---
title: SSRN papers featured by ML-Quant
url: https://www.ml-quant.com/papers/ssrn/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-09
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
---


# SSRN

Working papers in finance and economics from SSRN.

- [The Evolution of Quantitative Equity Investing: Half a Century of Alpha Research and Institutional Adaptation](https://www.ml-quant.com/papers/ssrn/7566701/) (2026-10-09): Surveys fifty years of quantitative investing as a history of changing implementation boundaries from diversification through machine learning, emphasizing alpha decay and the gap between research and production systems.
- [Does Autonomous Quant Research Improve? Leakage, Evidence Scarcity and Forward Tests in an LLM Factor-Discovery Loop](https://www.ml-quant.com/papers/ssrn/7555699/) (2026-10-09): An autonomous agent evolved 940 factors over 17 days and finds that reusing backtest data inflates edge by a quarter to a third and in-sample improvement predicts worse performance.
- [The Three Faces of Fed Surprise: Rate-Path, Information and Tone Shocks in the Equity Cross-section](https://www.ml-quant.com/papers/ssrn/7560218/) (2026-10-09): The study decomposes Federal Reserve announcements into three orthogonal shocks and traces each through the equity cross-section, finding rate-path surprises dominate while standard balance-sheet characteristics carry no additional power beyond beta and size.
- [Firm-level AI Exposure: Measurement, Determinants, and Pricing Implications](https://www.ml-quant.com/papers/ssrn/7575518/) (2026-10-09): A natural-language analysis of 13,757 firms finds AI exposure surged after ChatGPT and predicts 1.37 percent higher annual returns, though the effect is concentrated after the launch and driven by firm-specific factors.
- [Monetary-Policy Risk and Equilibrium Asset Prices](https://www.ml-quant.com/papers/ssrn/7585438/) (2026-10-09): Asset prices respond to monetary policy uncertainty around FOMC announcements; a New Keynesian model shows policy risk accounts for much of equity and inflation-bond risk premia and weakens activity when elevated.
- [Volatility Targeting, Variance Drain, and the Financialization Effect: A Century of Evidence](https://www.ml-quant.com/papers/ssrn/7585278/) (2026-10-09): Analyzing 100 years of daily data, the study finds volatility scaling harmed returns before 1985 but improved them after, a shift attributed to derivatives markets and deregulation, with walk-forward Sharpe ratio of 1.01.
- [A Multi-Strategy Quantitative Portfolio](https://www.ml-quant.com/papers/ssrn/7561158/) (2026-10-09): Eleven systematic strategies across equities, futures, and currencies achieve a 1.91 Sharpe ratio net of costs from January 2012 to June 2026, with 23.6% compound annual return.
- [Supplying Safety to an Aging World: Demographics, Debt, and Risk Premia](https://www.ml-quant.com/papers/ssrn/7570178/) (2026-10-09): A two-country model attributes 251 basis points of U.S. equity premium gains and 71 basis points of global rate decline since 1980 to demographic aging.
- [What Survives Honest Tes1ng? A Pre-registered Test of Eleven Trading Signals](https://www.ml-quant.com/papers/ssrn/7553278/) (2026-10-09): Applies a strict pre-registered protocol to eleven trading signals across equity and futures markets, finding only trend survives development and hold-out periods with borderline significance after multiple-testing adjustment.
- [Cross-Sectional Corporate Bond Returns](https://www.ml-quant.com/papers/ssrn/7566162/) (2026-10-09): Constructs a characteristics-based systematic risk measure using rate jumps that predicts out-of-sample corporate bond returns, with high-minus-low decile portfolio earning 9.12% annually and 7.80% alpha.
- [The Price of Conviction](https://www.ml-quant.com/papers/ssrn/7571098/) (2026-10-09): The study shows that high-conviction beliefs exert outsized influence on prices, with a long-short portfolio exploiting conviction-weighted disagreement earning 13.2 percent annualized six-factor alpha.
- [Agents Propose, Code Decides: An Autonomous Research Loop Framework to Build an Equity Alpha Model](https://www.ml-quant.com/papers/ssrn/7568638/) (2026-10-09): An autonomous research loop in which agents propose factor candidates and code verifies them builds multi-factor alpha models, screening 212 predictors and admitting 14 that raise baseline information coefficient out-of-sample.
- [GFlowAlpha: A Quality-Control-Aware Neuro-Symbolic Framework for White-Box Alpha Factor Mining](https://www.ml-quant.com/papers/ssrn/7576913/) (2026-10-09): GFlowAlpha uses neuro-symbolic search and internalizes econometric quality control as online rewards to mine white-box factors, delivering mean out-of-sample daily ICIR of 0.485 with Fama-MacBeth t-statistics of 4.99 on Chinese stocks.
- [Deflate by Bits, Not Trials: Why Query-Counted Deflated Sharpe Ratios Fail Under Adaptive Search, and a Sealed-Holdout Remedy](https://www.ml-quant.com/papers/ssrn/7557458/) (2026-10-09): The research proves that query-counted Deflated Sharpe Ratios certify noise in adaptive research and proposes sealed-holdout verification, where validity depends on bits revealed rather than strategy count.
- [The Credit Spread at Long Horizons](https://www.ml-quant.com/papers/ssrn/7570200/) (2026-10-09): The study shows that Baa-Aaa spreads at 27-year maturity test pricing kernels better than shorter maturities, with a structural refinancing model matching observed spreads while prior kernels overpredict by 27 to 68 basis points.
- [Large Language Models and Information Frictions in Corporate Bonds](https://www.ml-quant.com/papers/ssrn/7577906/) (2026-10-09): Shows that large-language-model scores of default risk from earnings calls predict bond rating migration one year ahead where analysts disagreed, capturing information not yet priced in spreads.
- [Fundamentals in Cryptocurrency Valuation: Evidence from DeFi Markets](https://www.ml-quant.com/papers/ssrn/7566845/) (2026-10-09): Revenue surprises on 95 DeFi protocols predict abnormal token returns, with one-standard-deviation surprises associated with 28.8 basis points higher returns over three days when tokens have direct revenue claims.
- [Predicting Crypto Contagion: Machine Learning Forecasts of Return Spillovers](https://www.ml-quant.com/papers/ssrn/7578304/) (2026-10-09): The study forecasts time-varying return spillovers among cryptocurrencies using machine learning, finding monetary policy and funding rates drive connectedness and classifying events that shift spillover composition into contagion types.
- [Reassessing Bitcoin’s Role under ETF-Driven Institutionalization: A Time-Frequency Connectedness Analysis](https://www.ml-quant.com/papers/ssrn/7566372/) (2026-10-09): The research shows that after U.S. Bitcoin ETF approval in January 2024, return and volatility linkages between Bitcoin and equities strengthened substantially, with volatility spillovers becoming markedly asymmetric from stocks to crypto.
- [Artificial Intelligence Exposure and Financial Stability: Cross-Industry Common Risk and Connectedness in U.S. Equity Markets](https://www.ml-quant.com/papers/ssrn/7577948/) (2026-10-09): The study measures AI exposure across 284 U.S. firms and finds that an AI-factor explains only 18% variance alongside conventional factors, suggesting AI is a distinct cross-industry risk lens separate from traditional spillovers.
- [Heterogeneous Estimation of the Causal Effect of Debt Restructuring Interventions: A Causal and Explainable Uplift Modeling Framework for the Economic Optimization of Credit Risk Portfolios](https://www.ml-quant.com/papers/ssrn/7556744/) (2026-10-09): A causal framework compares six estimators of treatment effect to optimally assign debt restructuring interventions, revealing that targeting borrowers by predicted effect generates substantially positive value where blanket treatment fails.
- [Exchange Rate Regimes and Term-Premium Spillovers from U.S. Monetary Policy](https://www.ml-quant.com/papers/ssrn/7588778/) (2026-10-09): U.S. monetary tightening raises term premia sharply in pegged economies but not floating ones; a DSGE model shows pegs force adjustment through domestic rates, tightening leverage constraints and amplifying spillovers.
- [Global Geopolitical Skewness Risk and Bond Risk Premia](https://www.ml-quant.com/papers/ssrn/7570538/) (2026-10-09): A measure of cross-country geopolitical risk skewness predicts U.S. Treasury risk premia out-of-sample and forecasts weaker activity, adding incremental value beyond yield curves and standard macroeconomic predictors.
- [Geoeconomic Fear and Sovereign Spreads](https://www.ml-quant.com/papers/ssrn/7580558/) (2026-10-09): The study constructs a geoeconomic fear index from news articles and finds that higher fear predicts wider emerging-market sovereign spreads, especially for speculative-grade issuers.
- [Firm's refinancing intensity, the cost of capital, and real investment](https://www.ml-quant.com/papers/ssrn/7589333/) (2026-10-09): A global study of 357,630 firm-years shows firms with more short-term debt cut capital expenditure by 2.156 percentage points, mainly due to refinancing availability constraints.
- [Common Banks, Supply Chains, and the Transmission of Real Shocks](https://www.ml-quant.com/papers/ssrn/7588198/) (2026-10-09): Using Portuguese wildfire data, the research finds firms whose banks also lend to suppliers experience 10 percentage points smaller credit declines and face attenuated real losses.
- [Delta-Guided Soft Actor-Critic for Derivative Hedgingunder Model Misspecification and Transaction Costs](https://www.ml-quant.com/papers/ssrn/7585078/) (2026-10-09): A reinforcement learning method embedding Black-Scholes delta guidance cuts hedging error by 19-38% across simulated and real options data with robust statistical significance.
- [A hybrid graph neural network for realized volatility forecasting: Multi-relation attention and dual-path fusion](https://www.ml-quant.com/papers/ssrn/7576359/) (2026-10-09): A graph neural network combining temporal and cross-asset volatility patterns reduces mean squared error and quasi-likelihood loss by approximately 9.9% and 3.4% on Dow stocks.
- [Expectations and the Transmission of Monetary Policy](https://www.ml-quant.com/papers/ssrn/7588262/) (2026-10-09): Optimal rate-setting creates time-inconsistency: market expectations become too sensitive to policy surprises, amplifying real effects of central bank forecast errors; less aggressive rules cut output volatility.
- [The Toll-Fenced Market: Documented Anomalies in Indian Equities Replicate but do Not Survive Retail Implementation Costs](https://www.ml-quant.com/papers/ssrn/7551520/) (2026-10-09): Well-documented Indian equity anomalies replicate strongly in-sample but all fail to survive 12 to 32 basis point retail transaction costs; required directional accuracy reaches 72% at hourly horizons.
