Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction
Piecewise linear representation (PLR) and back-propagation artificial neural network (BPN) have been integrated for the stock trading signal prediction recently (PLRBPN). However, there are some disadvantages in avoiding over-fitting, trapping in local minimum and choosing the threshold of the trading decision. Since support vector machine (SVM) has a good way to avoid over-fitting and trapping in local minimum, we integrate PLR and weighted SVM (WSVM) to forecast the stock trading signals (PLRWSVM). The new characteristics of PLRWSVM are as follows: (1) the turning points obtained from PLR are set by different weights according to the change rate of the closing price between the current turning point and the next one, in which the weight reflects the relative importance of each turning point; (2) the prediction of stock trading signal is formulated as a weighted four-class classification problem, in which it does not need to determine the threshold of trading decision; (3) WSVM is used to model the relationship between the trading signal and the input variables, which improves the generalization performance of prediction model; (4) the history dataset is divided into some overlapping trainingtesting sets rather than trainingvalidationtesting, which not only makes use of data fully but also reduces the time variability of data; and (5) some new technical indicators representing investors sentiment are added to the input variables, which improves the prediction performance. The comparative experiments among PLRWSVM, PLRBPN and buy-and-hold strategy (BHS) on 20 shares from Shanghai Stock Exchange in China show that the prediction accuracy and profitability of PLRWSVM are all the best, which indicates PLRWSVM is effective and can be used in the stock trading signal prediction.
stock trading signal prediction plr svm