PolyBench benchmarks LLMs on real market data, revealing significant performance gaps.
problem Benchmarking LLMs for real-world event prediction from live market signals.
method Multimodal benchmark derived from Polymarket, evaluating 7 LLMs under identical market states.
result Only two models achieve positive financial returns, highlighting the gap between fluency and probabilistic reasoning.
FAWMF adapts weights for implicit feedback recommendation efficiently.
problem Challenges in treating unobserved data as negative in implicit feedback recommendation.
method FAWMF uses a variational auto-encoder with a parameterized neural network to adaptively assign personalized data confidence weights, and fBGD for efficient learning.
result FAWMF and fBGD outperform existing methods in real-world datasets.
Specialists outperform generalists in ensemble classification.
problem Determining the accuracy of an ensemble of classifiers when individual classifier accuracies are known.
method Proved upper and lower bounds on ensemble accuracy, constructed specialist and generalist classifiers.
result Upper and lower bounds on ensemble accuracy, practical implications for classifier construction.
Meta-learned confidence improves few-shot learning accuracy.
problem Improving accuracy in few-shot learning with unreliable model confidence.
method Meta-learning confidence weights for query samples to improve transductive inference performance.
result Meta-learned confidence leads to new state-of-the-art results on benchmark datasets.
Aggregates diverse zero-shot LLM outputs for better corporate disclosure classification.
problem Combining varied zero-shot LLM predictions for improved stock return prediction.
method Multi-prompt framework with three fixed zero-shot LLM classifiers, logistic meta-classifier aggregation.
result Aggregated model outperforms single classifiers and baseline models, increasing balanced accuracy from 0.566 to 0.606.
The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated values. For instance, in times of severe market stress, one would expect with certain…
Knowledge Distillation (KD) consists of transferring âknowledgeâ from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the studentâs…
CITE algorithm provides anytime-valid certification of model outputs.
problem Challenges in controlling error levels in LLM self-consistency.
method Certification by Intersection-union Testing with E-processes (CITE) algorithm.
result Provable control of false certification at any prescribed level under arbitrary stopping rules.
Study improves model robustness in noisy datasets.
problem Instance-specific label noise in robust classification tasks.
method Coordinated Sparse Recovery (CSR) method introduces a collaboration matrix and confidence weights to reduce generalization error.
result CSR and CSR+ significantly reduce generalization error compared to existing methods.
PolySwarm uses a swarm of LLMs to predict and arbitrage prediction markets.
problem Real-time prediction market trading and latency arbitrage inefficiencies.
method PolySwarm employs a swarm of 50 diverse LLMs, Bayesian combination, and risk-controlled execution.
result Swarm aggregation outperforms single-model baselines in prediction tasks.
New metrics CWSA and CWSA+ improve model evaluation under confidence thresholds.
problem Lack of metrics capturing model reliability under confidence thresholds.
method Introducing CWSA and CWSA+ metrics that reward confident accuracy and penalize overconfident mistakes.
result CWSA and CWSA+ outperform classical metrics in trust-sensitive tests.
A new algorithm reduces regret in bandit problems with adversarial corruptions.
problem Optimizing decision-making in bandit problems with variable uncertainties and adversarial interference.
method Proposes HCW-GLB-OMD, an OMD-based estimator with Hessian-based confidence weights for robustness.
result Achieves instance-wise minimax optimality with a κ-factor in the corruption term. The CAPM's market returns are endogenously determined, affecting all assets' expected returns.
problem The standard CAPM's market return assumption is not endogenously consistent.
method Demonstrates the impact of endogenously determined market returns on asset returns and the range of feasible market returns.
result Expected returns are influenced by all assets' risks, and market returns are limited by asset distribution.
Stock markets show unusual overnight and intraday returns.
problem Unusual patterns of overnight and intraday returns in stock markets.
method Analyzed features of the returns to deduce the cause.
result The only plausible explanation for these returns is that they are due to market manipulation.
