so, Random Forest turned out to be the best performer among the decision trees. We also discuss how the prediction performance of such a hybrid approach can be boosted by selecting the technical variables. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02036 |
| By: | Yuichi IKEDA; Hideaki AOYAMA; Tetsuo HATSUDA; Tomoyuki SHIRAI; Taro HASUI; Yoshimasa HIDAKA; Krongtum SANKAEWTONG; Hiroshi IYETOMI; Yuta YARAI; Abhijit CHAKRABORTY; Yasushi NAKAYAMA; Akihiro FUJIHARA; Pierluigi CESANA; Wataru SOUMA |
| Abstract: | This study proposes an artificial intelligence framework to detect price surges in crypto assets by leveraging network features extracted from transaction data. Motivated by the challenges in Anti-Money Laundering, Countering the Financing of Terrorism, and Counter-Proliferation Financing, we focus on structural features within crypto asset networks that may precede extreme market events. Building on theories from complex network analysis and rate-induced tipping, we characterize early warning signals. Granger causality is applied for feature selection, identifying network dynamics that causally precede price movements. To quantify surge likelihood, we employ a Boltzmann machine as a generative model to derive nonlinear indicators that are sensitive to critical shifts in transactional topology. Furthermore, we develop a method to trace back and identify individual nodes that contribute significantly to price surges. The findings have practical implications for investors, risk management officers, regulatory supervision by financial authorities, and the evaluation of systemic risk. This framework presents a novel approach to integrating explainable AI, financial network theory, and regulatory objectives in crypto asset markets. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25113 |
| By: | Carlos Giraldo (Fondo Latinoamericano de Reservas - FLAR); Iader Giraldo-Salazar (Fondo Latinoamericano de Reservas - FLAR); Jose E. Gomez-Gonzalez (Department of Finance, Information Systems, and Economics, City University of New York – Lehman College); Jorge M Uribe (Universitat Oberta de Catalunya) |
| Abstract: | This study explores how banks’ capital ratios respond to government debt-to-GDP shocks and how this response varies with regulatory quality. Using local projections for a large panel of advanced and non-advanced economies, we document that on average, increases in public debt are followed by declines in banks’ capital-to-assets ratios. However, this aggregate trend conceals important heterogeneity. When regulatory quality is introduced as a conditioning variable, capital adjustment becomes state dependent. Banks operating in weaker regulatory environments incorporate fiscal pressure more slowly and may raise capital ratios in the medium term, whereas those in stronger systems record losses earlier and experience an immediate decline in capital, followed by a recovery in advanced economies as conditions stabilize. These results show that institutional quality shapes the transmission of fiscal shocks to bank balance sheets and that simple capital measures capture this adjustment more reliably than risk-weighted ratios. The findings highlight the need to account for fiscal conditions in macroprudential assessments and underscore the importance of supervisory capacity for maintaining bank resilience when public debt increases. |
| Keywords: | bank capital; sovereign risk; public debt; regulatory quality; macroprudential policy; local projections |
| JEL: | G21 G28 E32 E44 H63 |
| Date: | 2025–12–17 |
| URL: | https://d.repec.org/n?u=RePEc:col:000566:021931 |