- [Across the Capital Structure: Bond Signals and Equity Returns *](https://www.ml-quant.com/papers/ssrn/7527218/) (2026-10-02): Signals extracted from corporate bond portfolios predict next-month equity returns of same issuers at 21 basis points higher after controlling for stock characteristics.
- [Order Flow Imbalance and Price Discovery on Prediction Markets: Evidence from Kalshi](https://www.ml-quant.com/papers/ssrn/7520438/) (2026-10-02): Order flow imbalance predicts contemporaneous mid-price changes in Kalshi binary event contracts, with explanatory power varying from 0.29 for sports to 0.02 for macroeconomic events.
- [Banks versus Private Credit: How Capital Requirements Shape Tailored Lending](https://www.ml-quant.com/papers/ssrn/7524779/) (2026-10-02): The study models how capital requirements tax banks on tailored loans, pushing riskier firms toward private credit; evidence shows that tighter leverage rules reduce bank tailoring by 25 percent in quantitative terms.
- [What Survives an FOMC Surprise? Confounding and the Persistence of Announcement Effects in Asset Prices](https://www.ml-quant.com/papers/ssrn/7533561/) (2026-10-02): Analyzing 261 FOMC announcements shows that immediate stock and yield impacts from policy surprises persist, while post-announcement drift and monetary momentum unwind within days or weeks.
- [U.S. Monetary Policy and Global Market Liquidity](https://www.ml-quant.com/papers/ssrn/7535199/) (2026-10-02): The study shows that US federal funds rate shocks widen bid-ask spreads on equities across 37 markets for up to two months, while longer-maturity yield surprises reprice equities without affecting liquidity.
- [Credit When it's Due: Corporate Bond Factors on a Schedule](https://www.ml-quant.com/papers/ssrn/7524680/) (2026-10-02): The research finds that the first five trading days of each month account for 73% of individual bond credit returns and 83% of the market credit premium, revealing a concentrated timing pattern in fixed-income compensation.
- [Global Debt, Currency Mismatch, and the Spillovers of U.S. Monetary Policy](https://www.ml-quant.com/papers/ssrn/7541599/) (2026-10-02): US monetary tightening triggers larger currency depreciation and sovereign stress in emerging markets when global public debt is high and foreign-currency debt exposure is elevated.
- [Residual Learning in Empirical Asset Pricing](https://www.ml-quant.com/papers/ssrn/7528558/) (2026-10-02): Deep residual networks outperform shallow models in asset pricing, achieving a long-short Sharpe ratio of 2.07 versus 1.92 for shallow versions by preserving and refining earlier layers.
- [The Ant Group IPO Halt as a Natural Experiment on Chinese Fintech Regulation, Firm Valuations, and Shadow-Lending Contraction: A High-Frequency Event Study with Synthetic-Control Validation](https://www.ml-quant.com/papers/ssrn/7545235/) (2026-10-02): The study exploits Ant Group's suspended IPO in November 2020 as a natural experiment, finding that highly exposed firms suffered roughly 21 percentage point abnormal returns and experienced a 42% contraction in shadow-loan balances.
- [Market-Implied Policy-Outcome Dispersion and Long-Run Equity Volatility: Meeting-Level Evidence from Kalshi FOMC Contracts](https://www.ml-quant.com/papers/ssrn/7531599/) (2026-10-02): Using Kalshi FOMC contract probabilities, the paper shows that cross-outcome variance in Federal Reserve policy expectations contains significant information about long-run stock market volatility.
- [Dynamic Graph Neural Networks for Real Time Systemic Risk Surveillance: An Explainable AI Framework for Financial Stability](https://www.ml-quant.com/papers/ssrn/7522878/) (2026-10-02): The research proposes a temporal graph neural network with explainability tools for real-time systemic risk surveillance, achieving early warning signals 3-4 quarters ahead of financial distress on bank data.
- [Monetary Policy and Bond Return Decomposition Across Regimes](https://www.ml-quant.com/papers/ssrn/7549733/) (2026-10-02): The research decomposes bond returns into real rates, risk premia, and inflation expectations, finding that forward guidance and asset purchases had opposite effects at the zero lower bound versus normal times.
- [The Rollover Clock: Debt Maturity, the Central-Bank Balance Sheet, and the Debt Limit of a Reserve-Currency Sovereign](https://www.ml-quant.com/papers/ssrn/7542158/) (2026-10-02): Rollover clock measurement of consolidated Treasury and central bank liability repricing predicts U.S. Treasury interest rates and prices inflation costs of fiscal deficits.
- [The Capacity of Equity Anomalies](https://www.ml-quant.com/papers/ssrn/7524338/) (2026-10-02): Computing capacity for 35 anomalies reveals that high-alpha long legs support less than $2 billion before trading costs eliminate excess returns, while lower-alpha strategies support billions more.
- [Why Most Stocks Underperform the Index: Idiosyncratic Variance Drag and the Geometry of Compounding](https://www.ml-quant.com/papers/ssrn/7546579/) (2026-10-02): A lognormal model shows that individual stocks carry an idiosyncratic variance penalty making most underperform the index, with about one-third beating the market over five years.
- [Spot Bitcoin ETF Flows and Bitcoin Returns: Timestamp Alignment and Asymmetric Feedback](https://www.ml-quant.com/papers/ssrn/7545019/) (2026-10-02): Correcting for timestamp mismatch between ETF and Bitcoin markets, the study finds that same-day flows predict next-day returns at 1.67 percentage points per billion of inflow, while prior Bitcoin returns predict flows.
- [Liquidity Capacity and Token Survival in Cryptocurrency Markets](https://www.ml-quant.com/papers/ssrn/7536975/) (2026-10-02): The research shows that adjusted illiquidity—price impact scaled by market absorption capacity—predicts which cryptocurrency tokens survive better than volatility, improving out-of-sample forecasting of token death.
- [Does Crypto Trading Activity Follow New York Time? Cross-Venue and New York-London Evidence from Clock Changes](https://www.ml-quant.com/papers/ssrn/7526338/) (2026-10-02): Bitcoin and Ether trading activity on major venues increases during the UTC window aligned with New York market open, with the effect appearing on both assets.
- [Less Is More: Forecast Granularity, Estimation Error, and Portfolio Choice](https://www.ml-quant.com/papers/ssrn/7547959/) (2026-10-02): Investors using machine-learning forecasts can achieve Sharpe ratios of 1.2 by adjusting the number of portfolio groups based on the forecast's information coefficient, beating standard decile sorts.
- [An Auditable Public-Data U.S. Equity Research Pipeline: Point-in-Time Factors, Asset Pricing, Portfolio Construction, and Walk-Forward Machine Learning](https://www.ml-quant.com/papers/ssrn/7528419/) (2026-10-02): A reproducible pipeline for factor research finds no model reliably beats simple approaches after accounting for transaction costs in point-in-time factor tests.
- [Filing Discipline: Regulatory Disclosure Behavior and the Cross-Section of Stock Returns](https://www.ml-quant.com/papers/ssrn/7536681/) (2026-10-02): Filing discipline characteristics built from SEC timestamp data alone predict cross-sectional stock returns with a net Sharpe ratio of 1.27, concentrated in smaller and more volatile stocks.
- [Market History, Rebuilt A Reproducible Synthetic U.S. Total-Return Dataset for Leverage and Duration Research ChemicalStats • Anonymous Risk Researcher](https://www.ml-quant.com/papers/ssrn/7527762/) (2026-10-02): A new 234-year synthetic total-return dataset for equities and Treasuries reveals that permanent 3× leverage faces near-total loss in most reconstructions, ranking below 2× on risk-adjusted returns.
- [Tail Risk Hedging in Equity Portfolios: Evaluating the Cost-Effectiveness of Out-of-the-Money Put Options versus Dynamic Tail-Risk Mitigation Strategies During High-Volatility Regimes](https://www.ml-quant.com/papers/ssrn/7541959/) (2026-10-02): Static out-of-the-money put options provide crash protection but drag long-term returns, while dynamic tail-risk strategies adapt to market regimes and deliver superior risk-adjusted returns across cycles.
- [The Geometry of Risk: An Integrated Monitoring Framework for Multi-Asset Systemic Stress](https://www.ml-quant.com/papers/ssrn/7521018/) (2026-10-02): The research develops a four-layer diagnostic framework combining network topology, dynamic causality, tail risk, and regime classification to monitor multi-asset systemic stress in real time.
- [Evolution of Market Microstructure in the Age of AI From Double-Sided Continuous Auctions to Agentic Markets](https://www.ml-quant.com/papers/ssrn/7530158/) (2026-10-02): A survey traces how market design rules evolved from floor trading to limit order books to automated market makers to agent-based trading systems.
- [Geopolitical Risk and Global Financial Market Integration: Dynamic Connectedness across Energy, Commodity, Carbon, and Equity Markets](https://www.ml-quant.com/papers/ssrn/7549731/) (2026-10-02): Global connectedness across energy, commodity, carbon, and equity markets averages 52.4 percent and spikes to approximately 95 percent during major geopolitical disruptions.
- [Impact of Central Bank Digital Currencies on Financial Stability](https://www.ml-quant.com/papers/ssrn/7540719/) (2026-10-02): Calibrated simulations show existing central bank digital currencies remain below thresholds needed to measurably affect bank credit or financial stability.
- [Market Attention and Information Quality in On-Chain Prediction Markets: Evidence from Polymarket](https://www.ml-quant.com/papers/ssrn/7540819/) (2026-10-02): On-chain prediction market analysis finds 68.2 percent of volume sits in well-calibrated markets with average seven-day pricing error of 0.03.
- [Order Flow Imbalance and Price Impact in Indian Equity Derivatives: Cross-Sectional Evidence from NSE F&O Markets](https://www.ml-quant.com/papers/ssrn/7542098/) (2026-10-02): Multilevel order flow imbalance in Indian equity derivatives predicts mean-reverting price dynamics with information coefficients growing from -0.004 at ten seconds to -0.032 at sixty seconds.
- [Bayesian Vector Autoregression for Joint Macroeconomic Forecasting: Pseudo-Real-Time Evidence, Density Forecasts, Structural Dynamics, Scenario Analysis, and Practical Macro-Risk Use for the United States](https://www.ml-quant.com/papers/ssrn/7532698/) (2026-10-02): The research develops a compact Bayesian VAR for real-time forecasting of GDP growth, inflation, unemployment, and the federal funds rate, with evaluation emphasizing density scores and predictive-interval diagnostics.
- [Artificial intelligence and financial markets](https://www.ml-quant.com/papers/ssrn/7515878/) (2026-09-25): A survey examines how AI transforms information production, intermediation, and market structure, with implications for efficiency, competition and financial stability.
