TY  - CONF
EP  - 327
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010430957&doi=10.1109%2fICCOINS.2016.7783235&partnerID=40&md5=57a228268651c24800bc7d19bfa93750
A1  - Usmani, M.
A1  - Adil, S.H.
A1  - Raza, K.
A1  - Ali, S.S.A.
SN  - 9781509051342
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2016///
KW  - Artificial intelligence; Commerce; Electronic trading; Financial markets; Forecasting; Information science; Learning algorithms; Neural networks; Radial basis function networks; Support vector machines
KW  -  Auto-regressive integrated moving average; KSE-100 Index; Machine learning techniques; Multi layer perceptron; Radial Basis Function(RBF); Single layer perceptron; Stock market prediction; Stock predictions
KW  -  Learning systems
ID  - scholars6480
TI  - Stock market prediction using machine learning techniques
SP  - 322
N1  - cited By 73; Conference of 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125433
N2  - The main objective of this research is to predict the market performance of Karachi Stock Exchange (KSE) on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. The attributes used in the model includes Oil rates, Gold & Silver rates, Interest rate, Foreign Exchange (FEX) rate, NEWS and social media feed. The old statistical techniques including Simple Moving Average (SMA) and Autoregressive Integrated Moving Average (ARIMA) are also used as input. The machine learning techniques including Single Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are compared. All these attributes are studied separately also. The algorithm MLP performed best as compared to other techniques. The oil rate attribute was found to be most relevant to market performance. The results suggest that performance of KSE-100 index can be predicted with machine learning techniques. © 2016 IEEE.
AV  - none
ER  -