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on Financial Markets |
By: | Kamila Duraj; Daniela Grunow; Michael Haliassos; Christine Laudenbach; Stephan Siegel |
Abstract: | We revisit the puzzle of limited stock market participation using qualitative methods common in other social sciences but rare in economics. Through in-depth interviews with investors and non-investors in Germany—a high-income country with low market participation—we elicit open-ended reflections on money without mentioning investing upfront. This allows beliefs and barriers to emerge naturally. We analyze these interviews using traditional human-led content analysis, complemented with a large language model (LLM)-based approach. We validate our findings using a representative survey of more than 7, 000 individuals. While many known factors appear, we uncover a pervasive misconception: participation is believed to require selecting “safe” stocks, avoiding “bad” ones, and timing the market through monitoring and frequent trading. This inflates perceived costs and deters participation. Some investors overcome these barriers with support from family, friends, or trusted advisors. Notably, even active investors hold these beliefs, suggesting the misconception influences both entry and behavior in the market. |
Keywords: | stock market participation, qualitative research |
JEL: | G40 G50 G51 G53 |
Date: | 2025 |
URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11980 |
By: | Sophia Kazinnik; Erik Brynjolfsson |
Abstract: | This paper examines how central banks can strategically integrate artificial intelligence (AI) to enhance their operations. Using a dual-framework approach, we demonstrate how AI can transform both strategic decision-making and daily operations within central banks, taking the Federal Reserve System (FRS) as a representative example. We first consider a top-down view, showing how AI can modernize key central banking functions. We then adopt a bottom-up approach focusing on the impact of generative AI on specific tasks and occupations within the Federal Reserve and find a significant potential for workforce augmentation and efficiency gains. We also address critical challenges associated with AI adoption, such as the need to upgrade data infrastructure and manage workforce transitions. |
JEL: | C8 C9 G4 |
Date: | 2025–07 |
URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33998 |
By: | Götz, Martin; Laeven, Luc; Levine, Ross |
Abstract: | We construct a novel panel dataset on insider ownership for about 600 U.S. bank holding companies from 2003 to 2014 and evaluate whether ownership structure influences banks' equity composition and recapitalization decisions around the Global Financial Crisis (GFC). Before the crisis, banks with higher insider ownership relied less on common stock and more on retained earnings. Throughout the sample period, insider ownership changes little within banks. Following the onset of the GFC, banks with larger insider ownership sold significantly less common stock than comparable peers. This effect is more pronounced where insiders enjoy greater private benefits of control, as proxied by insider lending and earnings opacity. The findings suggest insiders are reluctant to dilute their shares and lose those private benefits. These results hold when employing instrumental variables for insider ownership. Our findings imply that ownership structure affects banks' equity issuances during crises, highlighting the importance of considering ownership when designing and evaluating regulatory reforms. |
Keywords: | Ownership Structure, Equity Issuances, Banking, Financial Crises, Regulation |
JEL: | G32 G21 G28 |
Date: | 2025 |
URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:320430 |
By: | Gao, Can; Han, Brandon Yueyang |
Abstract: | We demonstrate the asset pricing implications of investors' belief heterogeneity in the frequency of news arrival and its joint impact with heterogeneous beliefs about news content. Investors trade volatility derivatives against each other to speculate on the rate of news arrival: greater disagreement of this kind gives rise to more extreme derivative positions. When disagreement about news arrival frequency is low, volatility exhibits mean reversion because extreme optimists and pessimists incur substantial wealth losses amid intense market swings. In contrast, high disagreement about the news arrival rate leads to volatility persistence. When news is absent in such environments, volatility sellers dominate, and extreme payoffs are underweighted in the formation of market expectations, resulting in lower implied volatility. In this context, "no news" effectively becomes good news for risky asset valuations. |
Keywords: | News arrival, heterogeneous beliefs, derivatives, volatility |
JEL: | G11 G12 D83 D84 |
Date: | 2025 |
URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:320435 |
By: | Mark A. Carlson; Zack Saravay; Mary Tian |
Abstract: | We examine how primary dealers utilized repo operations conducted by the Federal Reserve from September 2019 until May 2020 and how usage affected dealer borrowing and lending. Using daily dealer-level supervisory data, we find that during normal market conditions, dealers primarily used Fed repo to expand their total repo borrowing and on-lent much of this funding to a broad variety of counterparties. However, during market stress in March 2020, dealers used Fed repo as a substitute for funding from other counterparties and focused their on-lending to affiliated counterparties. Moreover, dealers with more headroom under the Supplementary Leverage Ratio requirement used more of their Fed repo borrowing to provide intermediation in funding markets. Our results underscore the critical role that the Fed's repo operations played, especially in March 2020, by reducing dealer funding stress and enabling dealers to pass on liquidity. |
