Portfolio management is a theory that establishes a set of mathematical tools and concepts with the goal of maximizing the investor wealth. Portfolio theory has evolved and seen many other competing theories. In this thesis, we expose many techniques of portfolio management and trading. We then implement two novel money management techniques that use a Google application named Google trends to forecast the market behaviour. The first method is a technique that builds a stock portfolio based on the volume of searches for a given term browsed in Google search engine. This technique determines the weights of the assets in the portfolio based on a power law formula using Google Trends outputs for company related search terms. The second method is a technique of trading that builds trading decisions based upon the differential of Google trends output for financial related terms over a given time frame. We then compare the Google Trends portfolio method to the Dow Jones Industrial Average (DJIA) benchmark over metrics like standard deviation, Sharpe ratio and evolution of the return on investment. We show that performance strongly depend on the value of the shape parameter used in the power law formula. We then develop ourselves a new financial product derived from the second technique. This model differs from its original form by the way the asset is managed. Instead of executing pure hedge technique, we decide to hold on some assets of the portfolio in order to take advantage of the state of economy suggested by the output of Google trends. In turn, we sell part of the assets when the horizon is cloudy. Such a technique is beating both the Dow Jones Industrial Average and the original technique in terms of return on investment. It also proves better than returns of the Google Trends Portfolio based on Sharpe ratio maximization.