STOCK EVALUATION USING FUZZY LOGIC

2001 ◽  
Vol 04 (04) ◽  
pp. 585-602 ◽  
Author(s):  
HUSSEIN DOURRA ◽  
PEPE SIY
Keyword(s):  
Fuzzy Logic ◽  
Human Behavior ◽  
Stock Prices ◽  
Stock Price ◽  
Evaluation Method ◽  
Technical Analysis ◽  
Price Movement ◽  
Analysis Theory ◽  
System Output ◽  
Logic System

We use fuzzy logic engineering tools to detect human behavior in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. This system can create an optimum computerized model to evaluate stock price movement. This method relies on human psychology to predict human behavior when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis field. The flexibility of the system is also demonstrated.

scholarly journals Analisis Teknikal Harga Saham Waskita Karya Tahun 2017 dengan Metode ARIMA

2018 ◽  
Vol 7 (1) ◽  
pp. 122-126
Author(s):  
Wahyuni Windasari
Keyword(s):  
Key Words ◽  
Stock Prices ◽  
Stock Price ◽  
Historical Data ◽  
Arima Model ◽  
Security Analysis ◽  
Technical Analysis ◽  
Price Movement

AbstractAs an investor needs to do an analysis before making a decision either in selling or buyingshares. Security analysis consist of two types of analysis, namely tecnical analysis andfundamental analysis. Technical analysis to test wheater historical data will predict stock pricesas a consideration to buy or sell an investment's instrument. One type of technical analysis isthe ARIMA method. In this research uses daily stock price of WSKT Tbk during 1 Januari–10Oktober 2017 to predict stock prices the few days. The best ARIMA model to describe WSKTstock price movement is MA(4), with MAE predict data is 480.25.Key words : forecasting, ARIMA, technical analysis, stock prices.

scholarly journals Technical Analysis and Malaysian Banking Sector during COVID-19 Pandemic

2020 ◽  
Vol 4 (1) ◽  
pp. 41-46
Author(s):  
Kelvin Yong Ming Lee
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Banking Sector ◽  
Moving Average ◽  
Investment Decision ◽  
Movement Control ◽  
Technical Analysis ◽  
Price Movement ◽  
Technical Indicator ◽  
Control Order

The announcements of Movement Control Order and Loan Moratorium caused a significant impact on the stock prices of Malaysian banks during the COVID-19 pandemic. This study aims to investigate the effectiveness of technical analysis in predicting the stock price movement and the ability of the technical analysis in generating returns. In doing so, six moving average rules used as the proxy of technical analysis and tested in this study. Majority of the MA rules shown positive returns before the various announcements dates. Specifically, this study revealed that MA rules of (2,5) and (2,10) were among the best performing MA rules during the COVID-19 pandemic. This study also recommends the investors to use the signals emitted by the technical indicator as the reference for their investment decision in the banks’ stock.

scholarly journals Analisis Teknikal Harga Saham Waskita Karya Tahun 2017 dengan Metode ARIMA

2018 ◽  
Vol 7 (1) ◽  
pp. 80-84
Author(s):  
Wahyuni Windasari
Keyword(s):  
Key Words ◽  
Stock Prices ◽  
Stock Price ◽  
Historical Data ◽  
Arima Model ◽  
Security Analysis ◽  
Technical Analysis ◽  
Fundamental Analysis ◽  
Price Movement

As an investor needs to do an analysis before making a decision either in selling or buying shares. Security analysis consist of two types of analysis, namely tecnical analysis and fundamental analysis. Technical analysis to test wheater historical data will predict stock prices as a consideration to buy or sell an investment's instrument. One type of technical analysis is the ARIMA method. In this research uses daily stock price of WSKT Tbk during 1 Januari–10 Oktober 2017 to predict stock prices the few days. The best ARIMA model to describe WSKT stock price movement is MA(4), with MAE predict data is 480.25.Key words : forecasting, ARIMA, technical analysis, stock prices.

scholarly journals FORECASTING HARGA SAHAM MENGGUNAKAN METODE SIMPLE MOVING AVERAGE DAN WEB SCRAPPING

2019 ◽  
Vol 21 (3) ◽  
pp. 234-241
Author(s):  
Dessy Tri Anggraeni
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Secondary Market ◽  
A Value ◽  
Price History ◽  
The Difference ◽  
Absolute Percent Error ◽  
Error Method

Abstract:  The fluctuative of stock prices in a secondary market provide the possibility for investors/traders to gain profits through the difference in stock prices (capital gain). In order to obtain these benefits, it is necessary to analyze before buying shares, through fundamental and technical analysis. One of several methods in Technical Analysis is Simple Moving Average Method. This method can be used to predict (forecast) stock prices by calculating moving average of the stock price history. Historical stock prices can be obtained in real time using the Web Scrapper technique, so the results is more quickly and accurately. Using the MAPE (Mean Absolute Percent Error) method, the level of accuracy of forecasting can be calculated. As a result, the program was able to run successfully and was able to display the value of forecasting and the level of accuracy for the entire data tested in LQ45. Besides forecasting with a value of N = 5 has the highest level of accuracy that reaches 97,6 % while the lowest one is using the value of N = 30 which is 95,0 %.

scholarly journals Perancangan Aplikasi Analisis Saham Menggunakan Metode Rate Of Change

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Syahrul Anwar
Keyword(s):  
Stock Price ◽  
Technical Analysis ◽  
Rate Of Change ◽  
Analysis Method ◽  
Main Concept ◽  
Price Movement ◽  
Final Project ◽  
Stock Investment ◽  
Closing Price ◽  
The Difference