Keywords: | Federal Reserve; Dealer intermediation; Funding markets; Repo operation; Standing Repo Facility; Leverage ratio |
JEL: | E58 G23 G28 |
Date: | 2025–07–14 |
URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-52 |
By: | Ezra Oberfield; Esteban Rossi-Hansberg; Nicholas Trachter; Derek Wenning |
Abstract: | We study the spatial expansion of banks in response to the banking deregulation of the 1980s and 90s in order to develop a spatial theory of banking. During this period, large banks expanded rapidly, mostly by adding new branches in new locations, while many small banks exited. We document that large banks sorted into the densest markets, but that sorting weakened over time as large banks expanded to more marginal markets in search of locations with a relative abundance of retail deposits. This allowed large banks to reduce their dependence on expensive wholesale funding and grow further. To rationalize these patterns, we propose a theory of multi-branch banks that sort into heterogeneous locations. Our theory yields two forms of sorting. First, span-of-control sorting incentivizes top firms to select the largest markets and smaller banks the more marginal ones. Second, mismatch sorting incentivizes banks to locate in more marginal locations, where deposits are abundant relative to loan demand, to better align their deposits and loans and minimize wholesale funding. Together, these two forms of sorting account well for the sorting patterns we document in the data. |
Keywords: | multi-establishment firms; Spatial Sorting; branches; firm location; Span-of-control model |
JEL: | G21 R32 L22 L23 |
Date: | 2025–06–09 |
URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:101147 |
By: | Oleg Sokolinskiy |
Abstract: | This paper estimates trading costs in the off-the-run Treasury market using comprehensive transactions data and machine learning techniques. The analysis reveals several key findings that enhance the understanding of the off-the-run Treasury market liquidity. First, the indicative bid-ask spread is shown to be a biased measure of liquidity, even when not considering transaction volume. Specifically, bid-ask spreads systematically overstate trading costs of more seasoned Treasuries, and the liquidity of benchmark, on-the-run securities affects how off-the-run bid-ask spreads map to trading costs. Second, the paper demonstrates that trading costs may scale non-monotonically with transaction volume, which suggests selective, opportunistic liquidity-taking. Additionally, transaction size has greater effect on off-the-run securities' trading costs when benchmark, on-the-run liquidity is lower. Finally, indicative bid-ask spreads may notably overstate trading costs for larger orders of relatively less liquid securities. These findings contribute to our understanding of actual liquidity in the off-the-run Treasury market, while highlighting the limitations of a traditional liquidity measure. By providing a more nuanced view of trading costs, this study contributes valuable insights for supporting financial stability and optimal asset allocation. |
Keywords: | Liquidity; Treasury market; Off-the-run; Effective bid-ask spread |
JEL: | G10 G12 |
Date: | 2025–07–07 |
URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-49 |
By: | d'Avernas, Adrien; Vandeweyer, Quentin; Petersen, Damon |
Abstract: | This paper studies how Treasury market dynamics depend on adjustments to the central bank balance sheet. We introduce a dynamic model of Treasury bonds with traditional and shadow banks. In the model, both Treasury and repo market disruptions arise as a joint consequence of three frictions: (i) balance sheet costs, (ii) intraday reserves requirements, and (iii) imperfect substitutability between repo and bank deposits. Our model highlights the critical role of both sides of the central bank’s balance sheet as well as agents’ anticipation of shocks and policy interventions in matching observed market dynamics. JEL Classification: E43, E44, E52, G12 |
Keywords: | basis trade, hedge funds, liquidity risk, repo, reserves, shadow banks |
Date: | 2025–07 |
URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253066 |
By: | Jonathan Benchimol; Sophia Kazinnik; Yossi Saadon |
Abstract: | In this study, we examine the Federal Reserve’s communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, characterized by notable contextual uncertainty. Through comparative analysis, we juxtapose the Fed’s communication during the COVID-19 crisis with its responses during the dot-com and global financial crises, examining content, sentiment, and timing dimensions. Our findings reveal that Fed communication and policy actions were more reactive to the COVID-19 crisis than to previous crises. Additionally, declining sentiment related to financial stability in interest rate announcements and minutes anticipated subsequent accommodative monetary policy decisions. We further document that communicating about UMP has become the “new normal†for the Fed’s Federal Open Market Committee meeting minutes and Chairman’s speeches since the Global Financial Crisis, reflecting an institutional adaptation in communication strategy following periods of economic distress. These findings contribute to our understanding of how central bank communication evolves during crises and how communication strategies adapt to exceptional economic circumstances. |
Keywords: | central bank communication, unconventional monetary policy, financial stability, text mining, COVID-19 |
JEL: | C55 E44 E58 E63 |
Date: | 2025–07 |
URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-38 |
By: | Jihene Arfaoui; Harald Uhlig |
