Introduces GA-P/E, a growth-adjusted stock valuation measure.
problem Evaluating stock value and predicting future returns.
method Computes a payback period adjusted for earnings growth, using a sorted portfolio methodology.
result Low GA-P/E stocks outperform high GA-P/E stocks in absolute and risk-adjusted returns.
The paper examines how share buybacks impact a company's earnings per share.
problem The trade-off between reducing share count and decreasing net earnings due to share buybacks.
method Review of accretive share repurchases, analysis of EPS increase as a function of price paid, and quantification of earnings growth difference.
result Share buybacks can enhance EPS, but the net effect on earnings growth is mixed.
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…
Study shows long-term debt impacts financial growth of non-financial firms listed at Nairobi Securities Exchange.
problem Declining financial performance and reluctance to lend to non-financial firms listed at Nairobi Securities Exchange.
method Descriptive and panel data analysis of 45 non-financial firms over 10 years.
result Long-term debt positively and significantly influences financial growth measured by earnings per share and market capitalization.
Study assesses short-term debt's impact on non-financial firms' financial growth.
problem Declining financial performance and reluctance to lend to non-financial firms listed at Nairobi Securities Exchange.
method Explanatory research design, descriptive statistics, and panel data analysis.
result Short-term debt positively and significantly influences financial growth.
A modular cash-overlay rule for allocating between a fixed growth-defensive risky sleeve and interest-bearing cash.
problem Drawdown control
method Continuous cash-overlay filters
result Earnings an 18.83% CAGR versus 16.62% for 100% R
This study assesses how share capital affects financial growth of non-financial firms listed at NSE.
problem Non-financial firms listed at NSE struggle with financial growth due to declining performance and lack of investor interest.
method Descriptive and panel data analysis of 45 non-financial firms over 10 years.
result Share capital positively and significantly influences financial growth, explaining 32.73% and 11.62% of variations in earnings per share and market capitalization growth, respectively.
Study evaluates valuation models for UK companies using case studies.
problem Determining how accounting numbers affect business value.
method Comprehensive review of three valuation models: FCFVM, REVM, AEGM.
result Accounting numbers through valuation models can affect business value.
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.
Study estimates 163 million online freelancers globally.
problem Estimating the number of online workers globally.
method Combining data from various online labour platforms.
result Headline estimate of 163 million registered profiles.
Network models assume unrealistic idiosyncratic risk, which can be mitigated by allowing for correlated shocks.
problem Network models assume idiosyncratic risk, which can be unrealistic and lead to incorrect predictions.
method Proposed a production-based asset pricing model to account for substitutability between trade partners and correlation in supply and demand shocks.
result Assets positively exposed to average propagation of upstream and downstream shocks earn lower average risk premia.
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.
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.
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.
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.
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%.
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.
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.
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.
Specialists tolerate defects to gain flexibility, which can be removed when needed.
problem The economic benefits and limitations of deliberately tolerating defects in decision-making.
method Analyzes the conditions under which defects can be kept and removed, using economic models and structural analysis.
result A defect is profitably removable if certain conditions are met, and the premium is the support function of the class's ROC set.
Investors target specific regions of payoff distributions for portfolio optimization.
problem Optimizing portfolio performance across different return distribution regions.
method Developed a dynamic portfolio-choice framework targeting downside or upside quantiles.
result Policies focused on downside regions provide stronger left-tail protection and higher Sharpe ratios.
Specialists tolerate defects to gain flexibility, which can be removed when needed.
problem The economic benefits and limits of deliberately tolerating defects in decision-making.
method Analyzes the economic position of keeping and removing defects, using a coupling lemma and structural economic models.
result A defect is profitably removable if the detector-relevant distinction survives a restriction and the advantage condition holds.
Firm foundation theory estimates a security's firm fundamental value based on four determinants: expected growth rate, expected dividend payout, the market interest rate and the degree of risk. In contrast, other views of decision-making in the stock market, using alternatives such as human psychology and behavior, bou…
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.
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.
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.
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.
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.
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.
We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends…
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.
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…
Develops a theory linking managers' disclosures to market pricing.
problem Linking managers' earnings guidance to market pricing.
method Mathematical theory of managerial disclosure in asset pricing.
result Foundational approach for understanding disclosure impacts.
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 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.
LLMs outperform human analysts in predicting earnings direction.
problem Evaluating financial statements without narrative or industry-specific information.
method Trained GPT4 on standardized, anonymous financial statements and instructed to predict earnings direction.
result LLMs predict earnings directionally with accuracy comparable to narrowly trained ML models.
Study analyzes AI's impact on firms, markets, and workers using large language model data.
problem Understanding AI's effect on firms, markets, and workers.
method Used 380 trillion tokens from 400+ large language models to analyze AI's impact.
result Firms with higher AI exposure earn higher returns, creating an AI premium.
New financial price model using earning yield derived from CIR process.
problem Excess volatility and equity premium puzzles in financial markets.
method Proposes a new financial price process based on earning yield and Cox-Ingersoll-Ross (CIR) process.
result Derives analytically stylized facts of financial prices and returns, including power law distribution of returns and fat-tailed distribution of prices.
Paper introduces a new project control method using Monte Carlo and statistical learning.
problem Project control under uncertainty.
method Integrates Earned Value Methodology with Monte Carlo simulation and statistical learning.
result Estimates probabilities of project success and duration.
Stochastic discount factor (SDF) processes in dynamic economies admit a permanent-transitory decomposition in which the permanent component characterizes pricing over long investment horizons. This paper introduces an empirical framework to analyze the permanent-transitory decomposition of SDF processes. Specifically, …