- [Label alchemy: Target engineering for improved stock selection](https://www.ml-quant.com/papers/ssrn/7494298/) (2026-09-25): Reshaping the prediction target through location, scale and shape transformations raises long-short Sharpe from 0.68 to 1.69, with label choice mattering more than model choice.
- [Algorithmic Collusion by Reinforcement-Learning Pricing Agents: Simulation Evidence and Implications for Financial Markets and Competition Law](https://www.ml-quant.com/papers/ssrn/7500483/) (2026-09-25): Q-learning pricing agents in simulated duopolies reach supracompetitive outcomes with no communication, achieving collusion indices of 0.778 and 40% profit gains over competitive benchmarks.
- [From D&I to D&I: European Capital Markets' Regime Shift from Diversity and Inclusion to Defence and Infrastructure](https://www.ml-quant.com/papers/ssrn/7477998/) (2026-09-25): European defence stocks repriced sharply starting November 2021, two to three months before Russia's invasion, delivering 26% alpha and reflecting release of ESG-exclusion constraints.
- [Forward Guidance and the Dynamics of Bank Credit: The Bank Balance-Sheet Channel of Monetary News](https://www.ml-quant.com/papers/ssrn/7514178/) (2026-09-25): High-frequency analysis reveals contractionary forward guidance immediately cuts bank lending, while expansionary guidance produces weak stimulus, driven by binding capital constraints.
- [Memorisation or Alpha? Detecting Look-Ahead Contamination in Cross-Sectional Equity Signals](https://www.ml-quant.com/papers/ssrn/7490302/) (2026-09-25): Testing whether a large language model ranks stocks by forecasting or memory, the study finds a significant information-coefficient gap of 0.185 inside versus outside its training window, suggesting substantial look-ahead contamination.
- [Beta Recall, Alpha Recall, and a Contamination Detector that Needs No Labels * Measuring Training-data Leakage in LLM Equity Signals](https://www.ml-quant.com/papers/ssrn/7485818/) (2026-09-25): The study measures recall versus forecasting in an LLM's stock rankings by comparing cross-sectional information coefficients inside and outside the training window.
- [Incentives at Play: Fee-Induced Volume on a Regulated Perpetual Futures Venue](https://www.ml-quant.com/papers/ssrn/7512338/) (2026-09-25): Analysis of Kalshi's regulated Bitcoin and Ethereum futures reveals that 39-48% of notional trades are mechanical fixed-size orders that vanish when fees are charged, indicating costless artificial volume rather than legitimate trading.
- [LLM-Based Semantic Surprises in FOMC Communication: Asset Prices and Financial-Market Stress](https://www.ml-quant.com/papers/ssrn/7519200/) (2026-09-25): Semantic surprises extracted from Federal Reserve statements predict subsequent financial-stress dynamics and reduce forecast error by up to 23%, particularly when initial stress is high or during recessions.
- [Welcome to the Factor Zoo: Where Mutual Fund Alpha Hides](https://www.ml-quant.com/papers/ssrn/7508299/) (2026-09-25): Using factor selection, the study finds mean active alpha of plus 9 basis points monthly for mutual funds, reversing the no-alpha conclusion when benchmarks are tailored to each fund.
- [Speculative Leverage and Factor Momentum](https://www.ml-quant.com/papers/ssrn/7512099/) (2026-09-25): Factor momentum strategies earn 49 basis points per month extra return following quarters of rapid margin-debt growth, a predictability that persists after publication and reflects limits to arbitrage correction.
- [Execution-Aware Alpha Mining: Teaching LLM Factor Agents to Account for Trading Costs](https://www.ml-quant.com/papers/ssrn/7498983/) (2026-09-25): The paper builds a closed-loop system where an LLM proposes equity factors penalized for execution costs and shows that accounting for trading costs dramatically improves net performance.
- [Prices or implied volatilities? Choosing the loss function in machine learning option pricing](https://www.ml-quant.com/papers/ssrn/7498639/) (2026-09-25): The paper compares machine learning option pricing trained on pricing errors versus implied-volatility errors using 8.67 million S&P 500 index-option observations from 1997 through 2025.
- [Monetary policy transmission by securitising banks](https://www.ml-quant.com/papers/ssrn/7515879/) (2026-09-25): Banks engaged in securitization contract lending more sharply after monetary tightening because their investor base demands higher returns and cuts risk exposure when rates rise.
- [Hedge Fund Trading and Sovereign Bond Yield Sensitivity](https://www.ml-quant.com/papers/ssrn/7506360/) (2026-09-25): Leveraged hedge fund positions amplify sovereign bond yield sensitivity to monetary shocks by over a quarter through directional rebalancing, with effects scaling to position intensity.
- [Firm-Specific Price Delay and Momentum](https://www.ml-quant.com/papers/ssrn/7518623/) (2026-09-25): Momentum profits concentrate among firms with high price delay, a measure of information friction, directly supporting theories that gradual information incorporation drives momentum.
- [Industry Information and Equity Return Predictability](https://www.ml-quant.com/papers/ssrn/7486138/) (2026-09-25): Using production, employment, and sales data across 426 industries, the research shows that upstream industry signals predict aggregate monthly stock returns with 23.8% out-of-sample R-squared.
- [Expectations and the Term Structure of Interest Rates](https://www.ml-quant.com/papers/ssrn/7497046/) (2026-09-25): Decomposing yield sensitivity without assuming rational expectations reveals that expectations rather than risk premia drive short- and medium-term bond yields, with systematic inconsistencies across horizons.
- [Hedge Fund Performance and Interest Rate Conditions: Evidence from Regulatory Data](https://www.ml-quant.com/papers/ssrn/7493702/) (2026-09-25): Using SEC filings from 2013-2021, the paper finds hedge fund returns show heterogeneous sensitivity to interest rates, with effects varying by strategy, leverage, and derivative exposure.
- [Settlement Risk and Currency Markets](https://www.ml-quant.com/papers/ssrn/7201787/) (2026-09-25): Hungary's 2015 adoption of payment-versus-payment settlement reduced currency excess returns by ten basis points, demonstrating settlement risk is a priced friction limiting arbitrage.
- [Tail-Risk Forecasting with General Cubic Distributions](https://www.ml-quant.com/papers/ssrn/7504480/) (2026-09-25): A cubic quantile framework forecasts Value-at-Risk and Expected Shortfall more reliably than GARCH benchmarks across eight equity indices without requiring a parametric density.
- [MartingaleONet: Physics-Constrained Operator Learning for Real-Time Option Pricing and Volatility Calibration](https://www.ml-quant.com/papers/ssrn/7498326/) (2026-09-25): A deep operator network maps volatility surfaces to option prices under the Heston model 15,000 times faster than finite-difference methods while reducing dynamic hedging variance by over 59% under transaction costs.
- [State-dependent global banking systemic risk: An integrated framework of network connectedness, tail risk, and global financial conditions](https://www.ml-quant.com/papers/ssrn/7493706/) (2026-09-25): Combining quantile-connectedness, tail-risk measures, and network analysis, the research shows tail connectedness exceeds median levels and lower-tail effects persist longer, with the VIX alone reliably predicting next-week systemic risk.
- [The Low Return Channel of Negative Interest Rates in Bank Lending](https://www.ml-quant.com/papers/ssrn/7489554/) (2026-09-25): Japan's 2016 negative-rate policy reduced lending from low-profitability banks holding reserves, consistent with lower expected returns on bank assets rather than deposit-side stress.
- [Banking-System Heterogeneity and Monetary Policy Transmission in the Euro Area: High-Frequency Shocks, Local Projections, and Regime Dependence](https://www.ml-quant.com/papers/ssrn/7519040/) (2026-09-25): A 100-basis-point contractionary monetary shock lowers inflation and sales across 20 euro-area economies, with transmission strength varying by bank asset-risk exposure and assets-to-GDP ratio rather than a simple weak-strong taxonomy.
- [Fedspeak, LLM-Derived Signals, and High-Frequency Trading](https://www.ml-quant.com/papers/ssrn/7479619/) (2026-09-25): Semantic and tonal shifts across sequential Federal Reserve communications generate significant intraday price movements and abnormal volume, revealing incomplete information absorption at initial announcement.
- [Crossing the Zero Lower Bound: Negative Interest Rates and Corporate Valuation](https://www.ml-quant.com/papers/ssrn/7512572/) (2026-09-25): Comparing firms across the ECB's 2014 negative rate adoption shows treated European firms had higher valuations but reduced leverage, suggesting cash-flow and discount-rate channels dominate tax-shield effects.
- [Signature-Based Structural Models and Applications in Credit Markets](https://www.ml-quant.com/papers/ssrn/7498599/) (2026-09-25): The study develops a time-varying signature asset model for structural credit that improves calibration across CDS maturities and equity option prices, especially for high-yield firms.
- [Data-Driven Minimax-Regret Portfolio Optimization under Tail-Risk Ambiguity](https://www.ml-quant.com/papers/ssrn/7486600/) (2026-09-25): The research proposes a data-driven portfolio method that blends tail-risk models and projects onto valid mixtures, providing bounds on Expected Shortfall regret without Wasserstein assumptions.
- [Sell, Hold Out, or Accept: The Creditor's Trilemma in Distressed Debt Exchanges](https://www.ml-quant.com/papers/ssrn/7502204/) (2026-09-25): Analysis of 284 distressed exchanges from 2009-2022 reveals over 50% of firms face subsequent default, with large illiquid creditors trapped in a prisoner's dilemma explaining high acceptance rates.
- [Analysis of Fundamental and Technical Financial Ford Motor Company with The Arrangements of Implication Black Volatility](https://www.ml-quant.com/papers/ssrn/4482880/) (2025-12-28): The study shows that Ford Motor Company had its smallest earnings per share payout gap in 2020 compared to previous years.
- [Gingado: A Machine Learning Library Focused on Economics and Finance](https://www.ml-quant.com/papers/ssrn/4482553/) (2025-12-28): ML for Economics: Gingado is a developing Python library that helps incorporate machine learning into economic research by enhancing datasets and evaluating models.
- [Investigating the Corporate Governance and Sustainability Relationship A Bibliometric Analysis Using Keyword-Ensemble Community Detection](https://www.ml-quant.com/papers/ssrn/4482116/) (2025-12-28): The paper explores how corporate governance relates to sustainability, emphasizing the need to consider stakeholder interests in long-term responsibility practices.
- [Cash vs. Crypto in DeFi](https://www.ml-quant.com/papers/ssrn/4478395/) (2025-12-28): The article discusses how cryptocurrencies can improve societal functions compared to traditional currencies and emphasizes the innovations needed to build confidence in decentralized finance.