Abstract: | Inspired by the Silicon Valley Bank run and building on Diamond- Dybvig (1993), we develop a model in which asset price fluctuations can trigger bank runs. Liquidation amounts to selling assets at their market price. Depositors can buy and hold the assets after paying an idiosyncratic cost. We characterize the equilibria. We introduce a withdrawal pressure function to distinguish between fundamental runs, driven by market price declines, and self-enforcing runs triggered by depositor panic. Deposit insurance can prevent self-enforcing runs but incurs losses during fundamental runs. Regulatory measures ensuring price resilience reduce run risks, but at the expense of depositor welfare. |
JEL: | E43 E44 G01 G21 G28 |
Date: | 2025–06 |
URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33955 |
By: | Mai Dao; Brandon Tan; Jing Zhou |
Abstract: | Recurring debt ceiling standoffs cause political disruptions and economic costs. We quantify one type of cost which is receiving growing attention: the spillover to short-term funding markets. Using high-frequency aggregate as well as granular money market fund specific data, we find that flows in and out of the Treasury General Account triggered by the debt ceiling mechanism can create large swings in the repo spread and distort the supply of repo funding for the Treasury market. Applying our estimates to the expected debt ceiling lift-off in summer 2025 implies that the repo spread could fluctuate by 20-30 basis points around the lift-off date. A higher level of aggregate bank reserves and overnight reverse repo balance at the Fed can dampen the impact on funding spreads appreciably. |
Keywords: | Repo; Reserves; Treasury General Account; Debt Ceiling |
Date: | 2025–06–27 |
URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/127 |
By: | Shi, Mengjie; Zhang, Yupu; Meinerding, Christoph |
Abstract: | The introduction of the EU Carbon Border Adjustment Mechanism (CBAM) has triggered statistically significant negative stock market responses for firms within the EU. Comparing EU customers that have non-EU suppliers in CBAM-affected industries with their non- treated peers in the control group, we find an extra cumulative abnormal return of up to -1.3 percentage points over our main five-day event window around December 13, 2022. Fur- thermore, we document substantial anticipatory market responses reflecting updated beliefs about broader climate policy developments going forward. This paper is the first to provide empirical evidence of carbon border tax impacts on firm valuations through international supply chains. Our findings contribute to the understanding of climate policy transmission through international trade networks and inform the debate on stranded assets resulting from environmental regulations. |
Keywords: | Carbon border adjustment mechanism, carbon pricing, supply chain, event study, cumulative abnormal returns, trade |
JEL: | G12 G14 G15 Q58 |
Date: | 2025 |
URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:319628 |
By: | Marco Reuter |
Abstract: | This paper presents a novel methodology—leveraging a combination of AI and machine learning to estimate the geographic distribution of international stablecoin flows, overcoming the “anonymity” of crypto assets. Analyzing 2024 stablecoin transactions totaling $2 trillion, our findings show: (i) stablecoin flows are highest in North America ($633bn) and in Asia and Pacific ($519bn). (ii) Relative to GDP, they are most significant in Latin America and the Caribbean (7.7%), and in Africa and the Middle East (6.7%). (iii) North America exhibits net outflows of stablecoins, with evidence suggesting these flows meet global dollar demand, increasing during periods of dollar appreciation against other currencies. Further, we show that the 2023 banking crisis significantly impeded stablecoin flows originating from North America; and finally, offer a comprehensive comparison of our data to the Chainalysis dataset. |
Keywords: | stablecoins; capital flows; capital flight; capital flow management measures (CFMs); crypto assets; currency substitution; dollar demand |
Date: | 2025–07–11 |
URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/141 |
By: | Imad Talhartit (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research); Sanae Ait Jillali (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat); Mounime El Kabbouri |
Abstract: | Capital markets play a fundamental role in the economy by facilitating the flow of funds between investors with capital surpluses and those with financing needs. However, these markets' inherent complexity and high volatility-amplified by economic crises and geopolitical events-make decision-making particularly challenging. In this context, artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has become increasingly relevant for modeling complex financial time series such as stock prices. Among various learning approaches, Long Short-Term Memory (LSTM) networks stand out for their ability to capture long-term dependencies in sequential data. This study compares the predictive performance of LSTM and Artificial Neural Networks (ANN) models, on ten stocks comprising the MADEX index of the Casablanca Stock Exchange, across three forecasting horizons (10, 20, and 30 days). Results demonstrate that the LSTM model consistently outperforms the ANN model in terms of accuracy and trend detection. For instance, over a 30-day horizon, the LSTM correctly predicted 8 out of 10 stocks, compared to only 4 for the ANN. This work is part of a broader research effort aimed at identifying the most effective model for stock price forecasting. Building on the results of this and previous studies, particularly those involving LSTM models optimized using genetic algorithms, future research will explore other models such as Gated Recurrent Units (GRU) and Support Vector Machines (SVM) to further enhance prediction accuracy and robustness. |
Keywords: | Stock price forecasting, Casablanca Stock Exchange, Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), Prediction accuracy |
Date: | 2025–05–09 |
URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05063012 |