Extracts credit-relevant information from earnings calls.
problem Investors do not fully internalize credit-relevant information from earnings calls.
method Develops a novel technique to extract credit-relevant information from earnings call text.
result The extracted information forecasts future credit spread changes and firm profitability.
HSR reduces analyst earnings forecast errors by lowering travel friction.
problem How HSR connectivity affects analyst earnings forecast errors in China.
method Firm-year panel data from 2008-2019; placebo test to rule out pre-existing trends.
result HSR reduces analyst earnings forecast errors after connectivity, not before.
Machine learning improves earnings forecasting accuracy and speed.
problem Improving earnings forecasting accuracy and speed.
method Adopting machine learning models for earnings forecasting.
result Machine learning model outperforms traditional models in accuracy and speed.
Study shows GPT's earnings forecasts are human-like but not always accurate.
problem Information friction in AI-generated financial analysis.
method Examined GPT's earnings forecasts following corporate earnings releases and proposed a diagnostic framework.
result GPT's narrative attention is consistent and human-like but not always associated with higher forecast accuracy.
Study uses neural networks to predict firm earnings, outperforming benchmarks and analysts.
problem Limited coverage and biased estimates by financial analysts.
method Developed a neural network model using 40 years of financial data.
result Model outperforms benchmarks and analysts' forecasts for fiscal-year-end earnings predictions.
Study improves keyword forecasting in earnings-call prediction markets.
problem Accurately predicting future keyword mentions in earnings calls.
method Experiments on earnings-call mention markets, varying context and market probability, introducing MCP.
result Mixture of market probability and MCP yields the best forecasts.
SAGA predicts multi-year earnings with adaptive intervals, improving forecast accuracy.
problem Forecasting long-range nonlinear structure in lifetime earnings.
method Decoder-only transformer for irregular tabular sequences, split conformal calibration.
result Significant improvement in forecast accuracy compared to existing methods.
Deep learning models improve earnings prediction accuracy.
problem Reliable prediction of future earnings per share (EPS).
method Comparison of LSTM and TCN models on financial data.
result Both LSTM and TCN models outperform naive models and analysts.
Paper uses machine learning for nowcasting corporate earnings from mixed-frequency data.
problem Predicting corporate earnings for a large cross-section of firms with different frequency data.
method Structured machine learning regressions with sparse-group LASSO regularization for panel data.
result Machine learning models outperform traditional methods in nowcasting corporate earnings.
We study the statistics of earning forecasts of US, EU, UK and JP stocks during the period 1987-2004. We confirm, on this large data set, that financial analysts are on average over-optimistic and show a pronounced herding behavior. These effects are time dependent, and were particularly strong in the early nineties an…
Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cos…
EDINET-Bench evaluates LLMs on complex financial tasks using Japanese financial statements.
problem Challenges in evaluating LLMs on financial tasks due to specialized expertise and scarce benchmarks.
method Developed EDINET-Bench, an open-source Japanese financial benchmark for LLMs on tasks like fraud detection and earnings forecasting.
result State-of-the-art LLMs perform only marginally better than logistic regression in financial tasks, highlighting the need for more realistic benchmarks.
Bayesian consensus improves accuracy of forecasts from miscalibrated sources.
problem Aggregating predictions from miscalibrated and noisy sources.
method Bayesian approach to adjust for bias and noise, using hierarchical models.
result Bayesian consensus estimator is unbiased and more efficient than alternatives.
We analyze the relation between earning forecast accuracy and expected profitability of financial analysts. Modeling forecast errors with a multivariate Gaussian distribution, a complete characterization of the payoff of each analyst is provided. In particular, closed-form expressions for the probability density functi…
We detect lookahead bias in LLM forecasts using a novel statistical method.
problem Detecting lookahead bias in LLM-generated economic forecasts.
method Developed a statistical procedure using date-only recall queries and estimated Lookahead Propensity (LAP).
result LLM forecasts are contaminated with lookahead bias, as indicated by a positive interaction between LAP and the forecast in accuracy regressions.
This paper improves bidding price prediction for ancillary services markets, boosting revenues.
problem Volatility in renewable energy sources affects grid stability and revenue optimization.
method Machine learning models (SVR, DT, k-NN) and offset adjustment for pay-as-bid markets.
result The proposed approach increases potential revenues by 27.43% to 37.31% compared to baseline models.
