Weekly quantitative finance research: 90 papers from arXiv, SSRN and RePEc, 19 Sep to 25 Sep 2026.
Draft: ranked and summarised by the keyword heuristic (run with --no-llm).
We introduce MFAST, a Market-Friction-Aware Sentiment-to-Trading framework that converts timestamped financial text into auditable, reproducible, and market-feasible trading decisions.
To address these limitations, we propose LiMT, a Hierarchical Multi-Task Learning framework that integrates liquidity-aware signals for stock price forecasting.
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions.
We develop a universal conditional diffusion model that learns to jointly generate next-day IVS increments and the underlying stock's returns.
Artificial intelligence (AI) now supports investment workflows from data and prediction through research, portfolios, execution, and tool use.
This paper proposes a hybrid two-stage architecture that combines a long short-term memory (LSTM) network with an XGBoost gradient-boosted regressor for multi-horizon stock return prediction across a diversified panel of 14 U.S.
Over the 20--year period 2006--2026, using a conservative estimate of trading costs, we show that all risk-adjusted and drawdown metrics are improved using simple volatility control, where we dynamically mix the fixed-weight portfolios with cash…
We develop a surface-driven stochastic-volatility framework for soybean futures options using daily Chicago Mercantile Exchange Group Volatility Index (CME CVOL) indicators from October 2013 to August 2025.
Using historical S&P 500 data, we show that a mean-reverting SDD specification reproduces several empirical features of market diversity and dispersion.
We propose a cross-asset state-space trading system (CAST), consisting of two components: The predictor, Cross-Asset Collaborative Kalman Filter (CoKF), estimates each asset's latent state online, coupling all assets through their correlations and adaptively fusing multiple…
Commodity futures are shaped by harvest cycles, weather shocks, storage conditions, and seasonal demand, but it remains unclear whether recurring patterns yield robust out-of-sample trading profits.
We develop a diffusion-model framework for dynamic implied-volatility surface generation and evaluate its economic usefulness through data-driven hedging.
We study the infinite-horizon optimal investment and consumption problem in a general class of continuous financial markets, where uncertainty is driven by a continuous non-decreasing stochastic clock representing accumulated variance.
We examine the conditional volatility dynamics of the USD 1Yx10Y forward swap rate using GARCH(1,1), GJR-GARCH(1,1), and a two-regime Markov-switching GARCH (MSGARCH) model.
We cross three proposers (a script, a bandit and a language model) with this referee and with three deliberately leaky ones, in a synthetic world with planted truth, a probe-authoring environment and a ten-year walk-forward…
Here we present a CBDC architecture based on grassroots currencies that overcomes these limitations.
InvestorNerd is a web-based platform (investornerd.org) designed to educate and democratize financial understanding by providing accessible, AI-powered investment and personal finance insights tailored to potential user profiles.
We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic ruleset.
Market-making strategies in real limit order book markets face substantial model uncertainty and regime-shift risk.
To this end, we propose a three-step rebate design scheme with flexibility to accommodate specific liquidity targets imposed by an exchange.
We introduce a Markov chain Monte Carlo (MCMC) kernel that mixes better than existing samplers at the same computational complexity.
To bridge this gap, we propose FinRankGRPO, a framework that shifts LLM based portfolio construction from direct numerical prediction to listwise ranking of financial assets.
This paper develops a functional architecture for AI-supported trading that is aligned with market-coupling mechanics, cross-zonal transfer constraints, and compliance obligations under REMIT, MiFID II, MiFIR, and EMIR.
Hybrid Deep Learning for equity index forecasting is limited by three problems: propagation of OHLCV noise into derived technical indicators (TIs), channel-indiscriminate multi-scale decomposition that conflates heterogeneous frequency signatures, and static multi-branch fusion that cannot…
This paper proposes an investment strategy through resource allocation into Kalshi Crypto Event Contracts in order to effectively hedge exposure to spot asset volatility.
We write the often substantial error in these estimates as a sum of two interpretable terms, which we show have almost sure asymptotic limits as the number of variables grows with sample size bounded.
In this paper, we develop a deep reinforcement-learning market maker (RLMM) - a Rainbow-style distributional DQN (C51) which is calibrated and tested in a zero-intelligence limit order book.
We propose VertiFuseX, a hybrid LSTM architecture using penultimate-layer vertical fusion of multi-scale temporal representations.
Using Shapley-based explainability, we show that the dominant contributions are concentrated at the most recent lags.