- [Factors Undermining Quality of Medical-Care Services Delivered by a Physician in Today’s Medical-Care Market Country-Wise: Statistical Analysis](https://www.ml-quant.com/papers/ssrn/4474559/) (2025-12-28): This study examines the factors affecting the quality of medical services in Bangladesh, utilizing patient feedback and statistical analysis to pinpoint crucial influences on care quality.
- [Asset Prices, Collateral and Bank Lending: The Case of COVID-19 and Real Estate](https://www.ml-quant.com/papers/ssrn/4470421/) (2025-12-28): The paper investigates the euro area's banking system's role in transmitting asset price shocks to credit during the Covid-19 crisis, highlighting significant frictions and a decrease in lending related to real estate collateral.
- [Twitter Sentiment and Financial Trends](https://www.ml-quant.com/papers/ssrn/4467949/) (2025-12-28): A new financial sentiment index derived from Twitter data shows strong links to market conditions and can forecast stock market returns, particularly in response to changes in U.S. monetary policy.
- [Global Liquidity and Volatility](https://www.ml-quant.com/papers/ssrn/4482265/) (2025-12-28): Global liquidity from banks impacts responses to crises and eases funding strains internationally.
- [ESG in Auto Loans](https://www.ml-quant.com/papers/ssrn/4481545/) (2025-12-28): Higher ESG scores in auto loan securitizations lower costs and consumer interest rates, despite environmental concerns.
- [Vaccine Innovation Funding Strategy](https://www.ml-quant.com/papers/ssrn/4480682/) (2025-12-28): A portfolio approach to drug development may improve investment returns and speed up vaccine creation.
- [Robert C. Merton's Contributions](https://www.ml-quant.com/papers/ssrn/4480625/) (2025-12-28): Robert C. Merton is a significant finance scholar known for his work on derivatives pricing and finance theories.
- [Low Volatility Asset Valuation in Brazilian Stock Market: Lower Risk with Higher Returns](https://www.ml-quant.com/papers/ssrn/4480285/) (2025-12-28): Lower volatility Brazilian stocks have consistently outperformed high-volatility stocks in annual returns from 2003 to 2021.
- [Twitter and Monetary Policy](https://www.ml-quant.com/papers/ssrn/4479590/) (2025-12-28): Online discussions about central bank policies correlate strongly with market volatility, especially around ECB announcements.
- [Stochastic Social Preferences and Corporate Investment Decisions](https://www.ml-quant.com/papers/ssrn/4479486/) (2025-12-28): Investor preferences affect firms' green investments, potentially slowing down the transition to sustainable practices.
- [Corporate Bond Pricing Challenges](https://www.ml-quant.com/papers/ssrn/4478575/) (2025-12-28): The effectiveness of multifactor models for corporate bond returns is debated, with a preference for the bond CAPM in analyses.
- [Global Dollar Holdings Trends](https://www.ml-quant.com/papers/ssrn/4478513/) (2025-12-28): Foreign institutional investors significantly increased their USD security holdings, influenced by varying currency hedging demands.
- [Regulating Cash Holdings: Assessing Lost Returns in Mutual Funds](https://www.ml-quant.com/papers/ssrn/4478272/) (2025-12-28): Israeli mutual funds hold excessive cash, indicating a need for better liquidity management to reduce redemption risks.
- [Bitcoin's Price Alchemy: Unraveling the Influence of Macro Announcements on Volatility and Trading Volume in an Era of Rising Inflation](https://www.ml-quant.com/papers/ssrn/4478184/) (2025-12-28): Bitcoin's price volatility significantly reacts to FOMC and CPI announcements, showing unique patterns during inflation.
- [Bias in Credit Ratings](https://www.ml-quant.com/papers/ssrn/4478090/) (2025-12-28): Subscription-based credit rating agencies may have biases that lead to overly optimistic ratings, complicating conflict resolution.
- [Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks](https://www.ml-quant.com/papers/ssrn/4478013/) (2025-12-28): Analyzing Japanese stock charts with CNN reveals predictive patterns for returns, independent of common momentum trends.
- [Carbon Risk and Equity Prices](https://www.ml-quant.com/papers/ssrn/4476587/) (2025-12-28): Carbon transition risk is adversely affecting equity prices in the US and Europe, driving fund flows to greener investments.
- [Sustainable Investment in Climate](https://www.ml-quant.com/papers/ssrn/4475732/) (2025-12-28): Global investments in environmental and climate projects are diversifying as investors integrate more green initiatives into their portfolios.
- [Interpretable Machine Learning for Asset Pricing](https://www.ml-quant.com/papers/ssrn/4473746/) (2025-12-28): The paper utilizes deep neural networks to more accurately estimate equity risk premia over time, enhancing the interpretability of machine learning in economics.
- [Sparse Risk Parity Enhanced Index Tracking Portfolio](https://www.ml-quant.com/papers/ssrn/4470609/) (2025-12-28): It tackles a sparse risk parity portfolio problem for index tracking while managing asset risks, with successful results on the SP 500.
- [Chinese Bond Dynamics During COVID-19](https://www.ml-quant.com/papers/ssrn/4469784/) (2025-12-28): The study examines the changes in the Chinese government bond yield curve during the pandemic, highlighting new behaviors and arbitrage potential.
- [Optimal Trading with Costs and Predictability](https://www.ml-quant.com/papers/ssrn/4466658/) (2025-12-28): It establishes optimal trading rules for multiple assets with predictable returns, showing performance benefits through simulations.
- [The Banker in Your Social Network](https://www.ml-quant.com/papers/ssrn/4466139/) (2025-12-28): The research indicates that social financial advice significantly boosts stock market participation, especially through close social ties.
- [Asset Pricing and Stochastic Discount Factors](https://www.ml-quant.com/papers/ssrn/4465240/) (2025-12-28): The paper outlines the required conditions for modeling stock prices with characteristics-based factor portfolios, addressing covariate structure issues.
- [A probabilistic method for reconstructing the Foreign Direct Investments network in search of ultimate host economies](https://www.ml-quant.com/papers/ssrn/4464139/) (2025-12-28): It introduces Ultimate Host Economies for Foreign Direct Investment (FDI), reexamining the global FDI network through a probabilistic analysis of Italy.
- [Financial Intermediation and New Technology: Theoretical and Regulatory Implications of Digital Financial Markets](https://www.ml-quant.com/papers/ssrn/4464132/) (2025-12-28): The study highlights how technological changes are reshaping financial intermediaries, necessitating regulatory updates.
- [Financial Fragilities and Risk-taking of Corporate Bond Funds in the Aftermath of Central Bank Policy Interventions](https://www.ml-quant.com/papers/ssrn/4463970/) (2025-12-28): It finds that central bank asset purchases during the pandemic led corporate bond fund managers to take more risks, affecting market stability.
- [ESG Impact on Stock Prices](https://www.ml-quant.com/papers/ssrn/4463862/) (2025-12-28): The paper reveals that green firms experience smaller stock price declines than brown firms when interest rates rise due to sustainability preferences.
- [Tail Risk-Managed Portfolio Strategies](https://www.ml-quant.com/papers/ssrn/4463810/) (2025-12-28): It develops real-time Tail Risk-Managed portfolios that minimize tail risks and enhance risk-return profiles compared to standard strategies.
- [Sample Size Issues in Finance Research](https://www.ml-quant.com/papers/ssrn/4463148/) (2025-12-28): The study promotes the use of Bayesian statistics in finance to better analyze large AI-generated datasets and mitigate misleading significance from traditional methods.
- [Myopic Stock Pricing](https://www.ml-quant.com/papers/ssrn/4442760/) (2025-12-19): US. stock analysts' short-term focus leads to inaccurate price predictions due to varying expectations over different time frames.
- [The Cross-Section of Factor Returns](https://www.ml-quant.com/papers/ssrn/4441376/) (2025-12-19): Most of the 150 equity factors examined show positive returns but fail to deliver excess returns after accounting for risk, especially in downturns.
- [Romania's Roadmap to a Greener Financial System: An analysis of Environmental, Social and Governance Reporting on the Bucharest Exchange Trading Index](https://www.ml-quant.com/papers/ssrn/4440516/) (2025-12-19): Romania struggles to attract sustainable investments because its major companies have low transparency and high greenhouse gas emissions.
- [Financial Instruments for Decarbonization: Likely Pathways for the Romanian Economy](https://www.ml-quant.com/papers/ssrn/4440511/) (2025-12-19): The study highlights key financial tools in Romania, like green bonds and loans, which can help transition to a low-carbon economy, with banks playing a major role.
- [Asymptotic Expansions for High-Frequency Option Data](https://www.ml-quant.com/papers/ssrn/4440168/) (2025-12-19): A new method for analyzing financial data helps test for sudden volatility changes, with evidence from SP500 options indicating significant variation.
- [Satellite Census for Climate Risk in Housing](https://www.ml-quant.com/papers/ssrn/4975765/) (2025-12-01): The article suggests using open-source satellite data to map residential buildings worldwide, aiming to evaluate their vulnerability to climate risks and their environmental effects.
- [The Private Capital Alpha](https://www.ml-quant.com/papers/ssrn/4967890/) (2025-12-01): This study outlines a framework for estimating alpha in private capital, showing notable annual returns for buyouts but unreliable data for venture capital and real estate.
- [AI-Powered Direct Indexing: Exploring Thematic Universes for Enhanced Risk-Adjusted Returns](https://www.ml-quant.com/papers/ssrn/4977007/) (2025-12-01): The research presents FINDALL, a search engine that effectively identifies relevant stocks for direct indexing, outperforming traditional ETFs with lower costs.
- [Market Effects of Order Flow in Crypto](https://www.ml-quant.com/papers/ssrn/4961610/) (2025-12-01): Analysis indicates that payment for order flow in crypto markets increases trading costs and reduces volumes, especially for assets beyond Bitcoin and Ethereum, after new tokens are introduced.
- [CREDIT DERIVATIVE -An Alternative Tool for Indian Commercial Banks to Transfer Credit Risk](https://www.ml-quant.com/papers/ssrn/4973692/) (2025-12-01): Poor credit risk management in Indian banks has led to rising Non-Performing Assets, highlighting the need for modern risk tools, such as credit derivatives, to improve future performance.
- [European Real Estate Volatility](https://www.ml-quant.com/papers/ssrn/4964818/) (2025-12-01): This study shows that different European real estate markets have varying volatility and suggests using tactical asset allocation to improve investment performance.
- [Legal Implications of Tax Securitization Transactions in India](https://www.ml-quant.com/papers/ssrn/4971150/) (2025-12-01): The paper examines India's growing asset securitization trend and the confusing tax issues that come with it.
