eSignal Learning presents:
Presented by ,
WHEN: @ (your local time)
Event Details:Calen will introduce PredictWallStreet, a new app available with eSignal v12.1. PredictWallStreet combines the wisdom of the crowd with the power of its proprietary algorithms to produce forecasts that beat the S&P 500 by an average of 6.1% annually.
This webinar will begin with a light overview of the science behind PredictWallStreet, and concludes with a live demo of the tool to demonstrate how our data makes trading more profitable.
- Bullets: Measuring investor sentiment in real-time: Our community votes on the future direction of individual stocks and indexes, and receives market-beating data that helps make better trades.
- Showing accuracy of predictions: show users the vote tallies for each stock, along with the accuracy of the community predictions, which gives you even more information to help make profitable trading decisions.
- Going beyond the raw data: we spent years developing sophisticated algorithms that combine real-time raw data with our proprietary historical database for predictions that consistently beat the market.
- Comparing stock, industry, and sector: Our community sentiment meter uses our proprietary processed data to show how the prediction for an individual stock compares to the predictions for the rest of its industry and sector
- Charting to identify trends: Our advanced algorithms process real-time and historical data to produce our market-beating predictive sentiment data, which we plot alongside stock prices, making it easy for you to spot short- and long-term trends at a glance.
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About the Presenter
Calen oversees business development, engineering and information technology initiatives within the company and frequently serves as technical liaison. He started working with PredictWallStreet in 2002, developing a beta web site and the first graphical prediction interface. Outside PredictWallStreet, he founded a successful web consulting company and spun off a specialized online educational service. He earned his Bachelor of Science degree in Computer Science with honors from the University of California at Santa Cruz.