SSRN
SSRN
Working papers in finance and economics from SSRN. 2,921 featured so far, newest first.
- Featured
- 2,921
- Tracked on Semantic Scholar
- 713
- Cited 100+
- 1
- Since
- 24 May 2023
- 6 Nov 20264fanfare
The Hidden Sharpe Ratio
The study estimates maximum Sharpe ratios in large cross-sections without constructing the efficient portfolio, finding annualized maximal Sharpe of 7.4 in U.S. equities versus benchmark estimates of 1.0 to 4.0.
- 6 Nov 20264fanfare
The Financial Impact of Non-Financial Beliefs in Large Language Models
Following AI adoption, financial analysts revise high-ESG forecasts upward and become more bullish, but forecast accuracy deteriorates and errors rise for top-ESG firms.
SSRNLLMs & Text
- 6 Nov 20264fanfare
Does It Actually Run? An Execution-Based Reproducibility Study of Published Machine-Learning Finance Research
Testing 459 quantitative-finance machine-learning papers' released code, the study finds that execution success rates fall steeply due to environment and dependency issues, not link rot alone.
SSRNML & AI Methods
- 6 Nov 20264fanfare
How Much Data Does an Information Coefficient Need? The Sampling Distribution of a Cross-Sectional IC, and Why Breadth Does Not Buy Precision
Cross-sectional IC volatility has a floor from factor risk that does not shrink with breadth, capping effective sample size at roughly fifty observations.
- 6 Nov 20263fanfare
The flow, not the headline: point-in-time LLM sentiment and the cross-section of cryptocurrency returns *
Headlines scored by LLM within minutes predict cross-sectional cryptocurrency returns with a daily long-short Sharpe of 2.21 until the signal decays.
SSRNCrypto & DeFi
- 6 Nov 20263fanfare
Monetary Policy Expectations of Sophisticated Investors
Fine-tuned LLM analysis of hedge fund letters reveals policy expectations that predict yield-curve moves beyond market rates and correlate with fund returns.
- 6 Nov 20263fanfare
Monetary Policy, Expected Dividend Growth, and the Equity Risk Premium
Using equity and risk-free rate term structures, monetary policy shocks affect both expected dividend growth and equity risk premium, with effects beyond duration matching.
- 6 Nov 20263fanfare
Risk-Budgeted Mean-Variance Portfolios
A convex optimization method combines risk budgeting with expected returns, delivering higher average returns than risk parity with lower turnover and concentration than traditional mean-variance portfolios.
- 6 Nov 20263fanfare
Time-Series Momentum in the ETF Era: Decay of the Long-Short Premium and the Case for Long-Only, Volatility-Targeted Trend Allocation
The classic TSMOM long-short premium has decayed to a zero net Sharpe, but a long-only volatility-targeted version across multiple horizons earns 0.58 with strong crisis protection.
- 6 Nov 20263fanfare
Who Keeps Your Debt Alive? Creditor Capacity and Corporate Debt Renewal
A 2021 life-insurer capital rule change shows that when incumbent creditors' capacity shrinks, corporate debt issuance falls and firms struggle to refinance maturing debt.
- 6 Nov 20263fanfare
What Is (Not) in ABS-EE Data?
Analysis of 19.8 million securitized auto loans reveals that reported data systematically understate risk through selection bias, carry-over reporting gaps, and priced-at-origination hidden credit differences.
- 6 Nov 20263fanfare
Do AI Models Remember Indian Company Financials? Evidence on Look-Ahead Bias by Firm Size
Large language models recall Indian firm financials from within their training data with accuracy that declines sharply for smaller firms, creating serious look-ahead bias in LLM-based backtests.
SSRNML & AI Methods
- 6 Nov 20263fanfare
Buy the Rumor, Sell the News: When is News Priced in?
Analyzing 1.68 million stock-day events from 4.57 million articles, the study finds that directional price moves concentrate before and at publication day, with little incremental return later.
- 6 Nov 20263fanfare
Investing in Data Capital
Measuring data investment from earnings calls, the research shows data capital's share of investment reached about one-eighth in 2025 and commands a market premium of 12 dollars per dollar invested.
