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on Financial Markets |
| By: | Peter Boswijk (University of Amsterdam); Cees Diks (University of Amsterdam); Simon Trimborn (University of Amsterdam); Matteo Valle (University of Amsterdam) |
| Abstract: | The aim of this paper is to determine from market expectations how firms are affected by risks arising from environmental regulation. We use a text-based measure of environmental regulatory stringency derived from U.S. EPA legal documents and industry-level relevance scores to capture time-varying regulatory stringency exposure. We find that environmental regulatory stringency carries a positive and statistically significant return compensation, especially for firms with high cash holdings. For firms with low cash holdings, the effect is highly volatile, showing investors are uncertain about a firm's future when faced with stricter regulation. Firms’ environmental profiles further matter, as high-emission firms' returns are negatively affected when regulatory stringency increases. Because regulatory text is released infrequently, challenging real-time risk analysis, we utilise our studies insights to derive a high-frequency, market-expectation capturing Environmental Regulatory Risk Index (ERRI). We show that ERRI captures shifts in investors’ expectations of environmental regulatory stringency and how ERRI reacts during environmental policy and political developments. |
| Date: | 2026–07–15 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260044 |
| By: | Jaesung Kim; Changhee Cho; Jae Woo Lee |
| Abstract: | This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional benchmarks. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.10852 |
| By: | Sebastian M. Peters; Jürgen Huber; Michael Kirchler |
| Abstract: | The climate crisis is one of the major challenges mankind currently faces and, through green investments, the finance industry can play a crucial role in tackling it. However, this topic still appears to be controversially discussed in finance, and only very few studies have investigated the behavior of finance professionals with respect to green investments. In this experiment, we investigate the behavior of 174 finance professionals and 192 participants of the general population, measuring drivers of decision making in green investments. We find in both participant pools that individuals who say it feels better to invest in green assets also do so. Between the two subject pools, we find no difference in investment propensity in green assets. Furthermore, we observe that financially literate participants invest significantly more green and achieve higher portfolio returns. Interesting, while 89 percent of participants say they would not be ready to forego returns for greener assets, only 10 percent select the portfolio with the highest possible return. |
| Keywords: | Green investments, finance professionals, experimental finance |
| JEL: | C90 G40 G41 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:inn:wpaper:2026-07 |
| By: | Henry Han |
| Abstract: | Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight. Yet agentic AI governance in FinTech is under-investigated. We argue the binding governance constraint is not capability but verifiability. We define the Verifiability Gap as the shortfall between the verification delegated authority demands and the explainability and reproducibility retained after a decision. It is indexed to a verifier, evidentiary standard, and audit lag. We develop a multilevel governance theory for agentic AI and test its mechanisms in three studies over nine model versions, from a three-billion-parameter local model to a commercial frontier system. Study 1 shows that provider releases alter historical financial actions, and that the controls replay needs belong to the provider: the frontier model rejects temperature, top_p and top_k outright and exposes no random seed. Under the tightest controls each endpoint allows, a local model reproduced 320 of 320 executions, hosted models 319 of 320 and 959 of 960. Study 2 shows that orchestration is a latent policy layer. Architecture changes final actions, and no execution record repeated in any configuration at any scale. The frontier model reproduces its own actions more often than the local ones, its record no better, and loses a comparable share of its differentiation. Capability buys a higher starting point, not auditability. Study 3 shows two deterministic credit-model versions each reproduce their current action perfectly, yet the current cannot recover a historical one. We conceptualize reproducibility as a governance profile, not a scalar, yielding evidence-contingent delegation: authority is defensible only while retained evidence substantiates its exercise. Beyond finance, the framework extends to other high-stakes domains requiring auditability. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.11344 |
