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.
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.
Volatility forecasting and return prediction in high-frequency Chinese equity markets.
problem Improving statistical forecasting performance and economic strategy outcomes in equity markets.
method Developing a sequential two-stage framework combining realized volatility modeling and XGBoost return prediction.
result Regime-aware volatility forecasting outperforms baseline models.
New measure corrects news bias in NLP stock return forecasting.
problem Improving stock return and volatility forecasting accuracy.
method Hype-Adjusted Probability Measure, sentiment score equation.
result Significantly improved forecast accuracy for U.S. semiconductor tickers.
Forecast stock return distributions using neural networks.
problem Accurately modeling non-Gaussian stock return features.
method Two-stage quantile neural network with spline interpolation.
result Improved mean and variance forecasts compared to standard models.
DBNs improve VaR forecasting compared to traditional models, but SVaR forecasts are conservative.
problem Forecasting VaR and SVaR using dynamic Bayesian networks.
method DBN framework applied to S&P 500 index returns, comparing to autoregressive models and historical simulation.
result DBNs achieve comparable VaR forecasting accuracy to historical simulation models, but SVaR forecasts remain conservative.
Network analysis improves stock return forecasting.
problem Improving stock return forecasting using network properties.
method Network analysis of stock return correlations, using individual and global properties of stocks.
result 50% improvement in R2 score for long-term stock returns forecasting, 3% for short-term.
Motivated by the need for effectively summarising, modelling, and forecasting the distributional characteristics of intra-daily returns, as well as the recent work on forecasting histogram-valued time-series in the area of symbolic data analysis, we develop a time-series model for forecasting quantile-function-valued (…
Study sets a nontrivial upper limit on return forecasting accuracy.
problem Establishing a practical upper limit for return forecasting accuracy.
method Defined a coin-flip oracle model to theoretically outperform practical models and used its RextOOS2 as an upper bound. result Theoretical upper bound on RextOOS2 is a quadratic function of directional accuracy. A3T-GCN model forecasts FTSE100 stock prices using technical indicators and financial ratios.
problem Forecasting closing stock prices of FTSE100 constituents.
method Hybrid A3T-GCN architecture using technical indicators, financial ratios, and sector correlations.
result A3T-GCN model improves prediction accuracy with annualized log-returns and shorter sequence lengths.
Study forecasts stock returns on JSE using SGDLMs capturing cross-series dependencies.
problem Accurate forecasting of multivariate time series data.
method Simultaneous Graphical Dynamic Linear Models (SGDLMs) with customised DLMs and importance sampling/mean-field variational Bayes.
result SGDLMs accurately forecast stock data on JSE and respond to market changes.
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributi…
This study improves tail risk forecasting by integrating overnight information into semi-parametric models.
problem Improving tail risk forecasting in financial markets.
method Proposes RES-CAViaR-oc models combining overnight return and realized volatility, using Bayesian estimation.
result Realized volatility and overnight return significantly improve tail risk forecasting.
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.
New methods improve uncertainty in machine learning predictions for asset returns.
problem Uncertainty in machine learning predictions for asset returns.
method Developed new methods to construct forecast confidence intervals for expected returns from neural networks.
result Neural network forecasts of expected returns have the same asymptotic distribution as classic nonparametric methods, enabling standard error calculation.
The paper introduces a machine learning method to forecast market direction using efficient frontier coefficients.
problem Improving asset return estimation for portfolio optimization.
method Monthly directional market forecast using an online decision tree trained on efficient frontier coefficients.
result The method outperforms baseline portfolios and other feature sets.
In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approac…
The study forecasts ETF return direction using machine learning models.
problem Predicting the direction of ETF returns for investment decisions.
method Applied regression and classification models to historical ETF component data.
result Models outperformed naive and buy & hold strategies, especially linear regression and logistic regression.
Study finds sales forecasters overreact to extreme news.
problem Understanding how forecasters react to sales growth news.
method Proposes a framework with fat-tailed dynamics and linear forecasting rule.
result Forecasters overreact to significant sales growth news.
Study forecasts cryptocurrency returns using LOB data and Hawkes model.
problem Predicting cryptocurrency returns due to their chaotic nature.
method Hawkes model applied to LOB data with COE model.
result Outperforms benchmarks in cryptocurrency return sign forecasting.
The study evaluates forecast risk-adjusted performance using various metrics.
problem Evaluating forecast reliability beyond accuracy.
method Risk-adjusted performance measures (Sharpe, Sortino, Omega ratios) and Edge Ratio.
result Machine learning models often offer attractive risk profiles but not necessarily higher reliability.
Deep neural networks forecast financial return distributions accurately.
problem Forecasting probability distributions of financial returns.
method Used 1D CNN and LSTM architectures with custom loss functions to optimize distribution parameters.
result LSTM with skewed Student's t distribution outperformed classical models in multiple evaluation metrics.
This paper fine-tunes LLMs for stock return prediction using financial news.
problem Improving stock return forecasting accuracy using LLMs.
method Fine-tuning LLMs with text and forecasting modules, comparing encoder-only and decoder-only models, and integrating token-level representations.
result LLMs' aggregated token-level embeddings enhance return predictions for long-only and long-short portfolios.
Improved Markowitz method handles uncertainty in return forecasts.
problem Uncertainty in return statistics forecasts.
method Convex optimization with practical constraints.
result Handles uncertainty gracefully and efficiently.
This paper forecasts cryptocurrency log-returns using LASSO-VAR and sentiment analysis.
problem Forecasting log-returns of cryptocurrencies using social media sentiment.
method LASSO-VAR model combined with Twitter and Reddit sentiment data.
result The model predicts the correct direction of cryptocurrency returns more than 50% of the time.
