653,802 research outputs found
Predicting financial markets with Google Trends and not so random keywords
We check the claims that data from Google Trends contain enough data to
predict future financial index returns. We first discuss the many subtle (and
less subtle) biases that may affect the backtest of a trading strategy,
particularly when based on such data. Expectedly, the choice of keywords is
crucial: by using an industry-grade backtesting system, we verify that random
finance-related keywords do not to contain more exploitable predictive
information than random keywords related to illnesses, classic cars and arcade
games. We however show that other keywords applied on suitable assets yield
robustly profitable strategies, thereby confirming the intuition of Preis et
al. (2013)Comment: 8 pages, 4 figures. First names and last names swappe
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Identification and application of physical and chemical parameters to predict indicator bacterial concentration in a small Californian creek.
This study of Aliso Creek in California aimed to identify physical and chemical parameters that could be measured instantly to be used in a model to serve as surrogates for indicator bacterial concentrations during dry season flow. In this study, a new data smoothing technique and ranking/categorizing analysis was used to reduce variation to allow better delineation of the relationships between adopted variables and concentrations of indicator bacteria. The ranking/categorizing approach clarified overall trends between physico-chemical data and the indicators and suggested sources of the bacteria. This study also applied a principle component regression model to the data. Although the model was promising for predicting concentrations of total and fecal coliforms, it was somewhat weaker in predicting enteroccocci
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