- [Mutual Fund Decline in 401(k)s](https://www.ml-quant.com/papers/ssrn/4960502/) (2025-12-01): This research highlights the rise of collective investment trusts in 401k plans due to their lower costs and tailored options for investors.
- [Behavioral Biases in Fund Management](https://www.ml-quant.com/papers/ssrn/4961553/) (2025-12-01): The study looks at how mutual fund performance is influenced by internal biases when large amounts of capital are invested.
- [Navigating the Low-Carbon Shift: Balancing Municipal Finances with Climate Goals](https://www.ml-quant.com/papers/ssrn/4955515/) (2025-12-01): This research details how falling coal production negatively impacts municipal finances, leading to higher debt and bond yields in less diverse counties.
- [Gender Diversity's Effect on Firm Risk](https://www.ml-quant.com/papers/ssrn/4956112/) (2025-12-01): This research indicates that having more women in a company's leadership improves risk management, especially in uncertain times.
- [Early Crash Signal (AE)](https://www.ml-quant.com/papers/ssrn/4930925/) (2025-11-19): The paper proposes a simple early-warning signal that watches hidden market patterns found by a neural network (an autoencoder); when those patterns start moving together it warns of systemic market risk, helping investors time sell-offs and improve returns.
- [Hedge Funds in German Bonds](https://www.ml-quant.com/papers/ssrn/5057388/) (2025-10-27): Daily data (2005–2024) show hedge funds became key liquidity providers in German government bonds after 2015 as banks cut back due to higher balance‑sheet costs.
- [Private Video Game Returns](https://www.ml-quant.com/papers/ssrn/5047711/) (2025-10-27): Analysis of 631 private video‑game deals finds game investments outperform similar private and public deals, making game-focused funds attractive.
- [ESG Alpha in Corporate Bonds](https://www.ml-quant.com/papers/ssrn/5051908/) (2025-10-27): Firms' environmental traits create a distinct bond-market anomaly that improves portfolios beyond standard factors, and a simple model explains it.
- [Returns to Scale in Fund Management](https://www.ml-quant.com/papers/ssrn/5246703/) (2025-07-03): Fund managers can mitigate the adverse effects of competition on fund alpha by adjusting their level of active management, especially in response to competition from passive funds.
- [Cloud-Native AI Framework](https://www.ml-quant.com/papers/ssrn/5237913/) (2025-06-25): The article emphasizes the necessity for Big Tech firms to revamp their cloud infrastructures for better handling of machine learning tasks, and offers a guiding framework for this transformation.
- [Volatility Forecasting Models Comparison](https://www.ml-quant.com/papers/ssrn/5241995/) (2025-06-25): The paper finds that volatility models are most accurate when they match the data-generating process.
- [The FMA indicator: An index based exclusively on dividends](https://www.ml-quant.com/papers/ssrn/5241825/) (2025-06-25): The article introduces a technical analysis approach that uses dividends to predict a share's lifetime price range.
- [Beware of Large Shocks! A Non-Parametric Structural Inflation Model](https://www.ml-quant.com/papers/ssrn/5244264/) (2025-06-25): The study introduces a Bayesian machine learning model for inflation that reacts strongly to large shocks.
- [Operational Flexibility Impact on Firm Risk](https://www.ml-quant.com/papers/ssrn/5234344/) (2025-06-25): The paper shows that operational flexibility reduces implied volatility and equity cost, based on the introduction of an exit option.
- [Fiscal Financing and Investment Reversibility](https://www.ml-quant.com/papers/ssrn/5245203/) (2025-06-25): The research shows that dividend tax hikes initially cause investment inactivity, followed by a surge due to tax arbitrage and hangover effects.
- [NLP Axioms in Tamil Dialects](https://www.ml-quant.com/papers/ssrn/5245598/) (2025-06-25): The article investigates the challenges and opportunities in using Machine Learning for processing Classical Tamil language and its dialects, with a focus on linguistic and cultural aspects.
- [Simulation Study for Port Flows](https://www.ml-quant.com/papers/ssrn/5239631/) (2025-06-25): The research uses machine learning and a simulation model to improve accuracy in analyzing import container flows at the Port of New York-New Jersey.
- [AI in Tourism: Kerala Study](https://www.ml-quant.com/papers/ssrn/5242805/) (2025-06-25): Kerala Study: The study introduces the SMART AI-Driven Tourism Marketing Framework to boost tourist engagement in Kerala, using AI chatbots, predictive analytics, and personalized content.
- [Racial Challenges of AI in Economics](https://www.ml-quant.com/papers/ssrn/5237156/) (2025-06-25): The article claims that creating fair and racially-just machine learning is currently unachievable due to reasons like biased training data and opaque algorithm design.
- [Resilient Supply Chain Design](https://www.ml-quant.com/papers/ssrn/5235268/) (2025-06-25): The research proposes a framework for enhancing supply chain resilience during large-scale disruptions, focusing on supply chain design and vulnerabilities.
- [Stock Returns in Supply Chain Pressure](https://www.ml-quant.com/papers/ssrn/5245195/) (2025-06-25): The study shows that global supply chain pressures significantly contribute to negative forward returns in the U.S. stock market.
- [Unified Econometrics Discipline](https://www.ml-quant.com/papers/ssrn/5245790/) (2025-06-25): The article discusses the rapid growth and transformation of econometrics, emphasizing advances in cross-sectional data analysis, policy analysis, and time series techniques.
- [Artificial Intelligence and Relationship Lending](https://www.ml-quant.com/papers/ssrn/5241807/) (2025-06-25): The research explores the impact of AI adoption in credit scoring and relationship lending by banks, suggesting that AI investments can help banks manage the effects of relationship lending on credit supply and decisions.
- [Options on Drugs: Industry Exposure and Option Anomalies](https://www.ml-quant.com/papers/ssrn/5245162/) (2025-06-25): Pharmaceutical stocks offer higher returns when writing options due to their high growth potential and the unpredictability of drug trials and development.
- [Common Task Framework](https://www.ml-quant.com/papers/ssrn/5242901/) (2025-06-25): The Common Task Framework (CTF) can enhance innovation, effort, and honesty in research, and could be used in financial economics to assess asset pricing models.
- [AI in Day Trading](https://www.ml-quant.com/papers/ssrn/5246516/) (2025-06-25): A new intraday trading strategy for the USTEC CFD, using a large language model to filter trading signals based on news sentiment, boosts profitability.
- [IRS Officials' Trades](https://www.ml-quant.com/papers/ssrn/5245652/) (2025-06-25): IRS officials' personal stock trades yield positive abnormal returns and are linked to future tax enforcement outcomes for the invested firms.
- [FOMC Announcement Premiums](https://www.ml-quant.com/papers/ssrn/5237922/) (2025-06-25): Currency risk premiums fluctuate on U.S. FOMC announcement days, with currencies expecting a larger reduction in implied variance earning higher returns.
- [Ambiguity Preference in Credit](https://www.ml-quant.com/papers/ssrn/5246313/) (2025-06-25): Ambiguity preference variables can forecast credit asset comovements, with lower-rated US credit assets being more affected by ambiguity aversion.
- [WPI Students' Financial Knowledge](https://www.ml-quant.com/papers/ssrn/5245648/) (2025-06-25): Despite a strong interest in learning about financial preparedness, college students lack financial literacy and understanding of investing and debt.
- [Digital Asset Regulation in the U.S.](https://www.ml-quant.com/papers/ssrn/5245602/) (2025-06-25): The U.S.'s individual approach to digital asset regulation could isolate its markets and weaken its monetary power, indicating a need for global collaboration.
- [The Bond Agio](https://www.ml-quant.com/papers/ssrn/5243376/) (2025-06-25): Bonds issued in varying interest rate environments have different coupons and market prices, leading to higher losses for investors if default occurs.
- [Tax Losses and Capped Call Spreads](https://www.ml-quant.com/papers/ssrn/5238942/) (2025-06-25): Capital-loss carryforwards can be assessed using an option-pricing perspective, providing a market consistent basis for determining when such strategies yield real economic benefits.
- [Inflation Risk and Production Costs](https://www.ml-quant.com/papers/ssrn/5241864/) (2025-06-25): Companies with high fixed costs show negative exposure to inflation surprises, with variable costs being the main factor behind this negative inflation beta.
- [Electricity Price Volatility & Financial Stress](https://www.ml-quant.com/papers/ssrn/5286268/) (2025-06-18): The research explores the link between electricity price changes and financial stress in Europe, suggesting market-based pricing and diverse energy supplies.
- [Fourier Feature Physics-Informed NN](https://www.ml-quant.com/papers/ssrn/5286781/) (2025-06-18): The study introduces a tuning-free framework that accurately represents multifrequency responses in structural dynamics equations, removing the need for manual tuning.
- [Social Group Bias in AI Finance](https://www.ml-quant.com/papers/ssrn/5287153/) (2025-06-18): The article examines racial bias in financial decision-making models, suggesting a method to reduce racial disparities without affecting model performance.
- [ChatGPT for Student Engagement](https://www.ml-quant.com/papers/ssrn/5287045/) (2025-06-18): The paper highlights the use of ChatGPT to increase student engagement in online and hybrid learning settings.
- [Optimal Lotteries in Non-Convex Economies](https://www.ml-quant.com/papers/ssrn/5233164/) (2025-06-18): A new method has been developed for solving optimal lotteries in models with nonconvexities, proving more efficient than traditional methods.
- [Bayesian VAR Count Data Forecasting](https://www.ml-quant.com/papers/ssrn/5285954/) (2025-06-11): The article introduces a new method for predicting and modeling time series data, capable of managing overdispersion, skewness, and changing volatility.
- [Gamma Scalping American Option Valuation](https://www.ml-quant.com/papers/ssrn/5285239/) (2025-06-11): The paper reassesses the strategy and value of American-style options contracts, highlighting the importance of gamma scalping profitability in the decision to stop.
- [Market Power Abuse Electricity Markets](https://www.ml-quant.com/papers/ssrn/5286366/) (2025-06-11): The study examines the relationship between hedging and potential market power abuse in wholesale electricity markets, calculating the hourly economic incentives for non-competitive behavior.
- [Algorithmic Bias Anti-Discrimination Law](https://www.ml-quant.com/papers/ssrn/5283387/) (2025-06-11): The article explores the legal consequences of predictive uncertainty in machine learning systems under UK anti-discrimination law, stressing the significance of policy and design decisions.
- [Liquidity Flows Bank-Affiliated Broker Dealers](https://www.ml-quant.com/papers/ssrn/5287025/) (2025-06-11): The paper studies the function of repo lending within the same bank holding company, discovering that internal liquidity channels aid in distributing liquidity from high-reserve banks to the broader financial system.
