Financial Data Mining
1. Introduction
Nowadays, more and more people participate in the stock market. Recent survey reveals that there is a tendency of increasing number of youngsters, especially university students, get involved in the trading activities. We are no exception. Similar to many other investors, we are interested in forecasting the stock prices by using trends, patterns, moving averages observed from historical data.
However, there have been a certain number of people criticizing the use of past data. Among these people, a French mathematician, Louis Bachelier raised a theory called Efficient Market Hypothesis more than a century ago. The theory states that stock prices follow a random walk, which discouraged the study of historical data. This is very controversial and has led to an ever lasting dispute about the reliability of technical analysis. Nonetheless, people’s curiosity about past data has never gone. Being different from the vast majority who use typical technical analysis, we decide to use predictive data mining techniques which we regard as interesting and accurate in our forecasting.
Forecasting is an uncertain process and therefore a high accuracy is demanded. There are many forecasting techniques in the world. In general, they can be classified into three types: casual model, time-series model and smoothing techniques. Undoubtedly, they are of different features and thus are suitable for prediction under certain circumstances. For casual model, the most commonly used technique is simple linear regression model. In order to study the seasonal effect beside the trend, we choose to use decomposition analysis. There are many different kinds of developed time-series models. Box-Jenkins forecasting model is one of the most famous and relatively accurate time-series models. The univariate version of this model is a self- projecting time series forecasting method. The underlying goal is to find an appropriate formula so that the residuals are as small as
Nowadays, more and more people participate in the stock market. Recent survey reveals that there is a tendency of increasing number of youngsters, especially university students, get involved in the trading activities. We are no exception. Similar to many other investors, we are interested in forecasting the stock prices by using trends, patterns, moving averages observed from historical data.
However, there have been a certain number of people criticizing the use of past data. Among these people, a French mathematician, Louis Bachelier raised a theory called Efficient Market Hypothesis more than a century ago. The theory states that stock prices follow a random walk, which discouraged the study of historical data. This is very controversial and has led to an ever lasting dispute about the reliability of technical analysis. Nonetheless, people’s curiosity about past data has never gone. Being different from the vast majority who use typical technical analysis, we decide to use predictive data mining techniques which we regard as interesting and accurate in our forecasting.
Forecasting is an uncertain process and therefore a high accuracy is demanded. There are many forecasting techniques in the world. In general, they can be classified into three types: casual model, time-series model and smoothing techniques. Undoubtedly, they are of different features and thus are suitable for prediction under certain circumstances. For casual model, the most commonly used technique is simple linear regression model. In order to study the seasonal effect beside the trend, we choose to use decomposition analysis. There are many different kinds of developed time-series models. Box-Jenkins forecasting model is one of the most famous and relatively accurate time-series models. The univariate version of this model is a self- projecting time series forecasting method. The underlying goal is to find an appropriate formula so that the residuals are as small as