Stock Price Forecast By Using Neuro-Fuzzy Inference System

International Journal of Computer Applications, 2015

Stock market prediction plays a vital rule in taking financial decisions. Various factors affecting the stock market makes stock prediction somewhat complex and difficult. Different data mining techniques such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) etc are being widely used for predicting stock prices of different stock exchange cases. But there is no good work on stock prediction using ANN and ANFIS for Bangladesh Stock Markets. The goal of this paper is to find out an efficient soft computing technique for Dhaka Stock Exchange (DSE) closing data prediction. In this paper, ANN and ANFIS have been applied on different companies previous data such as opening price, highest price, lowest price, total share traded. The day end closing price of stock is the outcome of ANN and ANFIS model. Our experiment shows that, ANFIS is more effective and efficient technique to predict Dhaka Stock exchange (DSE) data.

JOIV : International Journal on Informatics Visualization

The financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both fields of fuzzy logic computing and the financial world in the use of fuzzy logic to predict future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy logic provides a way to draw definite conclusions from vague, ambiguous or imprecise information. Artificial Neural Network is one of data mining techniques being widely accepted in the business area ...

In 2010, Hadavandi, Shavandi & Ghanbari noted that fuzzy inference systemused for forecasting stock price requires an effective Rule Base and Knowledge Base to be built in its Expert System. Similarly, neural networks rely on expert systems in predicting quantities such as stock price. However, forecasts of stock price movements are non-linear and linear at the same time, which make the traditional time series and regression analysis less significant in delivering accurate outcomes especially when multiple factors(such as opening price, highest price and closing) are considered. This paper analyzed the forecasting capabilities of two widely deployed expert systems, fuzzyand neural networks. Also, the MSE, RMSE, MAPE and RAE served as performance measuring parameters.

International Journal of Advanced Trends in Computer Science and Engineering, 2020

Using features derived from technical indicators of stocks and foreign exchange rates, this paper presents a forecasting plan for the KOSPI's rise and fall after one day based on the neural network with weighted fuzzy membership functions (NEWFM). To extract the features to be used in NEWFM, select the three technical indicators in the first step: Relative Strength Index (RSI), Commodity Channel Index (CCI), and Current Price Change (CPC). The second phase extracts 13 features derived from the KOSPI data from the three technology indicators selected in the first stage. It also extracts one feature derived from the technical indicators of the KRW/USD exchange rates from the KRW/USD exchange rates. In this way, 14 features will be used as inputs to NEWFM to forecast the KOSPI's rise and fall in a day. NEWFM uses 13 and 14 features to show forecasting performance of 58.86% and 59.38%, respectively. These experiments show that the KRW/USD exchange rates affects the rise and fall of KOSPI stock prices.

Journal of Computer Science and Cybernetics, 2012

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2020

The aim of this research is to analyze ANFIS performance for prediction of financial time series data. Financial time series data is usually characterized by volatility clustering, persistence, and leptokurtic data behavior. The financial time series data are usually non-stationary and non-linear. ARIMA has a good performance to predict linear time series data, but its performance is decreasing when applied to predict non-linear times series data. ANFIS as one of hybrid models which composes neural network (NN) and fuzzy system is expected to be able to predict the financial time series data more accurate. Many research conclude that the effectiveness of ANFIS depend on the input selection, the membership function (MF) selection and the rule generation. In this study, the input variables of ANFIS are selected based on preprocessing of original data by using Subset ARIMA model. The rule bases of ANFIS are generated based on a linear Sugeno fuzzy model. The consequent parameters are i...

Eastern-European Journal of Enterprise Technologies

Adaptive neuro-fuzzy inference system (ANFIS) it is a type of neural network that combines the strengths of both fuzzy logic and artificial neural networks. ANFIS is particularly useful in stock trading because it can handle uncertainty and imprecision in the data, which is common in stock market data. In stock trading, ANFIS can be used for a variety of purposes, such as predicting stock prices, identifying profitable trades, and detecting stock market trends. One of the key advantages of using ANFIS for stock trading is that it can handle both linear and non-linear relationships in the data. This is particularly useful in the stock market, where the relationships between different variables are often complex and non-linear. ANFIS can also be updated and retrained as new data becomes available, which allows it to adapt to changing market conditions. Therefore, the main hypothesis of this work is to understand whether it is possible to predict the dynamics of prices for stocks of co...

Expert Systems with Applications, 2016

2015

Analysis and prediction of stock market is very interesting as this helps the financial experts in decision making and in turn profit making. In this paper an Adaptive Neuro-Fuzzy Inference System (ANFIS) model is initially considered for stock market prediction and its result is compared. The substance of the design of Adaptive Neuro-Fuzzy Inference System (ANFIS) can be seen as an optimization problem to find the best parameters with minimal error function. This proposed scheme proposes a combination of the Firefly Algorithm and Adaptive Neuro-Fuzzy Inference System. The fuzzy neural network model will be trained by the Firefly Algorithm, and applied to predict stock prices in the Vietnam Stock Market. The experiments will compare performance between the proposed system and ANFIS trained by the Hybrid Algorithm, Back Propagation and Particle Swarm Optimization (PSO). The experimental results show that the system has reasonable efficient performance. In this thesis Adaptive Neuro-F...

International Journal of Intelligent Systems, 2011

We propose an adaptive neuro-fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection-clustering algorithm. The neuro-fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc.