Market Forecasting using (2D) 2 PCA + RBFNN BY: DANNY SANCHEZ.

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Presentation transcript:

Market Forecasting using (2D) 2 PCA + RBFNN BY: DANNY SANCHEZ

Current Models (market forecasting)  Auto-regressive Moving Average (ARIMA)  Univariate  Benchmark used for comparisons  Predictor based regression model  Multivariate  Various types of neural networks used

“A Stock Market Forecasting Model Combining Two- Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network ”  Paper published: April 7, 2015  Authors: Zhiq Guo, Huaiqing Wang, Jie Yang, David J. Miller  Subject: Chinese DOW  Multivariate model Proposed:  (2D) 2 PCA + RBFNN  “The proposed (2D)^2 PCA+RBFNN model use (2D)^2 PCA to remove the noise from the input raw data, the feature will contain less noise information and serve as the input of the RBFNN to predict the value or movement of the next day’s closing price.”  Hardware:  2.30 GHz CPU + 2Gb RAM + MATLAB software

General Outline  Data Source (Open, High, Low, Adj. Close) -> Create Predictor variables -> Reduce noise/extract features ((2D) 2 PCA ) -> Use reduced matrix as input for RBFNN

Predictor Variables  Using market data (usually in.csv format) containing historical: Open – High – Low – Adj. Close  Predictors created using common technical indicator formulas. For the this research study 36 predictors were used including:  MACD – Stochastic – Relative Strength Indicator  Three of the most commonly used momentum indicators by technical traders

(2D) 2 PCA  Based on the idea of Principle Component Analysis  Been used in signals processing for noise reduction for years  2D aims to reduce dimensionality while retaining usefulness of data by reducing variation Ex.

RBFNN  Three-layered structure  Input Layer -> Hidden Layer -> Output Layer

Results

“Replicating (2D)2PCA + RBFNN market forecasting model in R”  Authors: Danny Sanchez  Subject: S&P 500 (^GSPC) otherwise known as SPX  Multivariate model used: (2D) 2 PCA + RBFNN  Similar to the original research but I have chosen my own predictor variables based on market knowledge  Hardware:  2.30 GHz CPU + 2Gb RAM + MATLAB software

Predictor Variables  Using Yahoo Finance Historical data for S&P 500 & VIX (Volatility Index) containing: Open – High – Low – Adj. Close  Based on previous trading knowledge, I chose 20 predictor vectors including:  MACD – Stochastic – Relative Strength Indicator  Three of the most commonly used momentum indicators by technical traders

Results

Results - Personal