This text grew out of a historical introduction initially written for a study of interest rates in cryptocurrency markets.
We propose a statistical arbitrage framework for equity pairs and small synthetic baskets that replaces the conventional fixed-window z-score trading rule with a sequence of estimated stochastic models, each addressing a specific weakness of the…
This paper is devoted to the execution of crude oil arbitrage and momentum strategies with volatility targeting by using Large Language Models (LLM), comparing efficiency and profitability of LLaMA2 and GPT3.5.
Using a comprehensive dataset drawn from FINRA’s Enhanced TRACE, the CRSP Mutual Fund Database, Mergent FISD, and TAQ intraday records, we construct fund-level and bond-level measures of liquidity, HFT intensity, and price impact.
Generative artificial intelligence (GenAI) is rapidly becoming an information intermediary, analytical assistant, and decision interface in financial markets.
This paper applies Value at Risk (VaR) and Expected Shortfall (CVaR) to a portfolio of four large-cap tech stocks: Apple (AAPL), Alphabet/Google (GOOGL), Microsoft (MSFT), and NVIDIA (NVDA).
In this paper, we present \textbf{MartingaleONet}.
This study investigates the microstructural phenomenon of “Liquidity Voids” (LQD) kinetic price shockwaves generated by the sudden withdrawal of massive limit orders within the high-frequency Ethereum futures market (ETH/USDT).
This paper shows that the leveraged positioning of hedge funds amplifies the response of sovereign bond yields to shocks, leading to temporary overshooting.
This study examines the effectiveness of traditional econometric models, such as ARMA, GARCH, and EGARCH, in forecasting the implied volatility of crypto derivatives when data history is limited.
It is crucial to accurately and rapidly calculate the bond dissociation energy of Si-X bonds as they determine the thermodynamic stability and reactivity of organosilicon compounds, which are significant in different chemistry fields.
This study compares the performance of machine learning models and traditional econometric models in forecasting exchange rates.
This study examines state-dependent systemic-risk transmission across global systemically important banks and emerging-market banking systems over 2008–2026.
This paper develops real-time semantic surprises from Federal Open Market Committee statements and studies how textual innovations enter asset prices and macro-financial stress.
This study provides an integrated framework linking risk measurement with risk management in green finance and energy markets.
This study develops an explainable artificial intelligence framework designed to optimise cryptocurrency trading signals by integrating established technical indicators with advanced ensemble machine learning techniques.
Using the full text of 45 FOMC statements (January 2021-July 2026), Google's Gemini 2.5 Flash model, IG Markets broker-executable daily prices, and official central-bank policy rates for carry, we construct a sentiment-tilted Conditional Value-at-Risk (CVaR)…
To investigate the stock resilience premium and its underlying determinants in China’s capital market, this study constructs a comprehensive resilience index using 32 macro- and micro-level indicators.
The stock-bond correlation varies substantially over time, altering the diversification benefits of long-term government bonds.
This paper presents a comprehensive machine learning architecture for yield curve fitting, term-structure interpolation, and relative-value arbitrage detection.
This paper extends the empirical framework of Christopher (2026), which documents a 49% contagion intensification between the S&P 500 and Nifty 50 during joint high-volatility regimes using GJR-GARCH and rolling-threshold regime classification, along two dimensions…
This paper introduces Generative Capital, a financial architecture that integrates tokenization, decentralized finance, artificial intelligence, quantitative portfolio construction and agents: (i) eligible assets are digitally represented and divisible, (ii) only assets, and counterparties, that pass…
FinTech ventures operate in a rapidly evolving environment in which digital payments, investment platforms, financial education, and wealth-management applications are becoming increasingly important.
On May 13, 2024, GameStop surged nearly 74% in a single trading session after “Roaring Kitty” returned to social media.
In this paper, we introduce a novel Supervised Sequence Modeling framework for continuous market making that uses Deep Long Short-Term Memory (LSTM) networks as its foundations and compare its outcomes with the performance of a…
This paper develops Tail Episode Risk (TER), a path-dependent tail-risk functional that quantifies the severity of the most extreme contiguous cumulative-loss episode within a forecast horizon, and Conditional Tail Episode Risk (CTER), which decomposes future…
This paper presents a PRISMA-based systematic review of financial multi-agent systems.
To further capture nonlinear and timevarying market dynamics, we propose novel regime-dependent predictors derived from a Markov regimeswitching framework and incorporate them into the learning process.