- 6 Nov 20263fanfare
The Hidden Cost of Tokenization: Blockchain Adoption and Native Token Price Discovery
NFT airdrops on Ethereum raise block fees and failure rates, shifting price discovery off-chain and impeding native-token information incorporation critical for proof-of-stake security.
SSRNCrypto & DeFi
- 6 Nov 20263fanfare
Shock Transmission Through Bank-Nonbank Linkages: Evidence from Private Credit
Private credit funds that borrow from banks transmit funding shocks to firm lending; following SVB's collapse, exposed banks increased credit-line commitments while cutting direct lending to nonfinancial firms.
- 6 Nov 20263fanfare
Short Squeezes in the Corporate Bond Market
The study identifies two types of squeezes in corporate bonds that degrade price discovery and returns, with lender squeezes predicting negative short-horizon returns and market squeezes predicting positive ones.
- 6 Nov 20263fanfare
The Corporate Bond ETF Creation Puzzle
Finds that authorized participants create corporate bond ETF shares at discounts to offload overvalued inventory, causing delivered bonds to underperform by 5.3 basis points.
- 6 Nov 20263fanfare
Exchange Rate Disconnect with Disagreeing Arbitrageurs
Models how wealth redistribution among arbitrageurs with different beliefs amplifies exchange rate volatility and generates interest-parity wedges, accounting for 62 percent of observed variance.
- 6 Nov 20263fanfare
The Illusion of Informed Congressional Trading: Evidence from Committee Networks and Hearings
Using machine learning to map firm disclosures to congressional committee jurisdictions, finds no evidence that members earn abnormal returns from legislative access or committee hearings.
- 6 Nov 20263fanfare
Not All Beats Are Equal: Anchoring on the Nominal Earnings Surprise
Shows that stock returns respond more to the same percentage surprise when reported earnings are larger in cents, consistent with anchoring on nominal rather than percentage earnings beats.
- 6 Nov 20263fanfare
Commodity Prices and Sovereign Risk
Research shows trade exposure determines whether commodity-price rises reduce or increase sovereign risk, with exporters gaining from higher prices and importers facing reinforced costs.
- 6 Nov 20263fanfare
Green Tastes, Beliefs, and Portfolios: Evidence from Synthetic Investors
Using language models as synthetic investors reveals institutional portfolios tilt greener despite weaker green tastes, and beliefs about green returns turn pessimistic after 2020.
SSRNML & AI Methods
- 6 Nov 20262fanfare
AP-PEN: A Hard-Constrained, Physics-Enforced Network for Convenience-Yield Inversion from Commodity Futures
A neural network that analytically enforces the Gibson-Schwartz PDE recovers commodity convenience yield from futures and avoided implausible estimates during the April 2020 WTI dislocation.
- 6 Nov 20262fanfare
The Shape of a Public-Data Volatility Surface: Term-Structure Carry, and the Limits of Measuring Skew
Tests term-structure and skew trading signals in equity volatility from free public data, showing term-structure carry lifts Sharpe ratio to 2.94 but skew lacks sufficient historical data.
- 6 Nov 20262fanfare
Is Retail Crypto Trading Better Than Gambling? Memecoins, Perpetual Futures and Prediction Markets Against Casino Odds
Memecoin traders get back 0.754 per unit staked, worse than any casino game, while perpetual futures and prediction markets match casino returns.
SSRNCrypto & DeFi
- 6 Nov 20262fanfare
Forecasting Stablecoin Depegs: Out-of-Sample Evidence from 219 USD Stablecoins
A gradient-boosted classifier trained on 219 USD-pegged stablecoins predicts which coins will lose their peg within one week, achieving 0.59 average precision on test data.
SSRNCrypto & DeFi
- 6 Nov 20262fanfare
Scoring Informed Trading in Prediction Markets: A Likelihood Ratio Framework
A likelihood-ratio framework ranks wallets by their information content in binary prediction markets, separating ranking from multiple testing and validating the method through simulation.