We investigate the two components of the total daily return (close-to-close), the overnight return (close-to-open) and the daytime return (open-to-close), as well as the corresponding volatilities of the 2215 NYSE stocks from 1988 to 2007. The tail distribution of the volatility, the long-term memory in the sequence, a…
The study finds significant power-law cross correlations in Bitcoin's return-volatility dynamics.
problem Investigating asymmetry in Bitcoin's return-volatility relationships.
method Analysis of daily and high-frequency Bitcoin data to identify cross correlations.
result Power-law cross correlations between returns and future volatilities are observed, indicating long-range dependencies.
We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return volatility are not correlated, while intraday volatility is. We …
Diversification return is an incremental return earned by a rebalanced portfolio of assets. The diversification return of a rebalanced portfolio is often incorrectly ascribed to a reduction in variance. We argue that the underlying source of the diversification return is the rebalancing, which forces the investor to se…
The paper links labor income risk to stock returns using industry portfolio returns.
problem Understanding the impact of sectoral shifts on stock returns.
method Using cross-industry dispersion (CID) as a proxy for unemployment risk, the paper examines the relationship between stock returns and the sensitivity of returns to CID innovations.
result Stocks with high sensitivity to CID have lower expected returns, suggesting they are more exposed to sectoral shifts and unemployment risk.
The paper uses PCA and HMM to forecast stock returns outperforming buy-and-hold.
problem Predicting stock returns accurately.
method Applied PCA to covariance matrix of S&P 500 stocks, used HMM on principal components, and forecasted stock returns.
result The model outperforms buy-and-hold strategy in terms of annualized Sharpe ratio.
The paper explores how market-based returns depend on past trade values.
problem Improving accuracy in forecasting market-based average and volatility of returns.
method Derives the dependence of market-based volatility and higher statistical moments of returns on statistical moments and correlations of current and past trade values.
result Market-based statistical moments can be approximated by a finite number of moments, improving forecast reliability.
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
problem Capturing nonlinear predictability in financial return dynamics.
method Decomposes returns into sign and magnitude components, using a joint distribution model.
result Significantly outperforms traditional linear models in forecasting U.S. stock market returns.
We present a simple microstructure model of financial returns that combines (i) the well-known ARFIMA process applied to tick-by-tick returns, (ii) the bid-ask bounce effect, (iii) the fat tail structure of the distribution of returns and (iv) the non-Poissonian statistics of inter-trade intervals. This model allows us…
Regression Trees analyze stock returns, revealing market excess return as the most informative factor.
problem Understanding informational content of three factors in stock returns.
method Joint regression tree analysis of daily stock return data for 5 major US corporations.
result The market excess return factor is always the most informative in all cases (solo and joint).
New algorithm optimizes adaptive return level for Markowitz portfolios.
problem Finding an optimal return level for Markowitz portfolios when investor's risk appetite is unknown.
method Krasnoselskii-Mann Proximity Algorithm based on proximity operator and momentum technique.
result Significant improvements over state-of-the-art methods in portfolio optimization.
The paper examines the Chinese market reaction to the ADR issue by comparing returns and their stochastic variances of the Chinese firms cross-listed in the U.S. stock market. First, It was implemented capital asset pricing model (CAPM) to determine expected returns A and N shares. The CAPM provided with a methodology …
Model uses LLM features to predict stock returns effectively.
problem Predicting stock returns from text data.
method Structured Event Representation (SER) model with attention mechanisms.
result SER-based model outperforms existing models in stock return prediction.
LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
problem Behavioral biases in LLMs' stock return forecasts.
method Comparison of LLM forecasts with crowd-sourced estimates and historical data.
result LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
A new perspective on portfolio selection using realized returns.
problem Choosing between two investments with the same expected return.
method Modeling realized returns as random variables and applying the CAPM formula.
result The CAPM formula applies to realized returns, not just their expectations.
Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived from minority game for modeling stock correlations, in which an agent's expected return for one stock…
Study estimates Medallion's compounded return before fees at 31.8%.
problem Incorrectly using yearly returns for compounding leads to overestimation of fund performance.
method Used fund sizes and trading profits to estimate compounded return; used manager's wealth as proxy for Simons.
result Annualized compounded return of Medallion before fees is likely under 35%
The vector of periodic, compound returns of a typical investment portfolio is almost never a convex combination of the return vectors of the securities in the portfolio. As a result the ex post version of Harry Markowitz's "standard mean-variance portfolio selection model" does not apply to compound return data. We pro…
With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…
With the rapid growth in fashion e-commerce and customer-friendly product return policies, the cost to handle returned products has become a significant challenge. E-tailers incur huge losses in terms of reverse logistics costs, liquidation cost due to damaged returns or fraudulent behavior. Accurate prediction of prod…
In reinforcement learning, Return, which is the weighted accumulated future rewards, and Value, which is the expected return, serve as the objective that guides the learning of the policy. In classic RL, return is defined as the exponentially discounted sum of future rewards. One key insight is that there could be many…
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
problem The long-term performance decay of leveraged ETFs due to volatility drag.
method Unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies.
result Return dynamics, including return autocorrelation, volatility clustering, and regime persistence, determine LETF performance.
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
The paper models stock returns using q-Gaussians and negative binomials.
problem Modeling stock return distributions and pricing options.
method Proposes a generalized jump-diffusion model and uses q-Gaussians and negative binomial distributions. result An explicit option pricing formula is derived.
We investigate scaling and memory effects in return intervals between price volatilities above a certain threshold q for the Japanese stock market using daily and intraday data sets. We find that the distribution of return intervals can be approximated by a scaling function that depends only on the ratio between the …
We show that the moments of the distribution of historic stock returns are in excellent agreement with the Heston model and not with the multiplicative model, which predicts power-law tails of volatility and stock returns. We also show that the mean realized variance of returns is a linear function of the number of day…
Study shows gaps in Bitcoin order book are linked to returns but only in the short term.
problem Understanding the relationship between gaps and returns in Bitcoin order books.
method Examined the dynamics of gaps and returns in a Bitcoin order book without considering long-term causation.
result The causal relationship between gaps and returns is limited to instantaneous causation.
Trend following in cryptocurrencies yields high returns, similar to commodities.
problem Investing in cryptocurrencies using trend following strategies.
method A decade of data analysis on cryptocurrency markets and trend following strategies.
result Cryptocurrencies offer strong returns and diversification against traditional equities.
Study finds TVL doesn't predict cryptocurrency returns.
problem Assumption of TVL predicting returns in crypto markets.
method Examined TVL-sorted portfolios against crypto market returns, using various TVL measures.
result TVL-sorted portfolios' returns are linear functions of crypto market returns, replicable with standard tools.
The study finds a liquidity premium in stock returns, but only after correcting for microstructure noise.
problem The positive association between expected idiosyncratic volatility and expected stock returns.
method Developed a novel method to eliminate microstructure influences from stock returns and estimate idiosyncratic volatility.
result The liquidity premium in value-weighted portfolios is driven by liquidity in the prior month after correcting for microstructure noise.
Bounds on long-term returns of leveraged ETFs are given.
problem Uncertainty in long-term returns of leveraged ETFs.
method Quadratic bounds on log-returns based on daily log-returns of the underlying index.
result Sufficient conditions for outperformance and underperformance of leveraged ETFs.
Bitcoin returns exhibit a distinct inverse cubic law scaling behavior.
problem Investigating the scaling properties of Bitcoin price returns.
method Analysis of recent Bitcoin data to determine tail index and autocorrelation of absolute returns.
result The tail index of Bitcoin returns changes to approximately 3, consistent with the inverse cubic law.
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
problem Optimal option portfolios under Sharpe Ratio maximization
method Formulation for explicit portfolio weights
result Different optimal portfolios for Sharpe Ratio and return-to-Value-at-Risk (VaR) ratio
This study examines how ChiNext IPOs' initial returns are influenced by regulation regime changes.
problem Investors' behavior and pricing of ChiNext IPOs under different regulation regimes.
method Analysis of three time periods with two different regulation regimes and three sets of listing day trading restrictions.
result Regulation regime changes significantly impact ChiNext IPO pricing and overreaction.