| کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
|---|---|---|---|---|
| 982571 | 1480382 | 2015 | 12 صفحه PDF | دانلود رایگان |
واژه های کلیدی
1. مقدمه
2. بررسی مطالعات قبلی
3. روش شناسی و داده ها
3.1. توصیف داده ها
جدول 1. شاخص های توسعه بازار سهام در سال 2012.
3.2. روش شناسی
3.2.1. استراتژی های تجاری فنی
3.2.2. استراتژی های تجاری بر پایه مدل پیش بینی LS-SVM
3.2.2.1. مبانی نظریه LS-SVM
3.2.2.2. فرمولاسیون مدل
4. نتایج و بحث
This research examines the efficacy of technical analysis and predictive modeling in defining the optimal strategy for investing in the stocks indices of emerging markets. Trading strategies are set regarding different technical indicators based on moving averages and volatility of the value and returns on stock indices. Simple trading rules are generated using two moving averages – a long period and a short period moving average, and Moving Average Convergence-Divergence (MACD) and Relative Strength Index (RSI). Selected technical indicators are used as features in defining predictive model based on Least Squares Support Vector Machines (LS-SVMs). A LS-SVM classifier has been used in order to predict trend of the stock indices’ value whereby the obtained outputs of the LS-SVM model are binary signals that can be used for defining the trading strategy. Comparing the results obtained from traditional statistical methods for predicting the trend of financial series and proposed LS-SVM model, it can be concluded that machine learning techniques capture the non-linear models which are dominant in the financial markets in more adequate way. Outperforming the results of Buy & Hold strategy and technical trading strategies, application of LS-SVM in decision making process on investing on the financial market significantly can contribute to maximization of profitability on investment.
Journal: Procedia Economics and Finance - Volume 19, 2015, Pages 51-62