- 6 Nov 20262fanfare
Contract-level Informational Efficiency in Prediction Markets: Measurable Ex Post, Not Predictable Ex Ante
Studying 244 resolved Kalshi markets, the research finds that efficiency is measurable ex post but not predictable ex ante, and naive trading strategies fail against market benchmarks.
- 6 Nov 20262fanfare
Quantitative Tightening in Segmented Interbank Markets
Models how central banks must retain more reserves for rate control when interbank trading cannot redistribute reserves across heterogeneous banks, even if aggregate demand is sufficient.
- 30 Oct 20264fanfare
The AI Monoculture Paradox in Financial Markets
When traders adopt more precise but common AI signals, synchronized order flow can outweigh forecast gains, reducing collective profits despite higher individual precision, a paradox that taxes can help resolve.
SSRNML & AI Methods
- 30 Oct 20263fanfare
Upsilon: A Joint Spot-Volatility-Time Sensitivity for Zero-Days-to-Expiration Index Options
The study defines Upsilon, an effective sensitivity for zero-days-to-expiration index options that captures the predictable, asymmetric joint dynamics of spot, implied volatility and time decay over their six-hour life.
- 30 Oct 20263fanfare
When Collateral Prices Count: Recognition Clocks and Liquidation Batches
Research on decentralized lending shows that collateral revaluation rules predictably determine when and how many positions are liquidated together, with large price moves triggering batch liquidations far exceeding single-position boundaries.
- 30 Oct 20263fanfare
Generative AI and the Speed of Price Formation Around Earnings Announcements: International Evidence
The study shows that greater ChatGPT exposure in countries accelerates how quickly stock prices incorporate earnings-announcement information, with effects strongest where investors are sophisticated and data is transparent.
SSRNLLMs & Text
- 30 Oct 20263fanfare
Macroeconomic Risk and the Equity Premium
The research identifies trend-growth risk as the largest component of the equity risk premium through persistent shocks in a structural VAR, with implications depending on whether yield changes come from monetary or fiscal sources.
- 30 Oct 20263fanfare
The State-Dependent Pricing of Panic Risk: Evidence from China's Stock Market
A real-time panic index combining price stress, investor holdings and drawdown pain reveals that exposure to panic innovations carries a negative return premium, larger during high-panic periods and persistent for weeks.
- 30 Oct 20263fanfare
Do ETFs Tame Crypto Crashes? Evidence from Eight Staggered Spot ETF Launches
Analysis of eight staggered spot cryptoasset ETF launches finds that late-2025 altcoin ETFs reduced daily volatility by about eighteen percent, though crash propensity remained unchanged.
SSRNCrypto & DeFi
- 30 Oct 20263fanfare
Reserves or Collateral? Identifying the Source of Repo Market Pressure with Treasury Cash Flows
Treasury debt settlements, not reserve scarcity alone, explain recent repo-market pressure, with 100 billion in settlements raising the SOFR-IORB spread by 4.3 basis points.
- 30 Oct 20263fanfare
Systemic Risk Without a Share Price: A Balance-Sheet Capital Shortfall for European Banks
Stressed expected loss measured from balance-sheet data captures systemic risk for unlisted banks, covering 38% of European bank assets that market-based SRISK cannot reach.
- 30 Oct 20263fanfare
Does CoVaR understate contagion? Conformal forecasts for crypto treasury companies
Conformal prediction corrects CoVaR understatement of crypto-treasury contagion by two- to fourfold, reducing simulated distress-day loss frequency from 30% to 7%.
SSRNCrypto & DeFi
- 30 Oct 20263fanfare
The Value of Private Meetings with Central Bankers
Private bilateral meetings between ECB officials and financial institutions correlate with higher trading profits, suggesting selective communication conveys economically valuable central-bank information.
- 30 Oct 20263fanfare
Who is Calling? Information Gathering versus Lobbying in Analyst Forecasts
Mobile-call data between brokerages and firms reveals that brokerage-initiated calls improve forecast accuracy, while firm-initiated calls reduce it, suggesting information gathering versus lobbying.
- 30 Oct 20263fanfare
Opening the Black Box: Which News Explain LLM-Based Stock Ratings?
Research using FinBERT embeddings finds that firm-specific and fundamental news topics explain LLM stock ratings far better than sentiment alone, doubling the model's explanatory power.
SSRNLLMs & Text
- 30 Oct 20263fanfare
Traders on GPT: AI and Cognitive Capital in Financial Markets
A model shows that cheaper AI substitutes for trader expertise and erodes cognitive skills, yet price informativeness remains stable until unexpected AI outages expose fragility in human expertise.
SSRNML & AI Methods
- 30 Oct 20263fanfare
What do Earnings Prediction Markets Know?
Polymarket earnings contracts incrementally improve forecasts by incorporating public information missed by analysts, though only in the final trading day before announcements.
- 30 Oct 20263fanfare
Common Ownership Around the World
Study of 49 economies finds common ownership roughly doubled or tripled since 2005, concentrated in large firms and increasingly driven by the Big Three asset managers globally.
- 30 Oct 20263fanfare
Stagflation and Asset Management
Classifying 107 years of data into real-time inflation and growth states, the study finds sixty-forty portfolios earn negative real returns in both overheating and stagflation conditions.
- 30 Oct 20262fanfare
How Long is Five Minutes?
Validating against event-contract settlement data, the five-minute midquote captures only 68.7% of true price impact and significantly misprices thin markets, challenging the convention.
- 30 Oct 20262fanfare
Daily mean Reversion in Global Equity Indices: Serial Dependence, Tail Risk, and Exploitation
Systematic trading strategies exploiting significant negative autocorrelation across six major equity indices achieve a pooled Sharpe ratio of 1.640 with regime filtering and downside-risk controls over 2002-2026.
- 30 Oct 20262fanfare
A Correlated Random-level Volatility Model: Exact One-dimensional Pricing with Skew
A six-parameter volatility model with one Gaussian factor prices European options exactly and captures implied-volatility skew better than SSVI or Heston, though short-maturity slopes remain too steep.
- 30 Oct 20262fanfare
Shallow versus Deep: The Anomaly in the Limit Order Book
Shallow-book imbalance shocks predict higher future volatility and positive returns, while deep-book shocks predict lower future volatility and negative returns, a pattern robust across Chinese stock constituents since 2014.
- 30 Oct 20262fanfare
Rebuilding an Equity Volatility Surface from Public Data: Reliability and the Variance Risk Premium
A pipeline reconstructs equity volatility surfaces from free options data and assesses reliability; a short delta-hedged straddle harvests the variance risk premium, profitable in pre-registered tests but with realistic frictions limiting the edge.
- 30 Oct 20262fanfare
AFNO: Universal Certifiably-Arbitrage-Free Operator Learning Framework for European Call-Price Surfaces
Proposes a neural-operator architecture that certifiably outputs arbitrage-free European call-price surfaces, addressing failure of standard neural operators to enforce no-arbitrage constraints.
- 30 Oct 20262fanfare
Taming the VAR
A VAR that groups 25 predictors by persistence into three composite state variables recovers stable estimates of discount-rate and cash-flow news without specification dependence.
- 30 Oct 20262fanfare
Picked Off in One Second: Adverse Selection and Out-of-Sample Defences for an NSE Index Options Market Maker
Analysis of live market-making on Indian options shows losses to adverse selection within one second, but regime-detection signals that pull quotes in stress cut losses by forty-six percent for NIFTY in early tests.
- 30 Oct 20262fanfare
Replicating USMV and SPLV with a Public-data Factor Risk ModelSpecification Ambiguity Dominates the Choice of Covariance Estimator
Rebuilding low-volatility indices from public data shows that a rank-based method replicates closely but an optimization-based method fails due to index-specification ambiguity, not covariance estimation.
- 30 Oct 20262fanfare
Investment Mandates and the Value of Distribution Timing in Currency Portfolios Choice
Adding skewness and kurtosis to mean-variance allocations in currency markets produces economically meaningful gains that depend critically on the investment mandate and ability to adjust gross exposure.
- 30 Oct 20262fanfare
Multi-factor Investing in Borsa Istanbul
A five-characteristic long-only strategy for Turkish stocks earns 4.4% annual alpha against the cap-weighted index, but equal-weighted scaling erodes the advantage, suggesting gains come from breadth and risk management rather than characteristics.
- 30 Oct 20262fanfare
Leakage-Safe Multi-View Learning for Financial Transaction Fraud Detection under Temporal Shift
A multi-view learning framework for fraud detection integrates transaction, behavioral, temporal and graph features while avoiding look-ahead bias, with value depending strongly on the dataset.
SSRNML & AI Methods
- 30 Oct 20262fanfare
Official Inflation Information and Household Expectations in High-Inflation Türkiye
Türkiye survey respondents shown the official 30.6-32.6% inflation rate lower their expectations by 3.2 percentage points, though the effect is selective by age and political affiliation.
- 23 Oct 20264fanfare
When Backtests Lie: A Governance-Grade Validation Framework for Machine-Learning Value-at-Risk Models under Regime Shifts
The study shows that machine-learning VaR models passing standard backtests breach risk limits two to nine times worse in stressed regimes, revealing false comfort in current regulatory validation.
- 23 Oct 20264fanfare
The Options-Implied Information Content for Cross-Asset Return and Volatility Prediction: Evidence from 3.8 Billion Option Contracts
Using 3.8 billion option contracts, the study finds implied kurtosis predicts five-day returns with 2.33 Sharpe ratio and improves volatility forecasting by 23.3 percent.
- 23 Oct 20264fanfare
When Price Charts Rhyme: Historical Visual Retrieval for Stock Return Prediction
Vision models retrieve visually similar price charts from three decades of history; similar historical patterns predict future cross-sectional returns, especially for stocks with limits to arbitrage.
SSRNML & AI Methods
- 23 Oct 20264fanfare
Artificial Intelligence and Firm Financing
As firms adopt AI, they hold less cash, shift from revolving to term credit, pay higher spreads, and rely more on non-bank lenders while expanding investment and employment.
SSRNML & AI Methods
- 23 Oct 20264fanfare
The Loan Renegotiation Channel of Quantitative Easing
Large purchases of mortgage-backed securities prompt exposed banks to renegotiate existing corporate loans on lender-favorable terms, reducing borrower investment by 6 percent.
- 23 Oct 20263fanfare
The Price of Systemic Liquidity Risk: An Intertemporal Theory of Financial Fragility and Cross-Sectional Returns
An intertemporal model shows that bank maturity transformation creates priced systemic risk where high liquidity-beta assets underperform by six to seventeen percent quarterly during stress.
- 23 Oct 20263fanfare
Running Hot: Industrial Emissions and the Equity Premium
When U.S. industry runs hot measured by carbon emissions, expected market returns fall 4.2 percentage points the next year, forecasting returns up to five years ahead.
- 23 Oct 20263fanfare
Monetary Policy Uncertainty and Bank Credit Allocation
Higher monetary policy uncertainty reduces banks' risk-bearing capacity, widening the lending gap between well-capitalized and undercapitalized banks by 1.65 percent in riskier credit.
- 23 Oct 20263fanfare
Asset Pricing with Cumulants
Log expected excess returns decompose exactly into joint cumulants of returns and the stochastic discount factor, explaining thirty-one anomaly factors with three structural parameters.
- 23 Oct 20263fanfare
A Model of Price Impact and Fire Sales in Crowded Trades
A model of factor crowding shows that forced exits amplify shocks up to sixfold and shift crowding costs from average returns into tail losses.
- 23 Oct 20263fanfare
Prediction Markets as Arrow–Debreu Securities: Evidence from Polymarket: State Prices, No-Arbitrage Bounds and Macroeconomic News in Blockchain-Based Contingent Claims
State prices from binary prediction markets on the Fed decision and presidential election satisfy the law of one price for complete contingent claims, with overround declining steadily toward resolution.
- 23 Oct 20263fanfare
The Breakdown of Price Discovery at the Critical Threshold of Passive Investment: An Agent-based Limit Order Book Formulation
An agent-based model shows how rising passive market share starves the limit order book of price-sensitive capital, leading to deteriorating price discovery and emergent liquidity crises.
