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on Financial Markets |
| By: | Hongyang Yang; Xiao-Yang Liu; Qingwei Wu |
| Abstract: | Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check all S&P 500 stocks (the Standard & Poor's 500). In this paper, we propose a practical scheme that recommends stocks from S&P 500 using machine learning. Our basic idea is to buy and hold the top 20% stocks dynamically. First, we select representative stock indicators with good explanatory power. Secondly, we take five frequently used machine learning methods, including linear regression, ridge regression, stepwise regression, random forest and generalized boosted regression, to model stock indicators and quarterly log-return in a rolling window. Thirdly, we choose the model with the lowest Mean Square Error in each period to rank stocks. Finally, we test the selected stocks by conducting portfolio allocation methods such as equally weighted, mean-variance, and minimum-variance. Our empirical results show that the proposed scheme outperforms the long-only strategy on the S&P 500 index in terms of Sharpe ratio and cumulative returns. This work is fully open-sourced at \href{https://github.com/AI4Finance-Foun dation/Dynamic-Stock-Recommendation-Mach ine_Learning-Published-Paper-IEEE}{GitHu b}. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12129 |
| By: | Matteo Crosignani; Lina Han; Marco Macchiavelli |
| Abstract: | Firm-level geoeconomic risk can affect even broadly diversified mutual fund portfolios. We study U.S. export controls that restrict sales of cutting-edge technology to selected Chinese firms for national security reasons. The stock prices of affected domestic suppliers drop immediately after the policy introduction. Mutual funds holding these stocks experience increased volatility and lower returns. Fund managers respond by selling stocks of exporters to China, buying lottery-like stocks, and increasing portfolio concentration. While stock-picking and market-timing skills do not help, specialist and high-fee funds are better at navigating geoeconomic risk. |
| Keywords: | mutual funds; Asset allocation; geoeconomic risk; Export controls |
| JEL: | G12 F51 F38 |
| Date: | 2025–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:102163 |
| By: | Babolmorad, N.; Massoud, N. |
| Abstract: | We build a framework to examine how the training regime-rather than model architecture-drives the performance of financial sentiment models. Using firm-level news and parsimonious classifiers, we compare three supervision regimes: human-only, hybrid, and market-only (fully automated). The framework opens the "black box" of sentiment modeling by tracing how supervision shapes each component of the classifier. Across extensive tests, the hybrid regime consistently outperforms fully automated training in explaining variation in stock returns and trading volume, enhancing interpretability and economic relevance. Human input improves sentiment inference, offering new insights into information processing and price formation in financial markets. |
| Keywords: | Sentiment Analysis, Financial Media News, Investor Sentiment, Stock Markets, Human versus Machine |
| JEL: | G02 G11 G12 G14 |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2577 |
| By: | Nicola Cetorelli; Saketh Prazad |
| Abstract: | In a previous post, we documented how, over the past five decades, the typical U.S. bank has evolved from an entity mainly focused on deposit taking and loan making to a more diversified conglomerate also incorporating a variety of nonbank activities. In this post, we show that an important driver of the evolution of this new organizational form is the desire of banks to efficiently manage liquidity needs. |
| Keywords: | banks; nonbanks; nonbank financial institutions (NBFIs); NBFIs; non-bank financial intermediaries; liquidity; bank regulation |
| JEL: | G01 G21 G23 G28 |
| Date: | 2025–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102119 |
| By: | Srichander Ramaswamy (The South East Asian Central Banks (SEACEN) Research and Training Centre) |
| Abstract: | Many central banks are assessing the benefits that a wholesale central bank digital currency (wCBDC) could bring to the financial system. This assessment is being driven by the rise of tokenised assets and the need for efficient and safe settlement assets in these markets. While wCBDCs can facilitate settlements on distributed ledger technology platforms for such assets, some central banks are of the view that existing systems (like the RTGS) can achieve similar outcomes through application programming interfaces without the need to introduce a new central bank liability. Beyond settlement of tokenised assets, wCBDC is also being seen having the potential in offering many benefits to cross-border payments by reducing settlement times and transaction costs. That is because existing arrangements employing correspondent banking models introduce frictions by having multiple intermediaries that introduce counterparty risk and longer settlement times. They are also costly as they need pre-funded nostro accounts. In theory, wCBDCs can eliminate the need for correspondent banks by allowing direct settlements between central banks, but this raises questions about central banks' willingness to assume correspondent roles. Alternative arrangements using automated market makers can also facilitate foreign exchange trading using wCBDCs, but their effectiveness and cost efficiency in less liquid currency pairs remain uncertain. The exploration of wCBDCs should, therefore, consider the existing capabilities of the financial system and the potential for private sector solutions to meet market needs effectively. |