LLMs improve stock price forecasting from financial news and reports.
problem Predicting stock prices with high accuracy and robustness.
method Analyzing financial news, reports, and transcripts using LLMs.
result LLMs can improve stock price forecasting but face practical challenges.
Media tone around earnings announcements predicts stock returns.
problem Determining if media tone around earnings announcements provides useful information for stock prices.
method Conducted an event study on media tone around earnings announcements for nonfinancial S&P 500 firms.
result Media tone around earnings announcements predicts abnormal stock returns.
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.
CET model uses contrastive learning to improve earnings data predictions.
problem Inaccurate stock predictions due to earnings data's irregular release and fast obsolescence.
method Contrastive Predictive Coding (CPC) for self-supervised learning of earnings data.
result CET model outperforms benchmarks in predicting stock price trends over time.
Model earnings call transcripts for better stock price prediction.
problem Predicting future stock price movements using earnings call transcripts.
method Deep learning framework with an attention mechanism to encode text data into vectors for predicting stock price movements.
result The proposed model outperforms traditional machine learning methods in stock price prediction.
Study earnings calls to predict stock price movements, finding them more predictive than traditional data.
problem Improving investment decisions by analyzing earnings calls for stock price predictions.
method Graph Neural Network based approach to process and analyze earnings call transcripts.
result Earnings call transcripts are more predictive of stock price movements than traditional hard data.
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.
Investor emotions predict earnings announcements, but excitement lowers returns.
problem The impact of investor emotions on earnings announcements and their returns.
method Social media data analysis over a decade to test the relationship between investor emotions and earnings announcements.
result Excitement about earnings announcements is associated with lower announcement returns.
Paper uses machine learning to analyze stock market anomalies, predicting drift direction and portfolio performance.
problem Capturing dynamics of Post-Earnings-Announcement Drift (PEAD) using machine learning.
method Uses Extreme Gradient Boosting (XGBoost) with genetic algorithm optimization to analyze PEAD dynamics.
result Demonstrates how PEAD dynamics are influenced by different factors across sectors and quarters.
Study shows integrating acoustic features in financial forecasting models can degrade performance.
problem Predicting stock market volatility from corporate earnings calls using speech features.
method Empirical investigation of acoustic feature extraction in teleconference environments using a two-stream late-fusion architecture.
result Integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%.
Improved earnings predictions through text-morphed earnings calls.
problem Improving earnings prediction models using narrative information.
method Introducing a text-morphing methodology to generate counterfactual transcripts.
result Analysts over-react to sentiment and under-react to risk and uncertainty.
FinBERT model identifies key speakers in earnings calls, boosting stock returns.
problem Unequal impact of all speakers in earnings call transcripts on stock returns.
method Utilized FinBERT, a domain-specific transformer model, to parse transcripts and weight speakers' sentiment.
result FinBERT section-weighted sentiment generates significant long-short alpha of 2.03%.
RiskLabs uses LLMs to predict financial risks from multimodal data.
problem Financial risk prediction using AI techniques.
method Integrates multimodal financial data (textual, vocal, time series, news) into LLMs for prediction.
result Empirical results show effectiveness in forecasting market volatility and variance.
This paper aims to explore the mechanical effect of a company's share repurchase on earnings per share (EPS). In particular, while a share repurchase scheme will reduce the overall number of shares, suggesting that the EPS may increase, clearly the expenditure will reduce the net earnings of a company, introducing a tr…
Research shows eco-innovation boosts earnings management, especially in constrained firms.
problem The impact of eco-innovation on earnings management in firms with financial constraints.
method Multi-method approach including entropy balancing, PSM, and Heckman Test correction.
result Eco-innovation positively correlates with earnings management, especially in firms facing financial constraints.
The study improves stock market valuation using volatility and earnings data.
problem Improving stock market valuation metrics.
method Time series model for asset returns, multivariate kernel density estimation, linear regression.
result The valuation measure is an improvement over Shiller's P/E ratio.
This study examines how earnings announcements affect option volatility and pricing.
problem The impact of earnings announcements on option volatility and pricing.
method Analysis of extremely short-term options data to study bimodality and concavity in IV curves.
result Investors pay a premium to hedge against extreme volatility during earnings announcements in the presence of concave IV smiles.
