relation: https://khub.utp.edu.my/scholars/6480/
title: Stock market prediction using machine learning techniques
creator: Usmani, M.
creator: Adil, S.H.
creator: Raza, K.
creator: Ali, S.S.A.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Usmani, M. and Adil, S.H. and Raza, K. and Ali, S.S.A.  (2016) Stock market prediction using machine learning techniques.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010430957&doi=10.1109%2fICCOINS.2016.7783235&partnerID=40&md5=57a228268651c24800bc7d19bfa93750
relation: 10.1109/ICCOINS.2016.7783235
identifier: 10.1109/ICCOINS.2016.7783235