Browse Research Library
Uncovering Expected Returns:
Information in Analyst Coverage Proxies
We show that analyst coverage proxies contain information about expected returns.
Our findings highlight the usefulness of analysts’ actions in expected return estimations, and a potential inference problem when coverage proxies are used to study information asymmetry and dissemination.
Cited By: 91
Valuation ratios, surprises, uncertainty, or sentiment:
How does financial machine learning predict returns from earnings announcements?
We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. Leveraging the model’s predictions in a zero-investment trading strategy yields annualized returns of 11.63 percent at a Sharpe ratio of 1.39 after transaction costs.
Cited By: 1
A Stock Decision Support System Based on ELM
People often tend to use a reliable way to predict the stock market in order to get a substantial return on investment.
Results show that our method is much better than the buy-and-hold strategy.
Cited By: 37
Can Twitter Help Predict Firm
Level Earnings and Stock Returns?
Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole.
Our findings highlight the importance of considering the aggregate opinion in individual tweets when assessing stocks future prospects and value.
Cited By: 339
July 18 2017
Directional Prediction of Stock Prices using Breaking News on Twitter
Stock market news and investing tips are popular topics on Twitter. In this paper, first, we utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website for the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content.
Cited By: 18
Breaking News Detection and Tracking in Twitter
Twitter has been used as one of the communication channels for spreading breaking news. Each story is provided with the information of the message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.
Cited By: 369
Quantifying trading behavior in financial markets using Google Trends
Crises in financial markets affect humans worldwide. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves.
Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.
Cited By: 1033
25 April 2013
Deep Learning Stock Volatility with Google Domestic Trends
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors.
Our preliminary investigation shows strong promise for better predicting stock behavior via deep learning and neural network models.
Cited By: 94
The Viability of StockTwits and Google Trends to Predict the Stock Market
Investors are always looking to gain an edge on the rest of the market. Traditional market theory tells us trying to predict future stock market movements is a wasted effort.
In short, an investor cannot reasonably expect to consistently beat the stock market through superior stock selections and market timing.
Cited By: 21
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