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.
Study news networks to predict stock returns.
problem Predicting cross-sectional stock returns using news networks.
method Constructed time-varying directed networks of S&P500 stocks from 1 million news articles, identified stock tickers using an algorithm, and tested for comovement and reversal effects.
result News network attention proxy, network degree, predicts monthly stock returns robustly.
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper im…
Estimates mean and covariance for large, unbalanced stock returns panels.
problem Estimating mean and covariance in large, unbalanced panel data.
method Nonparametric, kernel-based joint estimator for conditional mean and covariance matrices.
result The idiosyncratic risk explains more than 75% of cross-sectional variance.
We build a simple diagnostic criterion for approximate factor structure in large cross-sectional equity datasets. Given a model for asset returns with observable factors, the criterion checks whether the error terms are weakly cross-sectionally correlated or share at least one unobservable common factor. It only requir…
PRISM-VQ combines financial priors with vector quantization for better stock prediction.
problem Predicting cross-sectional stock returns is hard due to low signal-to-noise ratios and changing market conditions.
method Integrates expert priors, vector-quantized latent factors, and dynamic factor loadings.
result Consistent improvements in cross-sectional return prediction and portfolio performance.
Study shows different types of volatility and skewness changes affect stock prices.
problem Different types of volatility and skewness changes affect stock prices.
method Used intraday data for individual stocks to analyze cross-section of asset returns.
result Idiosyncratic transitory and persistent shocks to volatility and skewness are priced differently in stock returns.
Motivated by the literature on investment flows and optimal trading, we examine intraday predictability in the cross-section of stock returns. We find a striking pattern of return continuation at half-hour intervals that are exact multiples of a trading day, and this effect lasts for at least 40 trading days. Volume, o…
A new model explains asset returns with a single factor, improving cross-sectional performance.
problem Understanding the cross-section of asset returns with complex models.
method Proposes a non-linear single-factor asset pricing model with a nonparametric link function estimated jointly with sieve-based estimators.
result The model delivers superior cross-sectional performance with a low-dimensional approximation of the link function.
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.
Study finds high cyber risk stocks generate significant excess returns.
problem Understanding and quantifying cyber risk's impact on stock returns.
method Machine learning algorithm measuring cyber risk proximity to a corpus.
result High cyber risk stocks generate an excess return of 18.72% p.a.
Peer-reviewed research and mined data predict stock returns similarly.
problem Predicting stock returns using research quality.
method Cross-sectional analysis of 29,000 accounting ratios with t-statistics > 2.0.
result Post-sample performance is largely independent of whether the predictor is peer-reviewed or mined.
We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In "cryptoassets" we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market dat…
The study finds that supply chain information from LLM embeddings improves stock returns predictions.
problem Predicting stock returns using textual information from annual reports.
method Combining LLM embeddings of annual reports with supply chain knowledge graph propagation.
result Network-augmented embeddings significantly predict stock returns with a Sharpe ratio of 0.86 and alpha of 7.27%.
The isotropic correlation model explains equity returns better than linear factor models.
problem Understanding the covariance structure of equity returns.
method Developed an isotropic covariance model for equity returns, analyzed empirical data, and compared results to linear factor models.
result The isotropic covariance model provides a better fit to recent equity return data compared to linear factor models.
Study compares cryptocurrency and stock markets using statistical equilibrium models.
problem Comparing the stochastic structure of cryptocurrency and stock markets.
method Applied QRSE model to analyze daily returns of cryptocurrencies and S&P 500 companies.
result Revealed differences in informational efficiency between cryptocurrency and stock markets.
Publication bias skews asset pricing research findings.
problem Bias in sharing and publishing research findings.
method Meta-studies and empirical Bayes corrections.
result Publication bias effects are minimal and not dominant.
The study uses equity order flow to forecast stock returns and resolves the liquidity premium puzzle.
problem The liquidity premium and its relation to investment horizons.
method Directly estimated Kyle's price-impact coefficient λ from daily equity order flow data.
result Signed order flow predicts stock returns, with volume volatility predicting lower returns.
This paper proposes an empirical test of financial contagion in European equity markets during the tumultuous period of 2008-2011. Our analysis shows that traditional GARCH and Gaussian stochastic-volatility models are unable to explain two key stylized features of global markets during presumptive contagion periods: s…
Machine learning portfolios perform well with simple imputation of missing data.
problem Handling missing values in machine learning portfolios constructed from cross-sectional return predictors.
method Simple imputation with cross-sectional means compared to rigorous expectation-maximization methods.
result Simple imputation performs well due to the structure of missing data.
MarketGAN generates financial returns using GANs to match empirical stylized facts.
problem Generating financial returns under data scarcity and preserving stylized facts.
method Generative adversarial learning with a TCN backbone.
result MarketGAN outperforms conventional methods in portfolio applications.
We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We fin…
A new model decomposes equity returns and volatilities into memory components.
problem Understanding long-term equity dynamics and volatility patterns.
method Proposes a multivariate generalization of the variance ratio to decompose long-horizon equity dynamics.
result Identifies a five-factor model capturing persistent, antipersistent, and multi-scale memory in returns and volatility.