| Keywords: | Central banks, digital currency, cross-border payment, correspondent bank, tokenisation, large value payments. |
| JEL: | E42 E58 G21 G28 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:sea:wpaper:wp57 |
| By: | Della Corte, Pasquale; Gao, Can; Preve, Daniel P. A.; Valente, Giorgio |
| Abstract: | This paper investigates the long-horizon predictive variance of an international bond strategy 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 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:331899 |
| By: | SALGUERO, RICARDO ALONZO FERNANDEZ |
| Abstract: | The growing interest in sustainability has generated a vast academic literature on the intersection of climate change, green finance, and financial markets. However, heterogeneity in methodologies and results often makes it difficult to draw unified conclusions. This paper presents a review of meta-analyses and systematic literature reviews to synthesize the current state of knowledge. By analyzing a corpus of 22 reviews, we assess the consensus on three key areas: 1) the impact of climate risk (physical and transition) on asset valuation, 2) the existence and magnitude of a green financing premium (the "greenium"), and 3) the effects of green finance practices on corporate and banking profitability. Our findings indicate an emerging consensus on the pricing of physical climate risk in the real estate market and a modest but persistent negative premium for green bonds. However, the impact of sustainable finance on financial profitability remains ambiguous and highly dependent on the methodological context. This synthesis consolidates quantitative findings, evaluates prevailing methodologies, identifies research gaps, and proposes an integration theory with an agenda for future research, highlighting the need for greater standardization in metrics and analytical approaches to strengthen the robustness of empirical conclusions. |
| Date: | 2025–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:aqy84_v1 |
| By: | Rashad Ahmed; James A. Clouse; Fabio Natalucci; Alessandro Rebucci; Geyue Sun |
| Abstract: | The GENIUS Act, recently signed into law, establishes a dual federal and state regulatory framework for stablecoins, effectively segmenting the USD stablecoin market into GENIUS-compliant stablecoins and those that are not. This paper discusses the use cases and potential benefits of stablecoins in terms of payment system efficiency and costs, as well as their substitutability with money market mutual funds and bank deposits. It then analyzes the financial stability risks associated with both GENIUS-compliant and unregulated stablecoins using empirical analysis and historical case studies. It concludes by discussing the economic implications of the emergence of a large dollar stablecoin ecosystem. The discussion is supported by a new survey of expert opinions canvassed through Large Language Model (LLM) analysis of all U.S. podcast episodes on stablecoins from January 20 to July 17, 2025. |
| JEL: | E42 F33 G21 G23 O33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34475 |
| By: | Hledik Juraj (European Commission - JRC); Konecny Jakub (European Commission - JRC); Christaras Vasileios (European Commission - JRC); Di Girolamo Francesca (European Commission - JRC); Kounelis Ioannis (European Commission - JRC); Pagano Andrea (European Commission - JRC); Bazzanini Andrea; Iacovone Massimo |
| Abstract: | "This progress report presents the current state of development of the CIPHER framework, an robust and scalable platform for monitoring crypto-asset markets, decentralized finance (DeFi) protocols, and blockchain-based financial activity. CIPHER’s role is to integrate blockchain-derived data with structured monitoring modules, covering areas such as market activity, financial stability risks, and systemic dependencies. The report outlines the methodological foundations, technical infrastructure, and preliminary analytical outputs achieved to date. All of this is contextualised within the recent technological and regulatory development of the crypto landscape.The report highlights key challenges and lessons learned, including data issues, the computational demands of large-scale blockchain analytics, and the need for interoperability and long-term sustainability. Finally, it provides a forward-looking roadmap for further development, including infrastructure scaling, feature enhancements, and deeper collaboration with EU and international stakeholders.The ultimate objective of CIPHER is to provide a policy-relevant, operationally sustainable monitoring and analytics tool capable of supporting EU institutions in supervisory, regulatory, and strategic decision-making related to crypto-assets and DeFi." |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc143790 |
