Neural Networks with Technical Indicators Identify Best Timing to Invest in the Selected Stocks

نویسندگان

  • Asif Ullah Khan
  • Bhupesh Gour
چکیده

Selections of stocks that are suitable for investment are always a complex task. The main aim of every investor is to identify a stock that has potential to go up so that the investor can maximize possible returns on investment. After identification of stock the second important point of decision making is the time to make entry in that particular stock so that investor can get returns on investment in short period of time. There are many conventional techniques being used and these include technical and fundamental analysis. The main issue with any approach is the proper weighting of criteria to obtain a list of stocks that are suitable for investments. This paper proposes an improved method for stock picking and finding entry point of investment that stock using a hybrid method consist of self-organizing maps and selected technical indicators. The stocks selected using our method has given 19.1% better returns in a period of one month in comparison to SENSEX index.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Prediction-Based Portfolio Optimization Model for Iran’s Oil Dependent Stocks Using Data Mining Methods

This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...

متن کامل

Ii. Application of Neural Networks in Stocks

Identification of useful patterns in price movement of a stock in stock market needs tremendous analytical skills and effort. Careful analysis of the available technical indicators will help finding the right timing of trading of a stock to maximize the gains. To help investors manage their portfolios, we propose a tool for clustering and classification of stock market data using an unsupervise...

متن کامل

On the use of back propagation and radial basis function neural networks in surface roughness prediction

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...

متن کامل

Classification of ECG signals using Hermite functions and MLP neural networks

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of ...

متن کامل

A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis

In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of  Raw-based and Signal-ba...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015