| By: | Muhammad Abdullah Haroon |
| Abstract: | Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3, 491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.23370 |
| By: | Alberto Acedo |
| Abstract: | The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.10788 |
| By: | Alireza Kargarzadeh; Nariman Khaledian; Navid Parvini; Sid Ghatak; Arman Khaledian |
| Abstract: | Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it. Whether the claims hold, for which kinds of news, and by how much are basic questions about how fast markets absorb public information. We test them on 4.57 million financial news articles covering roughly 3, 000 US stocks (2023-2026). A large language model teacher, distilled into a compact classifier through active learning, assigns each article one of 17 event tags and five attributes; articles are clustered into stories to separate first reports from follow-up coverage; and beta-adjusted abnormal returns are measured around the resulting 1.68 million stock-day events, with 364, 405 neutral-sentiment events as a placebo group. Three results follow. First, the price move associated with news concentrates before and at publication: pooled across all signed events, the cumulative move in the news direction by the close of publication day is 2.8 times its value 20 days later, and for rumor-flagged events the rumor day captures the entire move while the subsequent confirmation contributes nothing. Second, measured against the placebo of comparable stocks, markets underreact to numbers and overreact to stories: quantified fundamental news (earnings, dividends, guidance, analyst actions) keeps drifting in the direction of the news for weeks, while soft story-driven news (launches, macro commentary, leadership) gives back its move. Third, news carries width as well as direction: publicity raises volatility before the publication day, and volatility declines once the news is out, because publication resolves uncertainty. The study also produces a table of measured drift for each event tag, usable as a prior in news-conditioned forecasting models. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.14014 |
| By: | Danny Auger; Adrian Walton |
| Abstract: | This note examines whether central clearing of repurchase agreements (repos) provides benefits not only to bank-owned dealers but also to non-dealer market participants in Canadian fixed-income markets. While prior research has largely emphasized the dealer perspective—showing that central clearing can reduce funding and balance-sheet costs—less is known about its implications for clients. Given the central role of repos in supporting liquidity and funding in Canadian fixed-income markets, understanding these broader effects is essential for evaluating the potential contribution of central clearing to market efficiency and resilience. This note explores how centrally cleared repos may affect non-dealer participation and considers whether expanded access to clearing could strengthen the overall functioning and stability of core Canadian funding markets. |
| Keywords: | Financial markets and funds management; Market structure |
| JEL: | E44 G12 G21 G23 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsap:26-31 |
| By: | Divyanee Garg |
| Abstract: | Understanding similarity among financial assets is essential for effective portfolio diversification. This paper proposes a novel sentiment-adjusted portfolio optimization framework that integrates Topological Data Analysis (TDA) with technical indicators and FinBERT-based sentiment scores extracted from financial news. A TDA-based distance measure is employed within an agglomerative clustering framework to identify topologically dissimilar assets for portfolio construction. By incorporating sentiment information, the framework captures rapid changes in market perception and investor behavior that are not reflected by technical indicators alone. Unlike conventional correlation and Euclidean distance based approaches, the proposed method characterizes complex nonlinear relationships through topological summaries. To account for the transient nature of market sentiment, a dynamic rolling-window rebalancing strategy with frequent portfolio updates is adopted. A retention mechanism is further introduced to preserve high-quality assets across consecutive rebalancing windows, thereby reducing portfolio turnover and transaction costs. Extensive empirical analysis on S&P 500 constituents demonstrates that the proposed framework consistently outperforms correlation and Euclidean distance based methods, as well as benchmark strategies including Na\"ive, Index, and full-universe portfolios, in terms of returns and reward-risk performance. Furthermore, the framework exhibits strong robustness by delivering positive performance during periods of heightened market uncertainty, such as the U.S.-Israel-Iran conflict. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.21170 |