| By: | Osei, Prince (Center for Mathematical Economics, Bielefeld University); Riedel, Frank (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | We study optimal portfolio choice when the variance of asset returns is ambiguous. Building on the smooth model of ambiguity aversion by Klibanoff et al. (2005), we introduce a one-period framework in which returns follow a Variance–Gamma specification, obtained by mixing a normal distribution with a gamma prior on the variance. This structure captures empirically observed excess kurtosis and allows us to derive closed-form solutions for optimal demand. Our main results show that ambiguity about volatility leads to bounded portfolio positions, in sharp contrast to the unbounded exposures predicted by the classical CAPM when expected excess returns are large or when the mean variance tends to zero. We characterize the comparative statics of the optimal allocation with respect to risk aversion, ambiguity aversion, and the parameters of the prior distribution. For small mean excess returns, portfolio demand converges to the CAPM benchmark, indicating that ambiguity aversion affects higher-order terms only. The model provides a tractable link between robust portfolio choice and realistic, heavy-tailed return dynamics. |
| Date: | 2025–11–27 |
| URL: | https://d.repec.org/n?u=RePEc:bie:wpaper:756 |
| By: | Markus Brunnermeier; Jonathan Payne |
| Abstract: | This paper examines how digital payment ledgers operated by BigTech platforms and central banks can expand uncollateralized credit. However, policymakers face a trilemma-no system can simultaneously achieve efficient credit enforcement, limit rent extraction, and preserve user privacy. Monopolistic platforms enforce repayment but compromise privacy and extract rents; public or privacy-respecting ledgers protect users but weaken enforcement; platform co-opetition or programmable public ledgers balance enforcement and rents, but only by reducing privacy. |
| Keywords: | ledgers, platform money, CBDC, currency competition, private currencies, industrial organisation of payments, platforms, big tech, trilemma |
| JEL: | E42 E51 G23 L51 O31 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1306 |
| By: | Ragnar Juelsrud; Plamen Nenov; Fabienne Schneider; Olav Syrstad |
| Abstract: | In this paper we study the cross-section of equilibrium returns on safe assets using a tractable asset pricing model with a micro-founded demand for liquidity and multiple safe assets with heterogeneous transaction costs. A key feature of our model is the “value of convenience, ” which is an equilibrium object that measures the level of liquidity risk-sharing in the economy. Changes in asset supply or the transaction cost of a single safe asset affect aggregate liquidity and the returns of all assets. The model features a pecuniary externality, which investors fail to internalize when forming their portfolios and which impacts equilibrium welfare. Therefore, policies that increase the payoff on the most liquid asset improve welfare in the competitive equilibrium. We test the main predictions of our theory using a novel measure of relative (in)convenience yields in the US Treasury market. |
| Keywords: | Asset pricing; Financial markets; Debt management; Monetary policy |
| JEL: | G12 E44 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-34 |
| By: | Ping Wu; Dan Zhu |
| Abstract: | Financial markets are interconnected, with micro-currents propagating across global markets and shaping economic trends. This paper moves beyond traditional stock market indices to examine cross-sectional return distributions-15 in our empirical application, each representing a distinct global market. To facilitate this analysis, we develop a matrix functional VAR method with interpretable factors extracted from cross-sectional return distributions. Our approach extends the existing framework from modeling a single function to multiple functions, allowing for a richer representation of cross-sectional dependencies. By jointly modeling these distributions with U.S. macroeconomic indicators, we uncover the predictive power of financial market in forecasting macro-economic dynamics. Our findings reveal that U.S. contractionary monetary policy not only lowers global stock returns, as traditionally understood, but also dampens cross-sectional return kurtosis, highlighting an overlooked policy transmission. This framework enables conditional forecasting, equipping policymakers with a flexible tool to assess macro-financial linkages under different economic scenarios. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.17140 |