ECC Analyzer uses LLMs to predict stock volatility from ECCs.
problem Leveraging unstructured ECC data for stock volatility prediction.
method Uses large language models to extract and fuse textual and audio features from ECCs.
result ECC Analyzer outperforms traditional benchmarks in volatility prediction.
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
problem Balancing public benefits and private insurance for loss of earning capacity in Denmark.
method Innovative approaches from researchers and practitioners, leveraging actuarial expertise.
result Development of equitable, data-driven solutions to mitigate risk and enhance societal well-being.
We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the stock market. We treat earnings growth as exogenous. The difference between log wealth and log earnings is modeled as an autoregression of order 1 with linear trend 4.6% and Gaussian inno…
Analyst reports contain valuable information for investment decisions.
problem Investment value in analyst reports is not fully understood or utilized.
method Embedded analyst reports with LLMs and ML forecasts of future returns.
result Portfolios formed on analyst report narratives outperform numerical forecasts and established factors.
SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.
problem Predicting earnings surprises from long, redundant financial documents.
method Sparse Autoencoder feature selection to filter out noise and identify key dimensions.
result SAE-FiRE significantly outperforms baseline approaches in financial datasets.
Advanced AI model predicts stock movements post earnings reports.
problem Inaccurate stock predictions from earnings reports.
method Fine-tuned LLMs with QLoRA compression, integrating financial and market data.
result Significantly improved predictive accuracy compared to benchmarks.
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
problem Predicting currency returns using volatility shocks.
method Constructed a dynamic, directed network of volatility connections using option-implied volatilities.
result Currencies that transmit more volatility shocks earn lower excess returns.
Study analyzes deep learning models for financial sentiment in earnings calls.
problem Leveraging NLP for sentiment analysis in financial transcripts.
method Comparative analysis of BERT, FinBERT, and ULMFiT models.
result Models' strengths and limitations in financial sentiment analysis.
Firms disclosing positive earnings surprises are more likely to disclose ESG information.
problem Transparency vs. performance in financial markets.
method Empirical analysis of earnings surprises and ESG disclosures.
result Positive earnings firms disclose more ESG information than negative earnings firms.
The study reveals distinct patterns in retail investors' holding periods affecting stock returns.
problem Understanding the impact of retail investors' investment horizons on stock returns.
method Using self-reported holding periods from StockTwits, the study categorizes retail investors into long-horizon and short-horizon groups and analyzes their return patterns.
result Long-horizon retail investors exhibit underreaction to earnings announcements, while short-horizon investors show overreaction.
Optimizes cash management in ATM networks to reduce costs and increase revenue.
problem Minimizing cash costs while ensuring adequate funds in a network of ATMs.
method Developed a discrete optimal control model using forecasting techniques and control theory.
result The proposed model outperforms classical inventory management models, earning 30% more revenue.
Improved stock selection through predictive fundamentals and uncertainty estimates.
problem Selecting stocks based on future financial data to outperform traditional factor models.
method Train deep nets to forecast future fundamentals, incorporate uncertainty estimates, and adjust portfolios to manage risk.
result Simulated annualized return of 17.7% and Sharpe ratio of 0.84 for uncertainty-aware model, significantly higher than 14.0% and 0.52 for standard factor models.
The purpose of this paper is to introduce a new growth adjusted price-earnings measure (GA-P/E) and assess its efficacy as measure of value and predictor of future stock returns. Taking inspiration from the interpretation of the traditional price-earnings ratio as a period of time, the new measure computes the requisit…
We study an option pricing framework that accounts for the price impact of an earnings announcement (EA), and analyze the behavior of the implied volatility surface prior to the event. On the announcement date, we incorporate a random jump to the stock price to represent the shock due to earnings. We consider different…
Study predicts stock price direction on earnings announcement days using multi-modal deep learning.
problem Predicting stock price movements during earnings announcements is challenging due to market noise and discontinuities.
method Constructed a multi-modal feature space combining fundamental metrics, technical indicators, and sentiment scores from financial news articles. Evaluated LSTM and Transformer models against a baseline.
result Transformer model outperforms LSTM in identifying volatile movements, achieving higher macro F1-score.