- [The Effect of BNPL on Consumer Debt and the Ability to Repay Non-BNPL Debt Obligations](https://www.ml-quant.com/papers/ssrn/5284530/) (2025-06-11): The paper uses a unique dataset to measure the effect of first-time Buy Now Pay Later (BNPL) use on non-BNPL consumer debt and repayment ability, finding no negative consequences.
- [SP 500 Index Option Returns Market Reversals](https://www.ml-quant.com/papers/ssrn/5284206/) (2025-06-11): The article presents new evidence supporting demand-based option pricing theory and the limits of arbitrage in option pricing, indicating that imperfect hedging or weekly rebalancing yield higher risk-adjusted returns for option writers.
- [Mathematical Causal Graphs](https://www.ml-quant.com/papers/ssrn/5284544/) (2025-06-11): The paper presents a mathematical framework for studying Causal Graphs with Dynamic Trace GCTD, aiming to pioneer a new research field in discrete mathematics and network theory.
- [Climate Normals Estimation](https://www.ml-quant.com/papers/ssrn/5284152/) (2025-06-11): The article highlights the need to quantify the interannual variability in climatological time series for accurate El NiñoSouthern Oscillation predictions, questioning current methodologies' effectiveness in a changing climate.
- [AI Casino Regulation in Macao](https://www.ml-quant.com/papers/ssrn/5285013/) (2025-06-11): The article announces the advent of the AI Casino era.
- [Time Series Stationarity Testing](https://www.ml-quant.com/papers/ssrn/5287311/) (2025-06-11): The article emphasizes the importance of the DickeyFuller Test and Augmented DickeyFuller ADF Test in confirming time series stationarity, crucial in actuarial science, quantitative finance, and machine learning.
- [Financial Optimization Strategies](https://www.ml-quant.com/papers/ssrn/5286592/) (2025-06-11): The paper suggests a new approach to handle model uncertainty in quantitative finance, proposing an ad hoc subsampling strategy when a natural model distribution is absent.
- [Vision-Language Model Evaluation](https://www.ml-quant.com/papers/ssrn/5283704/) (2025-06-11): The study introduces a surrogate model to assess the resilience of vision-language models to minor perturbations, using adversarial perturbations in text and image modalities.
- [Fraud Detection Diffusion Model](https://www.ml-quant.com/papers/ssrn/5285870/) (2025-06-11): The paper presents a class-balanced diffusion model to enhance credit card fraud detection, using a two-stage process to improve the quality of minority-class samples and remove noisy synthetic samples.
- [Artificial Intelligence in Tax Administration: Enhancing Compliance, Transparency, and Ethical Governance](https://www.ml-quant.com/papers/ssrn/5285760/) (2025-06-11): The article discusses the potential of AI, particularly NLP, ML, and intelligent chatbots, to improve tax administration, while also considering the ethical and regulatory challenges of AI deployment.
- [Patent Descriptions](https://www.ml-quant.com/papers/ssrn/5284940/) (2025-06-11): A new dataset has been developed using natural language processing and machine learning, providing detailed tech information about US public firms and patents over 30 years, aiding profitable trading strategies.
- [Safety in a Global World](https://www.ml-quant.com/papers/ssrn/5285858/) (2025-06-11): A proposed portfolio theory framework models safety as a variable, investor-specific property that changes based on geographical, political, and institutional factors, rather than assuming a universally risk-free asset.
- [Firm Linkages](https://www.ml-quant.com/papers/ssrn/5286827/) (2025-06-11): The new Characteristic Vector Linkages (CVLs) method estimates firm linkages and constructs profitable momentum spillover trading strategies, with Quantum Cognition Machine Learning outperforming Euclidean similarity.
- [Deep IV Factor Models](https://www.ml-quant.com/papers/ssrn/5283770/) (2025-06-11): The Deep Implied Volatility Factor Model, combining neural networks and linear regression, is proposed for estimating the daily Implied Volatility surface of individual stock options, improving performance around earnings announcements.
- [Impact of Anti-ESG Policies on Bonds](https://www.ml-quant.com/papers/ssrn/5287090/) (2025-06-11): Anti-ESG policies in Texas and Oklahoma have not significantly affected municipal bond markups or yields, contradicting the idea that such policies raise borrowing or transaction costs.
- [Portfolio Skewness with Semidefinite Relaxation](https://www.ml-quant.com/papers/ssrn/5284483/) (2025-06-11): A method to estimate higher portfolio moments like skewness using semidefinite relaxation is presented, showing that portfolio skewness can enhance the skewness of the optimal portfolio.
- [FAIR Framework for Financial Systems](https://www.ml-quant.com/papers/ssrn/5285784/) (2025-06-11): The FAIR framework is expanded to tackle temporal challenges in financial operations, offering guidelines for financial institutions using Large Language Models and autonomous systems.
- [Climate Metrics for Investments](https://www.ml-quant.com/papers/ssrn/5283074/) (2025-06-11): The integration of climate metrics into investment portfolios as optimization constraints is demonstrated, indicating that the MSCI World Index can handle high integration of climate metrics with minimal performance or tracking error losses.
- [Business Cycles and Information Networks](https://www.ml-quant.com/papers/ssrn/5284645/) (2025-06-11): Network informativeness, or the ease of information flow across sectors, is a leading indicator of real business cycles, with increased information flow leading to stronger industrial production and real GDP.
- [Quantum Machine Learning for Trading](https://www.ml-quant.com/papers/ssrn/5282586/) (2025-06-11): The study investigates the use of quantum machine learning to optimise high-frequency trading strategies in US treasuries and forex markets.
- [How reinforcement learning can drive personalized financial wellness](https://www.ml-quant.com/papers/ssrn/5276883/) (2025-06-11): The study suggests a new approach combining reinforcement learning, behavioral analytics, and natural language processing for personalized financial advice.
- [Customer Behavior Prediction in E-Commerce](https://www.ml-quant.com/papers/ssrn/5284346/) (2025-06-11): The article explores the application of data analytics and machine learning in ecommerce for predicting customer behavior and optimizing marketing strategies.
- [Dynamic Currency Mispricing](https://www.ml-quant.com/papers/ssrn/5285379/) (2025-06-11): A study reveals that mispricing in currency markets is common and is more influenced by currency characteristics than macroeconomic fundamentals.
- [Commodity Futures Investment Process](https://www.ml-quant.com/papers/ssrn/5286928/) (2025-06-11): Hilary Till discusses the commodity investment universe, covering topics like investment focus, return rationale, portfolio construction, and risk management.
- [Structuring a Finance Fund](https://www.ml-quant.com/papers/ssrn/5283031/) (2025-06-11): The paper outlines a framework for blended finance funds, highlighting the strategic use of concessional capital with private investment to promote sustainable development goals.
- [Export Repatriation and Exchange Rates](https://www.ml-quant.com/papers/ssrn/5287255/) (2025-06-11): Mandatory export proceeds repatriation doesn't significantly affect exchange rate volatility in Iran, Sri Lanka, and Turkey, as per a study using the Generalized Synthetic Control framework.
- [Reinforcement Learning for Life Insurance Hedging](https://www.ml-quant.com/papers/ssrn/5279418/) (2025-06-04): A new framework using deep reinforcement learning is suggested to improve the hedging of specific risk factors in financial instruments, using Shapley value decompositions to assign profit and loss to different risk categories.
- [Kelly Betting with Constraints](https://www.ml-quant.com/papers/ssrn/5281529/) (2025-06-04): A revised Kelly optimization is proposed that includes a probabilistic recovery constraint, balancing long-term growth with short-term recovery risk, especially beneficial for strategies with skewed returns like short volatility or insurance underwriting.
- [Market News Attention Hype Index](https://www.ml-quant.com/papers/ssrn/5279231/) (2025-06-04): The Hype Index is presented as a measure to quantify media attention towards large-cap equities, using Natural Language Processing to extract predictive signals from financial news.
- [Shifted Wishart Processes Portfolio Optimization](https://www.ml-quant.com/papers/ssrn/5277926/) (2025-06-04): The Markov-Modulated Shifted Wishart (MMSW) process is utilized to capture covariance dynamics in a portfolio optimization problem, providing a flexible strategy that adapts to sudden market stress and maintains diversification benefits.
- [HighDimensional Data Privacy](https://www.ml-quant.com/papers/ssrn/5278724/) (2025-06-04): The article explores a new data analysis and protection method that merges principal component analysis with differential privacy for improved handling of high-dimensional data and privacy protection.
- [Portfolio Optimization with RL](https://www.ml-quant.com/papers/ssrn/5276183/) (2025-06-04): The authors suggest a new approach to portfolio optimization that incorporates turnover cost and diversification into a convex optimization framework, using reinforcement learning-based control.
- [Machine Learning Rare Earth Alloys](https://www.ml-quant.com/papers/ssrn/5279446/) (2025-06-04): The authors have created advanced machine learning models to expedite the research and development process of advanced RE Al alloys.
- [AI Asset Pricing Impacts](https://www.ml-quant.com/papers/ssrn/5277572/) (2025-06-04): The article presents a model that examines the impact of AI on the economy, portfolio choices, and asset prices, suggesting that AI increases output growth and volatility and influences investor behavior.
- [Imbalanced Node Classification Exploration](https://www.ml-quant.com/papers/ssrn/5279529/) (2025-06-04): The authors introduce a new method, Topological Node Exploration and Suppression, to tackle the problem of imbalanced class distribution in graph data for semi-supervised learning in Graph Machine Learning.
- [Fracture Characterization Hybrid HMA](https://www.ml-quant.com/papers/ssrn/5276534/) (2025-06-04): The study uses AI and machine learning to detect and measure crack length development in semi-circular bending beam specimens, showcasing the effectiveness of the YOLOv8 algorithm in predicting crack lengths.
- [Enterprise Risk Management Mergers & Acquisitions](https://www.ml-quant.com/papers/ssrn/5275009/) (2025-06-04): The article discusses the discrepancy between the theoretical and practical value of Enterprise Risk Management, emphasizing the need to concentrate on ERM's potential to improve strategic decision-making.
- [High-D Learning in Finance](https://www.ml-quant.com/papers/ssrn/5281959/) (2025-06-04): The article investigates the role of machine learning in financial forecasting, focusing on the impact of standardization in Random Fourier Features and the challenges of learning in low signal-to-noise environments.
- [Relationship & Housing Choices](https://www.ml-quant.com/papers/ssrn/5280132/) (2025-06-04): The research uses a life cycle model to study how market conditions affect individuals' relationship and housing choices, indicating different experiences for single individuals and those in relationships.