To address it, this study proposes a new Hybrid TRN-XGBoost framework that mathematically separates temporal feature extraction from gradient-boosted regression.
We examine the pervasive participation of index ETFs in IPOs in the Chinese stock market, where ETFs exploit underpricing to generate returns of approximately 4.56% per annum.
Corporate carbon-neutral commitments create a measurement problem for investors because the activities that reduce valuechain emissions do not map cleanly onto separately disclosed financial-statement line items.
We show that the optimal monetary policy and exchange rate framework depend critically on the economy’s commodity exposure.
This paper introduces the Reddit Retail Investor Sentiment Indicator (R-RISI), a high-frequency measure of retail investor sentiment based on Reddit posts from major investing and crypto-related subreddits between 2015 and April 2026.
Forecasting inflation in the presence of changing economic conditions, external shocks, and evolving transmission mechanisms remains an active area of research.
This study compares belief-driven financial advice from professionals, peers, and LLM with vignettes, eliminating matching problems and enabling belief elicitation without incentive confounds.
Our results show that individuals who trust strangers in relation to financial matters are more prevalent cryptocurrency participants, both in terms of current holdings and intended future holdings, compared to less trusting individuals.
Would widely adopted stablecoins affect the price level, and through which channels?
This paper develops a model-free measurement and inference framework for high-frequency cross-market trading activity and provides empirical evidence of its importance in financial markets.
Rather than treat this as a dead end, this paper develops and formally tests an alternative: a small number of recurring, economically interpretable curve-shape states — fewer in number than they first appear, once rate…
Using detailed microdata on Swedish public and private firms, and high-frequency monetary policy surprises around Riksbank announcements, we document that smaller, financially constrained firms adjust prices significantly less than larger firms in response to changes…
This paper examines the responses of foreign exchange rates to the Federal Reserve’s large-scale asset purchases (LSAP) and forward guidance (FWG) from 2009 to 2022.
In response to an unanticipated monetary policy tightening in the US, the demand/financial channel of the international transmission of the shock dominates over the expenditure‑switching effect.
We study the demand for safety and liquidity in the crypto ecosystem.
We study the determinants of US dollar demand across market participants and traded instruments using survey-based exchange rate and macroeconomic expectations.
We study how stablecoins impact global capital flows by constructing a novel wallet-level dataset linking geotagged Ethereum Name Service registrations to stablecoin transactions around banking restrictions, currency crises, sanctions, and monetary disruptions.
To study the competition between incumbent and rising powers under financial interdependence, we develop a model of asset demand with microfounded network effects.
This study examines the factors influencing corporate decisions in sustainable bond issuance, focusing on Green Bonds (GB), Sustainability-Linked Bonds (SLB), Sustainability Bonds (SUB), and Social Bonds (SOB).
Dans le paysage évolutif et peu méconnu des marchés financiers émergents, cette enquête vise à évaluer systématiquement les conséquences des biais comportementaux et de la littératie financière sur les différents paramètres de la prise de…
This paper examines the relationship between bank capital and reliance on insured deposit funding.
We study how the financing of innovation shapes the transmission of monetary policy to productivity.
We study how increased uncertainty about U.S.
Households around the world increasingly take portfolio advice from the same handful of large language models.
Corporate borrowing creates safe claims for some households and concentrates residual risk in equity for others.
We propose the Blockwise Boosted Inflation Model (BBIM), a boosted tree framework that decomposes inflation dynamics into predictive components aligned with an open-economy hybrid Phillips curve.
Perpetual preferred equity issued against a Bitcoin reserve produces a forward-coverage problem.
Using granular data, we document significant fragmentation in interbank markets with a set of banks that never trade in interbank markets (inactive banks) and others that do (active banks).
We find that interest rate spreads are indeed less sensitive to changes in the capital-to-debt ratio for firms with higher intangible intensity.
This paper examines whether changes in cash in circulation contain predictive content for inflation in Morocco, and whether inflation itself feeds back into cash demand, during a period marked by major shocks (2017M01-2023M09).
We construct a new historical corporate bond database spanning 128 years to estimate a corporate bond counterpart to the equity risk premium.
This paper examines whether the early diffusion of artificial intelligence (AI) is visible in productivity and price outcomes relevant to monetary policy.
We ask whether high-frequency political communication shapes the level of conditional volatility, its persistence, or the probability of transiting between volatility states.
