No abstract
A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks (stock price time series, in USD) of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with timeparameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance. After which, multi-period portfolio optimization is done to demonstrate the practicality of using the rules in the real world.
An effective financial market trading decision is usually dependent on superior forecasting. Forex market as the largest financial market is chosen in this study. The main objective of this paper is to explore the forecasting performance of the proposed multiple-price model which integrates close, low and high price information, by using Artificial Neural Network (ANN). The architecture of the network and the related algorithms are described. The effects due to different choices of preprocessing methods, combinations of input variables and different time intervals of forecasting are examined. By using the best multiple-price model, trading strategies with high and low prices are developed as well. The results have shown that in terms of both absolute values and trends of the prices, forecasting accuracy has improved compared with single price model. This is especially so for low and high prices whose directional accuracies are much higher. The trading performance is also proven to have much better total return than buy & hold strategy, and trading with high price has the best overall performance considering both return and risk.
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