- 23 Oct 20263fanfare
Nonbank Lending to Distressed Firms
As firms approach bankruptcy, lending shifts to nonbanks and fintechs despite higher losses given default, revealing worse information and more pro-cyclical credit supply during downturns.
- 23 Oct 20263fanfare
Headwind or tailwind? How banks interpret borrowers’ AI exposure
After ChatGPT, banks lower default risk for AI-exposed borrowers by about 5 percent but compress credit to market leaders, reordering risk in ways that predict future credit losses.
- 23 Oct 20263fanfare
Homogeneity, Systemic Risk, and the Regulation of Automated Trading
A model shows how correlated automated trading decisions generate feedback and forced liquidations, with systemic risk rising as market depth shrinks relative to automated activity.
- 23 Oct 20263fanfare
Sealed Before the Open: A Verifiable Standard for Measuring AI Forecasts of Financial Markets
A transparency standard with cryptographic timestamping allows independent verification of AI forecasts before market opens, making backtests tamper-proof and comparable across models.
- 23 Oct 20263fanfare
Explainable Machine Learning for Basel and IFRS 9 Credit Risk: A Placebo-Controlled Test of Narrative Features
A placebo test reveals that features like central bank tone can raise credit model accuracy merely by indicating when loans were made, not by predicting defaults.
SSRNML & AI Methods
- 23 Oct 20263fanfare
Where Machine Learning Helps: Risk Timing, Trend Filtering, and an Untouched 2025-26 Holdout
Five pre-registered hypotheses about machine learning for volatility, drawdowns, trend filtering, stock ranking, and news failed validation on a 2025-26 holdout despite passing development tests.
SSRNML & AI Methods
- 23 Oct 20263fanfare
Learning Before Scheduled Events: Prediction Markets, State Prices, and Option Valuation
Prediction-market prices serve as risk-adjusted state prices to measure event probability and information arrival, improving option valuation and revealing market frictions in elections and Brexit.
- 23 Oct 20263fanfare
VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation: Reasoning-Augmented Tool Orchestration, In-Loop Numerical Computation, and Multi-Format Reporting
A platform couples a language model with deterministic execution to turn natural-language requests into auditable reports where every figure is from live data or reproducible code, not model hallucination.
SSRNML & AI Methods
- 23 Oct 20263fanfare
Candlestick Words Improve Intraday Volatility Forecasts Beyond HAR and GARCH
Encoding intraday bars as descriptive words adds 1.7 to 3.7 points of out-of-sample R² to standard volatility models after spikes but shows no edge on daily bars.
- 23 Oct 20263fanfare
What Is a Corporate Bond Spread Made of? Decomposition, Pricing and a Real-Time Backtest in Brazil’s Debenture Market
Decomposing debenture spreads into issuer, market, and contract components shows issuer risk explains 54 percent of variance, enabling out-of-sample pricing within 14 basis points.
- 23 Oct 20263fanfare
Stock Market Cycles
A new methodology identifies bear and bull markets by bandwidth calibration, finding median durations of 74 and 294 days, and fits regime-switching models on cumulative rather than daily returns.
- 23 Oct 20263fanfare
When the Efficient Price Is Observable: A Model-Free Decomposition of Price Impact
Using observable outcomes in prediction markets to identify efficient prices, the study decomposes impact into price-moving and anticipation components, finding informed traders earn mostly through prediction.
- 23 Oct 20262fanfare
Factor Timing with Transaction Costs
A strategy dynamically allocating across 50 smart-beta factors based on past returns and covariances significantly outperforms static allocation even with transaction costs included.
- 23 Oct 20262fanfare
Crowded Factors and Volatility Timing: When Risk Scaling Works
Crowding combined with volatility and market stress improves the risk-adjusted performance of volatility-managed factors more than standard volatility management alone.
- 23 Oct 20262fanfare
From PDE Residuals to Hedging Risk: A Posteriori Diagnostics for Neural Option Pricing with Trading Costs and Volatility Misspecification
The research separates pricing error from hedging loss by decomposing delta-hedge profit and loss into terminal mismatch, residuals, volatility misspecification, rebalancing, and trading costs.
