Using A Stock Trading System Based On Prediction Models Obtained With Daily Stock Exchange System Essay

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The dissertation project addresses the problem of trying to build a stock trading system based on prediction models obtained with daily stock quotes data. We will apply different models to predict the returns of IBM stocks at the New York Stock Exchange. These predictions will be used together with a trading rule that will generate buy and sell signals. This chapter addresses several new data mining issues: (1) how to use R to analyze data stored in a database; (2) how to handle prediction problems where there is a time ordering among training cases (usually known as a time series); (3) and the consequences of wanting to translate model predictions into actions. 3.1 Problem description and objectives Stock market trading is an application domain with a big potential for data mining. In effect, the existence of an enormous amount of historical data suggests that data mining can provide a competitive advantage over human inspection of this data. On the other hand there are authors claiming that the markets adapt so rapidly in terms of price adjustments that there is no space to obtain profits in a consistent way. This is usually known as the efficient markets hypothesis. This theory has been successively replaced by more relaxed versions that leave some space for trading opportunities. The general goal of stock trading is to maintain a portfolio of stocks based on buy and sell orders. The long term objective is to achieve as much profit as possible from these trading

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