Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging Markets

摘要: Abstract This research examines the efficacy of technical analysis and predictive modeling in defining optimal strategy for investing stocks indices emerging markets. Trading strategies are set regarding different indicators based on moving averages volatility value returns stock indices. Simple trading rules generated using two – a long period short average, Moving Average Convergence-Divergence (MACD) Relative Strength Index (RSI). Selected used as features model Least Squares Support Vector Machines (LS-SVMs). A LS-SVM classifier has been order to predict trend indices’ whereby obtained outputs binary signals that can be strategy. Comparing results from traditional statistical methods predicting financial series proposed model, it concluded machine learning techniques capture non-linear models which dominant markets more adequate way. Outperforming Buy & Hold strategies, application decision making process market significantly contribute maximization profitability investment.