Stock is the most popular method today for investing and offers a lot of profit. The profit came from the difference between the prices when you buy it and when you sell it. Even though, the risk for investing using stock is very big. That is why stock investment must be done with correct analysis to maximize the profit and to avoid loss.One of methods for analyzing stock price movement is technical analysis. Technical analysis is a method which is based on stock price movement in the past time. One of popular technical analysis method is Rate of Change. The main concept is to compare current closing price with the closing price x-times periods ago. Using this method, stock broker will know the pace at which price is changing. Tracking the rate of the change of price can confirm trends and forewarn of market reversals. In this final project, I’ve tried to study the process of technical analysis using Rate of Change method, and then I developed software to implement this method. In the early phase, I studied some literature which is related to theories of stock market and technical analysis. After that I did some analyses which involve the analysis of how Rate of Change method works and functional specifications of the software which I would develop. Based on the results of the analysis, I did the design process. The purpose of Rate of Change test is to examine the accuracy of this method on detecting trade signals. And then this final project closed with the conclusion and suggestion for future development. Key Words:

scholarly journals THE INFLUENCE OF USING FUNDAMENTAL AND TECHNICAL ANALYSIS ON SHARE PRICES

2021 ◽  
Vol 5 (2) ◽  
pp. 103-111
Author(s):  
Firdaus Gusti Redha romadi putra ◽  
Eni Wuryani
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Sample Selection ◽  
Technical Analysis ◽  
Share Price ◽  
Return On Assets ◽  
Book Value ◽  
Share Prices ◽  
Food And Beverage

This study aims to determine the effect of the variables contained in fundamental and technical analysis of stock prices. Variables used include Earning Per Share, Return On Assets, Book Value Per Share, Price to Book Value, Past Share Prices, Dup and Ddown. Sample selection uses saturated samples by using all food and beverage companies listed on the Indonesia Stock Exchange in the 2014-2018 period. The data analysis technique used is regression analysis using SPSS 23. The results of the study show that simultaneously all variables affect the stock price. Partially Earning Per Share, Price to Book Value, Past Share Prices, and Ddown have a significant effect on stock prices, while Return On Assets, Book Value Per Share, and Dup have no significant effect on stock prices.

scholarly journals Stock Price Movement through Technical Analysis: Empirical Evidence from the Information Technology (IT) Sector

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Shalini Singh ◽  
Anindita Chakraborty
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Technical Analysis ◽  
Future Price ◽  
Analysis Tool ◽  
Price Movement ◽  
Technical Indicators ◽  
Share Prices ◽  
The Future

<em>Technical analysis forecasts the future asset prices with the use of their historical prices, trading volumes, market action and primarily through the uses of charts that predicts the future price trends. Technical analysis guides the investor to track the market with different indicators which is convenient for their study. Technical indicators aids to analyse the short-term price movement of the shares, most importantly it indicates the turning point and helps in projecting the price movement. This paper is prepared to employ the technical analysis tool to IT index companies. Indicators have been analysed using share prices of companies for 1 years, i.e., from January 2015- December 2015. Study is performed using secondary data, which has been collected from NSE website. The Technical Indicators used for the study are Bollinger Bands and MACD (Moving Average Convergence and Divergence). The purpose of the study is to find the best technical indicator to analyse the share prices.</em>

scholarly journals Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Ikhlaas Gurrib ◽  
Mohammad Nourani ◽  
Rajesh Kumar Bhaskaran
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Oil Prices ◽  
Technical Analysis ◽  
Price Increases ◽  
Current Break ◽  
Study Methodology ◽  
Energy Companies ◽  
Better Than

AbstractThis paper investigates the role of Fibonacci retracements levels, a popular technical analysis indicator, in predicting stock prices of leading U.S. energy companies and energy cryptocurrencies. The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy. Daily crypto and stock prices were obtained from the Standard & Poor's composite 1500 energy index and CoinMarketCap between November 2017 and January 2020. This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk. Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos. Furthermore, most price violations were frequent during price falls compared to price increases, supporting that the Fibonacci instrument does not capture price movements during up and downtrends, respectively. Also, fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break. Furthermore, the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model. Finally, complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents. This study’s overall findings elucidate that, despite significant drops in oil prices, speculators (traders) can implement profitable strategies when using technical analysis indicators, like the Fibonacci retracement tool, with or without price crossover rules.

Predicting Stock Price Movement from Financial News Articles

2011 ◽  
pp. 96-128
Author(s):  
Robert P. Schumaker ◽  
Hsinchun Chen
Keyword(s):  
Mathematical Models ◽  
Stock Prices ◽  
Stock Price ◽  
Financial Data ◽  
Price Movement ◽  
Computational Approaches ◽  
Stock Recommendations ◽  
Fund Managers ◽  
Financial News ◽  
Computational Systems

However, using computational approaches to predict stock prices using financial data is not unique. In recent years, interest has increased in Quantitative funds, or Quants, that automatically sift through numeric financial data and issue stock recommendations. While these systems are based on proprietary technology, they do differ in the amount of trading control they have, ranging from simple stock recommenders to trade executors. Using historical market data and complex mathematical models, these methods are constrained to make assessments within the scope of existing information. This weakness means that they are unable to react to unexpected events falling outside of historical norms. However, this disadvantage has not stopped fund managers at Federated, Janus, Schwab, and Vanguard from trusting billions of dollars of assets to the decisions of these computational systems.

Sign in / Sign up

Export Citation Format

Share Document