This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.
 P. Sutheebanjard and W. Premchaiswadi, “Stock Exchange of Thailand Index Prediction Using Back Propagation Neural Networks,” 2nd International Conference on Computer and Network Technology, Bangkok, 23-25 April 2010.
 V. Pacelli, V. Bevilacqua and M. Azzollini, “An Artificial Neural Network Model to Forecast Exchange Rates,” Journal of Intelligent Learning Systems and Applications, Vol. 3, No. 2, 2011. pp. 57-69.
 A. La Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, No. 1, 1973, pp. 28-44. doi:10.1109/TSMC.1973.5408575
 S. Agrawal, M. Jindal, G. N. Pillai, “Momentum Analysis Based Stock Market Prediction Using Adaptive NeuroFuzzy Inference System (ANFIS),” Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, 17-19 March 2010.
 J. M. Anthony, N. F. Robert, L. Yang, A. W. Nathaniel, and D. B. Steven, “An Introduction to Decision Tree Modeling,” Journal of Chemometrics, Vol. 18, No. 6, 2004, pp. 275-285. doi:10.1002/cem.873
 M. Maragoudakis and D. Serpanos, “Towards Stock Market Data Mining Using Enriched Random Forests from Textual Resources and Technical Indicators,” Artificial Intelligence Applications and Innovations IFIP Advances in Information and Communication Technology, Larnaca, 6-7 October 2010, pp. 278-286
 Q.-G. Wang, J. Li, Q. Qin, S. Z. Sam Ge, “Linear, Adaptive and Nonlinear Trading Models for Singapore Stock Market with Random Forests,” Proceedings of 9th IEEE International Conference on Control and Automation, Santiago, 19-21 December 2011, pp. 726-731.
 Chart School, Stock Chart.com, 2010. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators
 Schoolworkout Math, “Spearman’s Rank Correlation Coefficient,” 2011. http://www.schoolworkout.co.uk/documents/s1/Spearmans%20Correlation%20Coefficient.pdf
 J. M. Anthony, N. F. Robert, L. Yang, A. W. Nathaniel and D. B. Steven, “An Introduction to Decision Tree Modeling,” Journal of Chemometrics, Vol. 18, No. 6, 2004, pp. 275-285. doi:10.1002/cem.873
 Fees: Schedules, DBS Vikers Online, 2010. http://www.dbsvonline.com/English/Pfee.asp?SessionID=&RefNo=&AccountNum=&Alternate=&Level=Public&ResidentType=