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.
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.
Bayesian approach confirms no return predictability for 1926-2004 data, weak evidence for 1953-2021.
problem Investigating return predictability using Bayesian methods.
method Developed a new shrinkage type prior for a model parameter in a VAR system, compared to other estimation methods.
result Bayesian approach outperforms reduced-bias estimator in terms of size and power.
ESN models predict intraday stock returns efficiently.
problem Intraday stock return prediction using machine learning.
method Echo State Network (ESN) models with random parameters.
result ESN models achieve strong forecasting performance efficiently.
Predicts asset return distributions using LSTM and quantile regression.
problem Predicting complex asset return distributions.
method Two-stage approach: quantile prediction using asset-specific features, market data adjustment.
result Significantly outperforms existing models (98% improvement over baseline).
Study finds no consistent return predictability using payout ratios across 16 countries.
problem Return predictability using payout ratios in various markets.
method Analysis of 16 developed countries' bond, equity, and housing markets using payout-price ratios.
result No consistent in-sample and out-of-sample performance with positive utility gain.
This paper proposes an embedding-based neural network for more accurate investment return prediction.
problem Accurately predicting investment returns requires understanding industry knowledge and news, as well as leveraging relevant theories.
method The approach uses embedding to encode investment IDs into low-dimensional vectors, leveraging dual branches to separate different information, and employs the swish activation function.
result The proposed embedding-based dual branch model outperforms traditional machine learning models like Xgboost, Lightgbm, and Catboost on the Ubiquant Market Prediction dataset.
Intangible investment becomes a strong predictor of stock returns over time.
problem Understanding the role of intangible investment in stock returns over different periods.
method Comparing intangible investment's predictive power over two distinct periods (1963-1992 and 1993-2022) using orthogonal factors.
result Intangible investment's predictive power for stock returns has significantly increased over time, becoming a main predictor for recent periods.
In this paper we derive the exact solution of the multi-period portfolio choice problem for an exponential utility function under return predictability. It is assumed that the asset returns depend on predictable variables and that the joint random process of the asset returns and the predictable variables follow a vect…
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.
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.
MPC outperforms reactive budgeting in non-stationary return environments.
problem Optimizing budget allocation under non-stationary returns.
method Receding-horizon Model Predictive Control (MPC) compared to reactive policies.
result MPC consistently outperforms reactive budgeting when return dynamics are predictable.
China's stock market is the largest emerging market all over the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependence. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic varia…
An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations a…
In this paper, we address one of the main puzzles in finance observed in the stock market by proponents of behavioral finance: the stock predictability puzzle. We offer a statistical model within the context of rational finance which can be used without relying on behavioral finance assumptions to model the predictabil…
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.
Paper clusters microstructure measures for better stock return prediction.
problem Finding the best microstructure measures for predicting stock returns.
method Clustering model of market microstructure features studied in 10-second time-frequency.
result Identifies the most effective microstructure measures for accurate stock return prediction.
This study investigates how Decision-Focused Learning improves stock return predictions for better portfolio optimization.
problem The challenge of precise expected returns estimation in mean-variance optimization.
method Investigates Decision-Focused Learning (DFL) to adjust stock return prediction models for MVO.
result DFL tilts prediction errors by the inverse covariance matrix, leading to systematic prediction biases in portfolio optimization.
Improved stock return prediction model handles noise and non-stationarity.
problem Predicting stock returns with robustness to noise and non-stationarity.
method Extended AROW algorithm to handle synchronous mini-batch updates and applied it to stock return prediction.
result The new model outperforms classical approaches in backtesting on S\&P500 stocks.
CPPS selects portfolios using conformal prediction for better returns.
problem Optimizing portfolio returns with predictive models and uncertainty.
method Conformal prediction framework for portfolio selection.
result CPPS outperforms simpler strategies in delivering superior returns.
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.
Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
Price and return predictions are limited by economic complexity, not just volatility.
problem Limited accuracy of price and return probability forecasts by Gaussian distributions.
method Analyzes economic reasons behind limitations in predicting price and return statistical moments.
result Predictions of price and return probabilities by Gaussian distributions are inaccurate due to economic complexity.
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.
Quarter-hour market bursts predict algorithmic trading and returns in crypto futures.
problem Predicting returns in cryptocurrency futures markets using quarter-hour market bursts.
method Analysis of trade data and Autocorrelation Map to identify and quantify algorithmic trading activity.
result Quarter-hour market bursts are associated with algorithmic trading and can predict returns.
In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Suc…
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…
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…
This paper explores how combining quantitative factors and news from LLMs improves stock return prediction.
problem Improving stock return prediction using quantitative factors and news.
method Introduces a fusion learning framework to learn unified representations from factors and LLM-generated newsflow, comparing combination, summation, and attentive methods. Explores mixture models and decoupled training approaches.
result Effective multimodal modeling of factors and news improves stock return prediction and selection.
Time-varying neural network improves stock return prediction.
problem Predicting stock returns in a time-varying market.
method Online early stopping algorithm for neural network training.
result The proposed algorithm outperforms current methods in predicting monthly U.S. stock returns.
Transformer pre-training improves stock return prediction accuracy.
problem Improving stock price prediction accuracy for better investment decisions.
method Pre-trained transformer models on TSX index, fine-tuned for individual stocks, compared to LSTM and XGBoost.
result Transformer model achieved lower mean squared error than benchmarks.
Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the …
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.
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.
New framework predicts earnings announcements using press release content, surpassing earnings surprises.
problem Predicting stock returns based on earnings press releases.
method Compared traditional and BERT-based embeddings of press releases, finding content as informative as earnings surprises.
result FinBERT yields highest predictive power for earnings announcement returns.
LSTM model predicts stock returns with over 90% accuracy.
problem Predicting future stock market prices and returns is challenging.
method Used Long Short-Term Memory (LSTM) model trained on historical NSE data.
result LSTM model achieved over 90% accuracy in predicting stock prices and returns.
A linear link between S&P 500 return and the change rate of the number of nine-year-olds in the USA has been found. The return is represented by a sum of monthly returns during previous twelve months. The change rate of the specific age population is represented by moving averages. The period between January 1990 and D…
Study finds option volume imbalance predicts equity market returns.
problem Predicting equity market returns using option volume imbalance.
method Nonlinear analysis of option volumes decomposed into five market participant classes.
result Strong signals of predictability of excess market returns from Market-Maker volumes.
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
Study improves stock return uncertainty prediction using Gaussian mixture distributions.
problem Improving prediction of stock market return uncertainty.
method Gaussian mixture distribution-based deep learning model.
result Superior performance in volatility estimation, especially during market volatility.
Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culpr…
StockGPT predicts stock returns using AI, outperforming traditional strategies.
problem Making accurate stock predictions and trading decisions.
method Trains an autoregressive model on historical stock returns, using attention mechanisms to learn patterns.
result StockGPT's portfolios outperform traditional strategies, yielding significant alphas.
The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Networ…
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
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.
The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in …
GPR ensemble method predicts stock returns efficiently.
problem Predicting stock returns using machine learning.
method Ensemble Gaussian Process Regression (GPR) for online learning.
result Method outperforms existing models in R-squared and Sharpe ratio. We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer so…