- [A FOMO-based Capital Asset Pricing Model](https://www.ml-quant.com/papers/ssrn/5276817/) (2025-06-04): The paper presents a Fear of Missing Out (FOMO) Capital Asset Pricing Model, suggesting that investors gain satisfaction from avoiding underperformance compared to their peers.
- [Sovereign Debt Post Downgrades](https://www.ml-quant.com/papers/ssrn/5276268/) (2025-06-04): The research explores how governments adjust their funding structure in response to changes in credit ratings, with a shift from bonds to loans mainly occurring in countries with low ratings.
- [Intangible Intensity & Performance](https://www.ml-quant.com/papers/ssrn/5281429/) (2025-06-04): The study examines the link between intangible asset intensity and abnormal net hiring in U.S. firms, finding a positive correlation with both intangible asset intensity and annual intangible investment.
- [Economists' Criticisms of Crypto](https://www.ml-quant.com/papers/ssrn/5277960/) (2025-06-04): The article assesses criticisms of cryptocurrencies by top economists, juxtaposing them with recent advancements and counterarguments in the field.
- [Equity Market Response to Shocks](https://www.ml-quant.com/papers/ssrn/5278755/) (2025-06-04): The note reexamines the impact of monetary policy on equity prices, considering changes in interest rates, term premia, and dividend risk compensation.
- [Anomaly Persistence](https://www.ml-quant.com/papers/ssrn/5276723/) (2025-06-04): The article introduces a method for testing asset pricing anomalies, showing that multiple paths on the same dataset lead to high outcome correlations, significantly affecting inference.
- [Time Preference and Premium](https://www.ml-quant.com/papers/ssrn/5278998/) (2025-06-04): The research studies the time variations of forward premiums in the currency market, pinpointing variables that can predict these changes, especially in less-developed countries.
- [Predicting S&P 500 Trajectories](https://www.ml-quant.com/papers/ssrn/5279690/) (2025-06-04): The 3MR Reactive Valuation Model is introduced as a dynamic, retrospective alternative to the dividend discount model, explaining how investors value earnings without using the discounting approach.
- [ESG Mania and Trading](https://www.ml-quant.com/papers/ssrn/5278151/) (2025-06-04): The study explores the motivations and economic outcomes of institutional investors flocking to the ESG stock market, uncovering evidence of impact-washing rather than impact-chasing.
- [Optimising Large Language Models](https://www.ml-quant.com/papers/ssrn/5278456/) (2025-06-04): The article categorises and reviews the optimisation strategies used in Large Language Models like ChatGPT, Claude LlaMA, and DeepSeek.
- [Big Data Analytics in Finance](https://www.ml-quant.com/papers/ssrn/5275567/) (2025-06-04): The piece highlights the benefits of using Big Data Analytics and predictive modeling in Risk Management within Banks and Financial Services Companies.
- [The Role Of Machine Learning In Predicting Market Crashes And Preventing Flash Crashes 2024](https://www.ml-quant.com/papers/ssrn/5278265/) (2025-06-04): The research discusses the role of Machine Learning in predicting market crashes and flash crashes, and the complexities involved.
- [Generative AI for Synthetic Data Creation](https://www.ml-quant.com/papers/ssrn/5268010/) (2025-06-04): The paper discusses the use of Generative AI models for synthetic data generation, and how synthetic data can enhance model performance and facilitate privacy-preserving data sharing.
- [Online Gambling Forums](https://www.ml-quant.com/papers/ssrn/5266388/) (2025-06-04): The research explores the use of a Reddit gambling forum as a data source for reducing harm in online gambling.
- [Crime Prediction with Data Mining](https://www.ml-quant.com/papers/ssrn/5266531/) (2025-06-04): The paper employs machine learning and deep learning models to predict and categorize crime, with the RNN-LSTM model proving most accurate.
- [RealTime IV Surface Forecasting](https://www.ml-quant.com/papers/ssrn/5275880/) (2025-06-04): A novel two-step real-time sequential forecasting framework is introduced for predicting option implied volatility surface, which performs better than random walk forecasts.
- [Multiverse Asset Pricing Model](https://www.ml-quant.com/papers/ssrn/5265948/) (2025-06-04): The study critiques the Capital Asset Pricing Model for its free parameter problem and proposes a multiverse asset pricing model, which allows for multiple equilibria and is based on investment beliefs.
- [Public Pension Funds and Risk](https://www.ml-quant.com/papers/ssrn/5276222/) (2025-06-04): A study reveals that U.S. public pension funds take on more risk when risk-free rates and funding ratios are low or their sponsors are financially weak.
- [Evaluating Green Investment Awareness among Salaried Women: A Case Study of Ludhiana](https://www.ml-quant.com/papers/ssrn/5267749/) (2025-06-04): A study in Ludhiana, India, shows women are generally open to green investments, but lack of information and perceived risks remain as barriers.
- [Commodities Returns and Alternatives](https://www.ml-quant.com/papers/ssrn/5276594/) (2025-06-04): Hilary Till spoke at the Women Investment Professionals organization in Chicago, discussing commodity indices, futures contracts, and hedge funds.
- [Co-Jump Asymmetry in Equity Markets](https://www.ml-quant.com/papers/ssrn/5268226/) (2025-06-04): Stocks with higher cojump asymmetry, indicating more left-skewed jump codependence, yield higher average monthly returns, a study using high-frequency stock return data shows.
- [A Presentation on Inferring Energy Fundamentals through Price-Relationship Data](https://www.ml-quant.com/papers/ssrn/5277217/) (2025-06-04): Hilary Till spoke at the 7th Annual International Trading Conference in South Korea, discussing the potential and challenges of big data and insights from futures price data.
- [Venture Capitalists](https://www.ml-quant.com/papers/ssrn/5274809/) (2025-05-30): Personal investments by Venture Capital partners can negatively affect their institutional investments, particularly if they have significant experience in institutional investing.
- [AI Financial Advisory](https://www.ml-quant.com/papers/ssrn/5268858/) (2025-05-30): AI-powered roboadvisors are transforming wealth management by improving accessibility and efficiency, despite issues such as data privacy and regulatory obstacles.
- [Sequence-Space Jacobians](https://www.ml-quant.com/papers/ssrn/5274675/) (2025-05-30): A new algorithm simplifies the calculation of sequence-space Jacobians in overlapping generations models, aiding in their analysis in general equilibrium.
- [The Bank of Italy’s Statistical Model for the Credit Assessment of Non-Financial Firms](https://www.ml-quant.com/papers/ssrn/5270521/) (2025-05-30): The Bank of Italy uses a combination of statistical models and expert assessments in its in-house credit assessment system to predict default probabilities of non-financial firms, aiding in monetary policy.
- [Fine-Tuning Large Language Models for Financial Markets via Ontological Reasoning](https://www.ml-quant.com/papers/ssrn/5274196/) (2025-05-30): Large Language Models struggle with accuracy in specialized fields due to lack of specific knowledge in training data, a problem that can be solved by fine-tuning with domain-specific data.
- [Optimal Learning Schedules](https://www.ml-quant.com/papers/ssrn/5272363/) (2025-05-30): A link between stochastic approximation and Kalman filtering has been found, leading to an online algorithm that adaptively tracks variances and achieves optimal learning rates.
- [Analytics-Literate Auditors, Consulting Business Opportunities, and Audit Quality](https://www.ml-quant.com/papers/ssrn/5267522/) (2025-05-30): The impact of data analytics on audit quality depends on client consulting potential, with audit offices having high analytics capabilities more likely to disengage from high potential consulting clients.
- [Quantum Credit Default Prediction](https://www.ml-quant.com/papers/ssrn/5273166/) (2025-05-30): A new model combining quantum and classical machine learning has been proposed to improve the accuracy of credit default predictions in emerging markets.
- [Stochastic Malliavin Calculus](https://www.ml-quant.com/papers/ssrn/5273649/) (2025-05-30): A comprehensive framework for Malliavin calculus has been developed, extending to infinite-dimensional Wiener space and enabling rigorous computational methods for stochastic analysis.
- [Linear Regression Residual Decomposition](https://www.ml-quant.com/papers/ssrn/5273170/) (2025-05-30): A new method for decomposing residuals in linear regression models has been introduced, offering detailed analysis of prediction errors and an overfitting detection index.
- [Hedge Fund Strategies with AI](https://www.ml-quant.com/papers/ssrn/5267626/) (2025-05-30): A study compares the role of AI and Machine Learning in hedge fund trading strategies, evaluating an AI-driven trading model against human-recommended trades.
- [Neural Network Logarithm Entropy Estimator](https://www.ml-quant.com/papers/ssrn/5274641/) (2025-05-30): A new LogDet estimator has been proposed to address the challenges of handling high-dimensional samples in machine learning using entropy estimators.
- [Macroeconomic Stability Quantum Model](https://www.ml-quant.com/papers/ssrn/5269697/) (2025-05-30): A novel theoretical framework uses quantum mechanics principles to model inflation and macroeconomic systems, treating economic goods and assets as quantum-like particles.
- [Aircraft Accident Detection with Computer Vision](https://www.ml-quant.com/papers/ssrn/5273380/) (2025-05-30): A systematic review of the literature on the use of computer vision techniques in detecting victims in aviation accidents has been conducted, with a focus on search and rescue operations.
- [Safe Asset Emergence in Global Housing Returns](https://www.ml-quant.com/papers/ssrn/5269749/) (2025-05-30): New data and time series on global real estate returns over centuries have been presented, combining historical primary data with machine learning approaches and econometrics.
- [Modeling Volatility Spillovers Between Petroleum and Stocks](https://www.ml-quant.com/papers/ssrn/5273787/) (2025-05-30): The study analyzes the relationship between petroleum prices and stock sector indices in Canada, Saudi Arabia, the US, and China, revealing diverse volatility interdependencies and fluctuating optimal portfolio weights and hedge ratios.
- [Hybrid Models for Forecasting](https://www.ml-quant.com/papers/ssrn/5268691/) (2025-05-30): The research uses traditional econometric models, machine learning, and deep learning techniques to predict financial time series, using SP 500 index and Bitcoin data, and assesses the models based on forecast error metrics and trading performance indicators.
- [How important are ESG factors for banks' cost of debt? An empirical investigation](https://www.ml-quant.com/papers/ssrn/5270535/) (2025-05-30): The paper explores the link between banks' ESG scores and their funding costs, concluding that higher ESG ratings positively influence funding costs and that changes in ESG ratings significantly impact banks' bond yields.
- [Comparative Analysis of Financial Data Techniques](https://www.ml-quant.com/papers/ssrn/5268353/) (2025-05-30): The study contrasts the traditional method of calculating logarithmic returns with the fractional differencing method in data preparation for machine learning models, finding that fractional differentiation methods enhance predictive model forecasting performance.