- 23 Oct 20262fanfare
How Much of the Edge Is Luck? Bootstrap Overfitting Diagnostics and Portfolio Synthesis Across a Decade of FX Anomalies The Deflated Alpha Series
Resampling bootstrap of ten walk-forward validated FX strategies places their median expectancy at the 77th percentile of its own distribution, indicating modest optimization bias.
- 23 Oct 20262fanfare
Intraday Volatility Transmission Across Asset Classes: A Connectedness Analysis of Equities, Foreign Exchange, Commodities, and Cryptocurrencies
Analysis of ten instruments finds U.S. equity futures are net transmitters of volatility shocks while cryptocurrencies became net receivers in 2024-2026, with total connectedness peaking in 2025.
- 23 Oct 20262fanfare
Comparative Performance of Archimedean and Elliptical Copulas in Tail Risk Estimation: Evaluating VaR and Expected Shortfall During Market Crashes
Asymmetric Clayton and Gumbel copulas and fat-tailed Student-t copulas substantially outperform Gaussian copulas at capturing lower-tail dependence and crisis losses.
- 16 Oct 20264fanfare
From Algorithms to Agents: A Survey of LLM-Based Trading Systems
A survey of 47 LLM trading systems across six markets finds that reported margins shrink or vanish when rerun, with best evidence placing language models upstream of execution.
SSRNLLMs & Text
- 16 Oct 20264fanfare
Misallocated, Not Mispriced: The Resolution Limit of Local Strength in the Factor Zoo
Rejected factors earn 1.30 Sharpe in their home market versus 0.85 overall, yielding 3.8% six-factor alpha per year through venue reallocation constrained by signal-to-noise limits.
- 16 Oct 20264fanfare
Pre-registering a Market Risk Backtest: A Frozen Gaussian Model for a Rates and Currency Portfolio, 2015–25
A frozen Gaussian VaR model, pre-registered and anchored in Bitcoin, passed coverage tests but showed clustering in 2020, 2022 and 2023, while exponentially weighted variance improved.
- 16 Oct 20264fanfare
An Empty Region of Technical Strategy Space: 18,630 Pre-Registered Variants Across Cryptocurrency and FX
A blockchain-verified sweep of 18,630 cryptocurrency and FX trading variants found no strategies exceeding a Deflated Sharpe of 0.95, with median detectable Sharpe of 3.49 and collective underperformance versus random selection.
- 16 Oct 20264fanfare
An Itô-type Formula: High Frequency Estimation under Rough Volatility
A new calculus framework for rough volatility below the semimartingale regime enables bias-corrected estimation of leverage effects and roughness with central limit theorems, rejecting semimartingale assumptions for recent S&P data.
- 16 Oct 20264fanfare
Infinite Factor Structure in Conditional Asset Pricing
The research reveals that pooling returns across market states creates an infinite-factor structure unconditionally, meaning infinitely many priced components and no finite principal-component basis for expected returns.
- 16 Oct 20263fanfare
Machine Learning in Asset Pricing
A survey reviews machine learning methods for predicting stock returns in time series and cross-section, covering LASSO regularization, deep learning, genetic programming, and LLM applications.
SSRNML & AI Methods
- 16 Oct 20263fanfare
A Tale of Two Types of Tail Risk and Market Returns
Using partial least squares on 18 tail-risk proxies, the study constructs an index that strongly predicts the equity premium both in- and out-of-sample.
- 16 Oct 20263fanfare
New Forecasting Methods for an Old Problem: Predicting 147 Years of Systemic Financial Crises
Random forests and gradient boosting forecast financial crises across 147 years and 17 countries with 77 percent accuracy, using real-sector predictors as the dominant source.
- 16 Oct 20263fanfare
Multi-Horizon Stock Return Prediction via Transformer-Based Deep Learning
Transformer-based models capture pricing signals via self-attention that boost out-of-sample Sharpe ratios across one-, three-, six-, and twelve-month stock return forecasts, outperforming standard machine learning approaches.
SSRNML & AI Methods