Simple bounds show most cross-sectional predictability findings are likely true.
problem Determining the validity of cross-sectional return predictability findings.
method Developed simple and intuitive bounds on the false discovery rate (FDR).
result Bounds show the FDR is small, indicating most findings are likely true.
In this paper, we study the determinants of expected returns on the listed penny stocks from two perspectives. Traditionally financial economics literature has been devoted to study the macro and micro determinants of expected returns on stocks (Subrahmanyam, 2010). Very few research has been carried out on penny stock…
Cross-sectional "Information Coefficient" (IC) is a widely and deeply accepted measure in portfolio management. The paper gives an insight into IC in view of high-dimensional directional statistics: IC is a linear operator on the components of a centralizing-unitizing standardized random vector of next-period cross-sec…
Topological anomaly scores predict return curves in S&P 500 stocks
problem Detecting anomalies in financial time series
method BallMapper, decoder-conditional VAE, Function-on-Function regression
result Anomaly history carries predictive content for return curves
We study properties of the cross-sectional distribution of returns. A significant anti-correlation between dispersion and cross-sectional kurtosis is found such that dispersion is high but kurtosis is low in panic times, and the opposite in normal times. The co-movement of stock returns also increases in panic times. W…
ChatGPT snapshots predict future stock returns.
problem Predicting future stock returns using pre-cutoff text.
method Extracted LLM outlook scores from OpenAI snapshots.
result Outlook scores positively correlate with future stock returns.
We discuss a simple, exactly solvable model of stochastic stock dynamics that incorporates regime switching between healthy and distressed regimes. Using this model, which is analytically tractable, we discuss a way of extracting expected returns for stocks from realized CDS spreads, essentially, the CDS market sentime…
The paper finds stocks with higher dynamic network risk have lower returns.
problem Understanding and pricing short-term and long-term dynamic network risk in stock returns.
method Examined the relationship between stock sensitivities to dynamic network risk and expected returns, using economic theory and empirical analysis.
result A one-standard deviation increase in long-term network risk loadings associates with a 7.66% drop in annualized expected returns.
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.
We discuss the finding that cross-sectional characteristic based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts in an analysis of equity returns of stocks listed on the JSE. Under the assumption of general no-arbitrage conditions, we arg…
Paper introduces EEMs for pricing contingent claim returns.
problem Computing expected future prices of contingent claims.
method Dynamic change of measure approach to construct EEMs.
result EEMs provide physical and pricing expectations of contingent claim prices.
News novelty predicts negative stock market returns.
problem Negative stock market returns due to increased news novelty.
method Quantified news novelty using entropy measure from recurrent neural network applied to a large news corpus.
result Entropy exposure carries a negative risk premium, indicating that assets positively correlated with entropy hedge aggregate news risk.
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a …
Sparse APCA identifies sparse factors in financial returns over time.
problem Analyzing co-movements of high-dimensional panel data over time.
method Sparse asymptotic PCA with truncated power method for sparse factors and sequential deflation for multi-factor cases.
result Identification of nine risk factors influencing the S&P 500 stock market.
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.
New method controls false discoveries in financial asset pricing.
problem Controlling false discoveries in time series with unknown correlations.
method Double bootstrapping method to control false discovery rate.
result Superior statistical power and controlled false discovery rate.
Develops a continuous compliance index for Islamic equity screening.
problem Binary rulebooks lead to inconsistent compliance assessment of firms.
method Integrates six leading financial and business activity standards into a single continuous index.
result Firms with the same pass/fail label can differ significantly in compliance strength.
Simple model finds high correlation in retail crypto returns.
problem Discerning correlation in retail cryptocurrency markets without factors.
method Used N*(N) statistic to compare models of daily returns.
result High average pairwise correlation (60%) found, supports isotropic model.
This study revisits Fama-French models using sample innovations to address misinterpretation of high R-squared values.
problem Misinterpretation of high R-squared values in Fama-French models due to serial dependence and volatility clustering.
method Use of sample innovations to derive standard econometrics time series models to overcome misinterpretation.
result Suggests the Fama-French model should consider heavy-tail distributions due to relevant tail behavior in financial data.
We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitc…
Study finds 'happiness' search data predicts stock returns, suggesting utility needs impact firm performance.
problem Investing in firms that meet societal utility needs.
method Used Google Trends data on 'happiness' search volume to predict stock returns.
result Happiness search exposure (HSE) explains future stock returns, particularly for big and value firms.
Study uses CSIE to estimate portfolio volatility relative to market.
problem Estimating relative volatility risk of stock portfolios.
method Cross-sectional intrinsic entropy (CSIE) model to estimate cross-sectional volatility.
result Discover sets of symbols that outperform market indices in terms of return with similar or lower risk.
Develops a method to predict stock returns with time-varying risk premia.
problem Predicting stock returns with time-varying risk premia while maintaining no-arbitrage restrictions.
method Penalized two-pass regression with time-varying factor loadings, incorporating penalization in the first pass and grouping in the second pass.
result The proposed method reduces prediction errors compared to other approaches.
Study finds stocks with common firm fears earn lower returns.
problem Identifying and quantifying firm-level investor fears.
method Analysis of equity options to identify common firm-level fears and their impact on stock returns.
result Stocks with exposure to common bad fears earn lower returns and require higher compensation.