- [The Fundamental Role of the Repo Market and Central Clearing](https://www.ml-quant.com/papers/ssrn/5271022/) (2025-05-30): The paper examines the economic functions of repo contracts and the structure of government bond repo markets, investigates the main factors influencing euro-area repo market dynamics, and discusses the role of central clearing services and new client clearing models in the repo market.
- [Temperature Exposure and Firms' Green Revenues](https://www.ml-quant.com/papers/ssrn/5271517/) (2025-05-30): The research investigates the impact of temperature anomalies on firm-level risk, concluding that temperature beta affects firms' productivity, profitability, and cash flows, and that stocks with high temperature betas yield higher risk-adjusted returns.
- [Global Implications of AI in Investment Advisory](https://www.ml-quant.com/papers/ssrn/5270350/) (2025-05-30): The article discusses the transformative effects of AI on the global financial system, including benefits and potential issues like algorithmic bias and data privacy.
- [Relationships, Inventories, and the Value of Private Information in a Decentralized Market](https://www.ml-quant.com/papers/ssrn/5271444/) (2025-05-30): The research finds that dealers do not exploit private information in client-dealer relationships and such relationships do not necessarily improve client prices.
- [Quantum Estimation of Volatility Models](https://www.ml-quant.com/papers/ssrn/5274549/) (2025-05-30): The paper introduces two new methods for estimating stochastic volatility diffusions using Quantum-Inspired Classical Hidden Markov Models and Quantum Hidden Markov Models.
- [LP Net Profitability Analysis](https://www.ml-quant.com/papers/ssrn/5274557/) (2025-05-30): The study suggests a new AMM protocol to eliminate liquidity providers' impermanent loss, after examining the profitability of automated market maker liquidity providers in ETH/USD pools.
- [Historical Outcomes of 401(k) Investing](https://www.ml-quant.com/papers/ssrn/5267778/) (2025-05-30): The article concludes that only a few could have succeeded in 401k and defined contribution plans without extreme risk tolerance and thrift.
- [Risk and Loss Aversion in Financial Decision Making](https://www.ml-quant.com/papers/ssrn/5273091/) (2025-05-30): The research integrates risk and loss aversion into a model of household financial decision-making, finding that loss aversion significantly impacts participation, portfolio allocation, and savings decisions.
- [Resisting Populism through Financial Market Exposure: Experimental Evidence from Brexit](https://www.ml-quant.com/papers/ssrn/5273113/) (2025-05-30): Brexit Study: The paper indicates that exposure to financial markets can decrease support for populist policies, as shown by an RCT implemented before the Brexit referendum.
- [Farah Financial Model](https://www.ml-quant.com/papers/ssrn/5265196/) (2025-05-30): The Farah Model is a new method for predicting price changes in financial markets, linking them to volatility and volume dynamics.
- [Bayesian Hydraulic Model Calibration](https://www.ml-quant.com/papers/ssrn/5265012/) (2025-05-30): A Bayesian calibration framework uses convolutional neural networks to efficiently quantify uncertainty and infer parameters in flood-prone areas with limited data.
- [Youth Investment Trends](https://www.ml-quant.com/papers/ssrn/5267326/) (2025-05-30): Young people are increasingly investing in mutual funds and stocks, but financial independence is difficult due to reliance on family support or personal savings.
- [Corporate Finance Monte Carlo Simulation](https://www.ml-quant.com/papers/ssrn/5268498/) (2025-05-30): Monte Carlo Simulation is used to predict a company's financial outcomes for various events, with exotic derivatives offering new ways to reduce capital costs and increase shareholder return.
- [Elastic Structures Machine Learning](https://www.ml-quant.com/papers/ssrn/5263512/) (2025-05-30): A new approach for linear elasticity problems combines machine learning and the Matrix Discrete Empirical Interpolation Method to efficiently estimate problem output sensitivities.
- [Asset Pricing with Reference-dependent Preferences](https://www.ml-quant.com/papers/ssrn/5269148/) (2025-05-30): The study suggests that asset prices can be influenced by sentiment-driven preferences, which may explain inconsistencies in stock returns and economic fundamentals.
- [Hybrid Machine Learning for Malicious Mobile Apps](https://www.ml-quant.com/papers/ssrn/5267367/) (2025-05-30): The research presents a new method for detecting Android malware, using machine learning and permission analysis.
- [Data Governance for Global Banks](https://www.ml-quant.com/papers/ssrn/5263917/) (2025-05-30): The paper highlights the importance of robust data governance in banks for regulatory alignment, operational resilience, and accurate decision-making.
- [The asset pricing implications of dynamic asset supply in endowment economies](https://www.ml-quant.com/papers/ssrn/5265205/) (2025-05-30): The research shows that allowing the supply of financial assets to fluctuate over time can lead to persistent deviations in dividends.
- [Statistical Arbitrage within Crypto Markets using PCA](https://www.ml-quant.com/papers/ssrn/5263475/) (2025-05-30): The study investigates the effectiveness of principal component analysis in cryptocurrency trading, suggesting potential for improvement.
- [Minimizing Payout Volatility in Longevity Risk-Sharing](https://www.ml-quant.com/papers/ssrn/5266306/) (2025-05-30): The paper discusses the use of longevity risk-sharing pools in retirement plans, emphasizing the need for methods to reduce payout volatility.
- [Machine Learning for Corporate Fraud Detection](https://www.ml-quant.com/papers/ssrn/5263459/) (2025-05-30): The study assesses the use of machine learning in detecting accounting fraud, aiming to compare its effectiveness with traditional models.
- [Crypto Portfolio Risk and Reward](https://www.ml-quant.com/papers/ssrn/5264628/) (2025-05-30): The study finds that the Minimum Variance Portfolio performs best in cryptocurrency selection, but lacks diversification.
- [Ukrainian Banks in Bond Market](https://www.ml-quant.com/papers/ssrn/5268333/) (2025-05-30): The article explores the paradox of Ukrainian banks' excessive activity in the government bonds market despite ample liquidity and positive financial results.
- [Public Disclosures and Capital](https://www.ml-quant.com/papers/ssrn/5264116/) (2025-05-30): The research analyzes firms' disclosure decisions and investors' incentives, concluding that the current equilibrium is socially inefficient.
- [Environmental Data Scores](https://www.ml-quant.com/papers/ssrn/5270713/) (2025-05-30): The paper proposes a new classification system for environmental scores, aiming to better assess unrated companies and guide investment strategies.
- [Case Studies and Risk Management in Commodity Derivatives Trading: A Presentation](https://www.ml-quant.com/papers/ssrn/5267622/) (2025-05-30): Hilary Till discussed various risk management aspects at a meeting of the Professional Risk Managers International Association in Chicago.
- [Cross Trading Corporate Bonds](https://www.ml-quant.com/papers/ssrn/5263491/) (2025-05-30): The study finds that mutual funds often cross-trade in corporate bonds, which is beneficial during stressful times, but new regulations have reduced the associated cost savings.
- [Covered Interest Parity Drivers](https://www.ml-quant.com/papers/ssrn/5271538/) (2025-05-30): The paper identifies foreign investors' supply forces and domestic agents' demand forces as the main drivers of deviations in covered interest parity, based on micro-level transaction data.
- [Equity Funds in China](https://www.ml-quant.com/papers/ssrn/5265363/) (2025-05-30): The study reveals that personal and professional traits of Chinese fund managers, such as gender, experience, and education, partially affect their performance and ability to generate excess returns.
- [Local Preference in Mutual Fund Portfolios in India During Covid-19 – A Study](https://www.ml-quant.com/papers/ssrn/5269304/) (2025-05-30): The study finds that during the Covid-19 period, Indian equity mutual funds increased their investments in foreign stocks, particularly US technology stocks, resulting in unusually high net returns.
- [Global Economic Uncertainty & Oil Market](https://www.ml-quant.com/papers/ssrn/5258748/) (2025-05-21): The research shows a predictable two-way relationship between the Global Economic Policy Uncertainty index and global crude oil prices, mainly seen in volatility correlation.
- [RLDAUNCE: Reinforcement Learning for Data](https://www.ml-quant.com/papers/ssrn/5259995/) (2025-05-21): Reinforcement Learning for Data: The article presents RLDAUNCE, a method that improves data assimilation using physical constraints, focusing on uncertainty quantification and computational efficiency.
- [Machine Learning IV Estimators](https://www.ml-quant.com/papers/ssrn/5258814/) (2025-05-21): The paper highlights the challenges of nonparametric instrumental variable estimation, proposing machine learning instrumental variable algorithms for better performance through advanced regularization techniques.
- [RealTime Earthquake Intensity ML](https://www.ml-quant.com/papers/ssrn/5262668/) (2025-05-21): The research suggests a machine learning model for quick earthquake damage assessment using operational data from base station service providers, showing high accuracy and real-time functionality.
- [Liquidity Risk in Bank Failures](https://www.ml-quant.com/papers/ssrn/5260010/) (2025-05-21): The article examines the failures of Silicon Valley Bank and Credit Suisse, advocating for a revision of current liquidity risk metrics to better reflect the pace and size of stress outflows in modern banking.
- [Predicting Work Accidents with ML](https://www.ml-quant.com/papers/ssrn/5259984/) (2025-05-21): The study assesses the effectiveness of dimensionality reduction methods in predicting occupational accidents in retail, finding that Forward Feature Selection combined with the Gradient Boosting Classifier is most effective.
- [CBDC AND FINANCIAL STABILITY IN DUAL-CURRENCY SAVINGS ECONOMIES](https://www.ml-quant.com/papers/ssrn/5260008/) (2025-05-21): The summary introduces a framework for evaluating the financial stability implications of Central Bank Digital Currencies in dual-currency savings economies, using a robust scenario-driven methodology.
- [Arbitrage in Perpetual Contracts](https://www.ml-quant.com/papers/ssrn/5262857/) (2025-05-21): The study finds that price differences in cryptocurrency markets are influenced not only by transaction fees but also by the funding swap mechanism's clamping function.
- [Challenges of Data Encryption in Emerging Economies](https://www.ml-quant.com/papers/ssrn/5262460/) (2025-05-21): The paper explores the difficulties and potential solutions for implementing strong data encryption technologies in developing economies for secure and efficient big data marketing.
- [Undetected Accounting Fraud: Implications for Theory and Machine Learning Predictive Models](https://www.ml-quant.com/papers/ssrn/5259405/) (2025-05-21): The study uses machine learning to identify non-fraud instances in financial fraud research, improving inference and addressing undetected frauds.

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