A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.
problem Forecasting Value-at-Risk (VaR) in financial markets.
method Proposes a parametric forecasting model based on the normal inverse Gaussian distribution (NIG) incorporating intraday information.
result The model outperforms traditional GARCH models, especially in high-risk scenarios.
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.
Paper uses bipartite graph to forecast cross-market returns, revealing asymmetry.
problem Cross-market return predictability and asymmetry between U.S. and Chinese markets.
method Directed bipartite graph capturing time-ordered linkages, hypothesis testing for edge selection, regularized and ensemble machine learning models.
result U.S. returns predict Chinese intraday returns, but not vice versa, revealing asymmetry.
Novel framework uses causality for financial forecasting.
problem Balancing invariance and prediction accuracy in financial time series.
method Causality-inspired models for forecasting asset returns.
result Efficacy in stable and accurate predictions, especially in turbulent markets.
Improved Hawkes model forecasts extreme financial returns more accurately.
problem Forecasting extreme tail events in financial log-returns.
method 2T-POT Hawkes model with multiple exceedance thresholds.
result 2T-POT Hawkes model outperforms GARCH-EVT model in risk forecasting.
This paper uses Gaussian processes to forecast short-term stock price volatility.
problem Inaccurate short-term volatility forecasts for high-frequency trades.
method Combines numerical and probabilistic models, specifically Gaussian Processes (GPs), to correct and forecast stock price data.
result Effective short-term volatility forecasts for high-frequency trades using Gaussian Processes.
Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals---the waiting time between consecutive extremes---we show that these extreme returns are predictable on the short term. Examining a range of different types of returns and th…
Optimizes LightGBM for stock market forecasting with novel feature engineering and transformation methods.
problem Accurately forecasting stock market fluctuations to mitigate risks.
method Feature engineering and transformation methods for LightGBM optimization.
result Log Returns, Returns and EMA Difference Ratio are the most effective target variable transformations.
EXFormer predicts foreign exchange returns with high accuracy using a multi-scale self-attention mechanism and dynamic variable selection.
problem Accurately forecasting daily exchange rate returns in international finance.
method EXFormer uses a multi-scale trend-aware self-attention mechanism with dynamic variable selection and embedded squeeze-and-excitation blocks.
result EXFormer outperforms other models in forecasting daily exchange rate returns, achieving statistically significant improvements in directional accuracy.
The leverage effect refers to the well-established relationship between returns and volatility. When returns fall, volatility increases. We examine the role of the leverage effect with regards to generating density forecasts of equity returns using well-known observation and parameter-driven volatility models. These mo…
Proposes a new model to maximize out-of-sample Sharpe ratios by forecasting tangency portfolios.
problem Maximizing Sharpe ratios when returns and covariances are not stationary.
method Forecast the tangency portfolio using vector autoregressions and invest in the minimum Euclidean distance portfolio.
result Empirically validated superior out-of-sample Sharpe ratios.
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.
Study shows HPO improves stock return forecasting models.
problem Improving accuracy of stock return forecasting models.
method Used deep neural networks with hyperparameter optimization (HPO) and regularization techniques.
result Model with technical indicators and dropout regularization outperformed other models by 0.53% in-sample and 1.11% out-of-sample.
The study uses LSTM and random forests to forecast stock price movements for intraday trading.
problem Forecasting directional movements of stock prices for intraday trading.
method Employed random forests and LSTM networks to analyze S&P 500 constituent stocks.
result Multi-feature setting provided higher daily returns (0.64% using LSTM, 0.54% using random forests) compared to single-feature setting.
Optimizes trading policies using future price forecasts.
problem Static reinforcement learning agents lack mechanisms for using price forecasts at inference time.
method FPILOT framework inspired by Model Predictive Control (MPC). Uses a predictive model to construct an allocation-based imagined return objective at each decision step.
result Consistent improvements in total return and risk-adjusted metrics across various policy learning algorithms.
HANET combines LSTM and attention mechanisms for better financial forecasting.
problem Lack of distinct macroeconomic regimes in financial datasets.
method Hierarchical Cross-Attention mechanism integrating long-run macro contexts with high-frequency market dynamics.
result HANET outperforms neural forecasters, especially during turbulent periods.
Enhances portfolio construction with tailored regime forecasts for individual assets.
problem Traditional portfolio construction methods fail to account for asset-specific market conditions.
method Hybrid framework combining unsupervised and supervised learning for regime identification and forecasting.
result Outperforms traditional portfolio models across various asset classes.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
problem Forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) for financial returns.
method Semiparametric approach using restricted quantile regression to model the conditional scale of financial returns.
result The method provides robust, distribution-free estimates of extreme losses and captures risk dynamics.
This study was conducted to find an appropriate statistical model to forecast the volatilities of PSEi using the model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the R software, the log returns of PSEi is modeled using various ARIMA models and with the presence of heteroskedasticity, the l…
Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political…
Gradient-free ensemble learns sector forecasts from diverse models.
problem Predicting sector returns in a volatile market.
method Dynamic model combination using out-of-sample R-squared.
result Ensemble outperforms individual models in sector rotation.
Improved covariance matrix forecasting for S&P 500 using factor models and shrinkage.
problem Forecasting large covariance matrices of returns in finance.
method Decompose covariance matrix into firm-level factors and sectoral restrictions. Estimate using VHAR models with LASSO.
result Significantly improved forecasting precision compared to benchmarks.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.