| By: | Alireza Kargarzadeh; Nariman Khaledian; Navid Parvini; Arman Khaledian |
| Abstract: | Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction. We study an uncertainty-aware construction that feeds model-predicted risk -- decomposed into aleatoric and epistemic components -- directly into the covariance matrix of portfolio allocators, rather than treating portfolio risk as fixed or adjusting only expected returns. We evaluate the pipeline on Russell 2000 equities under three stock-selection regimes: a pure-alpha trigger that isolates abnormal stock moves not explained by macro indicators, a pure-beta trigger that captures macro-indicator moves before the stock itself fires, and a beta trigger in which both channels agree. Across the full holding-period grid, the separated pure-alpha and pure-beta legs usually dominate the beta intersection on Sharpe and return. Two horizons are especially informative. At one day, pure beta can work under low and moderate transaction costs because it captures immediate lead-lag spillovers from liquid macro and sector indicators into exposed small-cap stocks, but this advantage disappears at 100 bps when turnover and microstructure noise dominate. At 40 days, pure beta works for a different reason: slower macro repricing overtakes the firm-specific pure-alpha channel. The strongest conservative row is pure beta with GPT-4o mini sentiment, a Student-t target, a 40-day holding period, and risk parity allocation, reaching Sharpe 2.33 at 100 bps. The results suggest that stock-selection regime and allocator choice matter at least as much as the sentiment model, and that separating firm-specific and macro-exposure triggers is more informative than requiring both to fire simultaneously. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12283 |
| By: | Julia Ko\'nczal; Rafa{\l} Po{\l}ocza\'nski |
| Abstract: | Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.09576 |
| By: | Ayelen Banegas; Lucas Devigne; Mulalo Mamburu; Kleopatra Nikolaou; Anna Samarina; Fabio Tamburrini |
| Abstract: | This paper synthesizes the literature on vulnerabilities in government bond-backed repo markets, focusing on the features that contribute to both the fragility and stability of these markets. The literature shows that the same features that enable efficient liquidity provision, including short-term funding, dealer intermediation, extensive collateral reuse, and low haircuts, can also create channels for rapid transmission of stress. The review documents tight linkages between repo and government bond markets, highlighting how repos are key to the build-up of leverage and can propagate stress across funding, cash, and derivatives markets, particularly through dealers and nonbank financial intermediaries such as investment firms, hedge funds, and money market funds. Evidence from recent stress episodes illustrates how these vulnerabilities materialize in practice. The review also examines post-crisis regulatory reforms and central bank interventions, identifying how these measures have enhanced market resilience while also creating trade-offs for market dynamics, with implications for liquidity and collateral availability. |
| Keywords: | repo markets; government bonds; vulnerabilities; financial stability |
| JEL: | G10 G12 G15 G20 |
| Date: | 2026–08–12 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:103647 |
| By: | Itamar Drechsler; Hyeyoon Jung; Weiyu Peng; Dominik Supera; Guanyu Zhou |
| Abstract: | Credit card interest rates currently average 22%, an 18% spread over the short rate. This spread far exceeds that on any other loan or bond, yet nearly half of households are credit card borrowers. Why are credit card rates so high? To understand this, and the economics of credit card banking, we use regulatory account-level data to analyze the lifetime cash flows of 550 million monthly accounts, representing 90% of the US credit card market. While charge-off rates are comparatively high, averaging around 6%, they explain only a fraction of cards' spread. Reward payments and non-interest expenses are more than offset by interchange and non-interest income. Operating expenses, particularly marketing, are very large, and are used to generate pricing power. Yet, after deducting them, card lending still earns a 6.8% return on assets (ROA), more than four times the banking sector's ROA. Using the cross section of accounts, we estimate that credit card rates price in a 4.3% default risk premium, similar to high-yield bonds. Accounting for this, card lending earns an alpha of around 1.5% relative to the aggregate bank sector. |
| JEL: | E21 E42 E50 G12 G21 G23 G41 G5 G51 M3 M31 M37 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35607 |
| By: | Jean-Sébastien Fontaine; Vincent Meh; Jayden Plener |
