Can Wikipedia Article Traffic Statistics be Used to Verify a Technical Indicator? An Exploration into the Correlation Between Wikipedia Article Traffic Statistics and the Coppock Technical Indicator.

Recent studies have shown that, through the quantification of Wikipedia Usage Patterns as a result of information gathering, stock market moves can be predicted (Moat et al 2013). There was also research performed to determine the predictive nature of Wikipedia Data to predict movie box office success (Mestyan et al. 2013). The goal of any investor, in order to maximize the return of their investments, is to have an edge over other participants in the markets. Several tools and techniques have been used over the years to fulfil this, some proving to generate a consistent stream of income (Gillen 2012). With the improvement of technology and communication links, what was once considered a closed door, gentleman’s club operation, can now be tapped into by anybody who has access to a PC and communications link. It is said that approximately only 20% of investors are consistently successful in their investments (Terzo 2013). In order be successful, there needs to be a strategy in place that is strictly adhered to. The objective of these trading systems is to minimize, or ideally cut out, the human emotion factor and naturally, as a consequence, allow the strategy operate at its optimum. An example of this is through the use of technical analysis indicator which, when used correctly, can net the investor considerable, consistent returns. (Gillen 2012). Technical indicators, such as Coppock, are widely used in the field of stock market investment to provide traders and investors with an insight into which direction a stock or index is moving so as to facilitate the optimum time to enter or exit the market. This project investigates whether Wiki Article Traffic Statistics can be used to verify trading signals given by the Coppock technical indicator through the use of a suitable correlation technique

Predicting Stock Market Using Online Communities Raw Web Traffic Streams

This paper investigates the predictive power of online communities traffic in regard to stock prices. Using the largest dataset to date, spanning 8 years and almost the complete set of SP500 stocks, we analyze the predictive power of raw unstructured traffic by filtering stock daily returns with traffic features. Our results partially challenge the assumption that raw traffic simply trails stock prices, as expected from a noisy signal without the sentiment direction. Raw traffic is shown to predict prices with statistical significance but with small economic impact. Anyway, this impact rises to moderate under the following conditions: 3 to 7 days lag and stable traffic level. Moreover, the quality of the predictions significantly increases when a high level of traffic is coupled to low market volatility, while a high level of traffic in period of high volatility usually denotes late reactions to violent market movements and a consequent poor predictive power. The findings set interesting future works in the definition of novel indicators for market analysis based on web traffic analysis, to be coupled with complementary tools such as sentiment analysis