| Abstract: | Banking regulations can shape the asset-management landscape in an underappreciated way. We document that banking regulations push asset managers’ liquid holdings away from bank savings accounts and toward money-market assets. The case of Canadian HISA ETFs sheds light on the mechanism. These ETFs, launched in 2013, gather investors’ funds and invest them in high-interest savings accounts. When policy rates rose sharply after the pandemic, HISA ETFs became a surprisingly effective way for households and institutions to earn competitive deposit-like returns. Funds poured in. Then, in late 2023, OSFI reaffirmed how banks under Basel III must treat deposits from ETFs and asset managers more broadly. The result is a clean quasi-experiment. Banks holding HISA ETF deposits lowered the yields offered to HISA ETFs, which then responded by moving holdings toward money-market securities. We describe what HISA ETFs do, their rapid expansion, the regulatory concerns behind OSFI’s stance, and how regulations ultimately shifted HISA ETFs toward holding money-market securities. This episode reminds us that banking regulations ensure the sound liquidity of banks, but it also highlights broader implications for the liquidity-management decisions of other financial institutions. |
| Keywords: | Financial markets and funds management; Funds management; Financial system; Financial institutions and intermediation |
| JEL: | E44 G11 G18 G23 G28 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsap:26-32 |
| By: | Petr Jakubik (Cayman Islands Monetary Authority, Cayman Islands & Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia); Matej Korinek (Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia); Saida Teleu (Anglo-American University, Prague, Czechia) |
| Abstract: | This paper examines how global financial conditions shape bank profitability in small, open, and externally dependent economies. Using bank-level data for Caribbean and Central American countries and System GMM estimation, we assess the impact of U.S. long-term interest rates on two profitability measures: return on equity (ROE) and return on assets (ROA). We find that U.S. Treasury yields have a strong positive effect on ROE, while the response of ROA is considerably weaker. This asymmetric transmission is consistent with portfolio and balance-sheet frictions that constrain balance-sheet adjustment and may limit asset expansion and portfolio reallocation, with global yield movements operating as an indicator of external financial conditions within the global financial cycle rather than as a purely isolated U.S.-specific monetary shock. Using a leverage-based benchmark, we show that the magnitude of the ROE response cannot be explained by accounting mechanics alone, supporting the interpretation that the ROE-ROA divergence reflects constrained adjustment rather than purely mechanical leverage amplification. The findings indicate that equity-based profitability measures are more informative indicators of external financial transmission, with implications for international financial analysis and financial stability monitoring in small open economies. |
| Keywords: | Bank profitability, Portfolio friction; Global financial cycle; Small open economies; System GMM; Caribbean banks |
| JEL: | G21 G15 E44 E58 F36 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2026_23 |
| By: | Jiahao Weng |
| Abstract: | This study develops an agent-based financial market model to explain stock-price momentum and reversal through the joint effects of local herding and delayed information diffusion. Investors form heterogeneous Gaussian beliefs about the next-period price, choose among buying, selling, and remaining inactive, and revise their action probabilities in response to neighboring investors. The local interaction structure is represented by von Neumann and Moore lattices and is later replaced by Erd\H{o}s--R\'enyi and Watts--Strogatz networks for robustness. A separate information process updates investor beliefs through a finite-speed diffusion mechanism, allowing informational adjustment to be distinguished from behavioral imitation. The simulations show that stronger herding produces spatially clustered trading, larger price fluctuations, and more pronounced excess kurtosis in returns. Faster information diffusion reduces the time required for prices to approach the signal-implied value, whereas the combination of information diffusion and social reinforcement generates overshooting and subsequent reversal. An empirical application to China's A-share market compares conventional CSAD and LSV measures with a rolling tail-based herding indicator obtained after Johnson $S_U$ transformation. The indicators display similar time variation and rise during major market disruptions. These findings identify information delay, local social reinforcement, and the eventual decay of herding as complementary mechanisms behind momentum and reversal. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.27063 |