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 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 PERGERAKAN HARGA SAHAM DENGAN MENGGUNAKAN INDIKATOR STOCHASTIC OSCILLATOR DAN WEIGHTED MOVING AVERAGE

KEUNIS ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Nur Alviyanil 'Izzah ◽  
Dina Yeni Martia ◽  
Maria Imaculata ◽  
Moh Iqbal Hidayatullah ◽  
Andhika Bagus Pradana ◽  
...  
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
First Semester ◽  
Share Prices ◽  
Stochastic Oscillator ◽  
Industrial Sectors ◽  
The Right ◽  
Weighted Moving Average

<p><em>Investments in the stock market are closely related to the price movement risk. Investors used technical analysis to minimize the risk caused by changes in stock prices. Hence, investors get the right decision to buy or sell the stocks. Weighted Moving Average and Stochastic Oscillator are technical analysis indicators that investors often use due to their ease and accuracy predictions. This study combines the Stochastic Oscillator and Weight Moving Average (WMA) indicators to predict stock price movements in various industrial sectors during the 2015 to 2019 period and the first semester of the 2020 covid-19 pandemic outbreak in Indonesia. This combination aims to provide better predictive results by completing the weaknesses of each indicator. To provide recommendations for the right investment decisions for investors interested in investing their funds, especially in various industrial sectors. Using the combination of WMA and SI indicator charts from the investing.com website resulting in a better prediction of the right time to buy or sell stocks in various industry sectors. While the shares of SCCO, INDR, INDS appear stable during 2020, the movement of KLBM and KLBI's share prices seems to be affected by the Covid-19 pandemic in Indonesia.</em></p>

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Shishir Kumar Gujrati
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Future Price ◽  
Range Data ◽  
Strength Index ◽  
Price Trends ◽  
Past Data ◽  
Price Movements

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.

scholarly journals PENGARUH VOLUME PERDAGANGAN DAN SIMPLE MOVING AVERAGE TERHADAP HARGA SAHAM

2020 ◽  
Vol 1 (1) ◽  
pp. 1-16
Author(s):  
Gama Paksi Baskara ◽  
Suyanto Suyanto ◽  
Sri Retnaning Rahayu
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Qualitative Data ◽  
Moving Average ◽  
Analytical Tool ◽  
T Test ◽  
Quantitative Approach ◽  
F Test ◽  
Regression Tests

Trading volume is a sheet of company shares traded on a particular transaction and has beenagreed between the seller and the buyer, Simple Moving Average is a method that studies themovement of the previous stock price based on the number of certain days in order to predict thestock price that will occur to the next.The objective of the study is to find out how much influenceTrade Volume and Simple Moving Average on Stock Prices is and what are the most dominantaspects in influencing Stock Prices. The type of the research uses a quantitative approach, namely anapproach in which the data are in the form of numbers or qualitative data that have been used asnumbers. The technique of collecting data uses documentation. The analytical tool used is multiplelinear regression tests including T Test, F Test and Coefisein R² Determination processed usingEviews. The results of the study show that partially the trading volume variable does not have asignificant effect on Stock Prices and the Simple Moving Average variable shows a positive andsignificant effect on stock prices while the results of the research simultaneously show that theTrading Volume and Simple Moving Average variables simultaneously affect the Stock Price .

scholarly journals Comparison of ARIMA, ANN and LSTM for Stock Price Prediction

2020 ◽  
Vol 218 ◽  
pp. 01026
Author(s):  
Qihang Ma
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Ability ◽  
Research Direction ◽  
Ann Model ◽  
Advantages And Disadvantages ◽  
Stock Price Prediction

The prediction of stock prices has always been a hot topic of research. However, the autoregressive integrated moving average (ARIMA) model commonly used and artificial neural networks (ANN) still have their own advantages and disadvantages. The use of long short-term memory (LSTM) networks model for prediction also shows interesting possibilities. This article compares three models specifically through the analysis of the principles of the three models and the prediction results. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing. The ANN model performs better than that of the ARIMA model. The combination of time series and external factors may be a worthy research direction.

scholarly journals FORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS

2020 ◽  
pp. 33-41
Author(s):  
Olena Nikolaieva ◽  
Anzhela Petrova ◽  
Rostyslav Lutsenko
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Financial Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Financial Indicators ◽  
Econometric Methods ◽  
Global Companies ◽  
The Moment ◽  
Data Analysis Methods

In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.

scholarly journals Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jian Wang ◽  
Junseok Kim
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Rapid Development ◽  
Price Trend ◽  
Technical Indicators ◽  
Volatility Index ◽  
Fixed Weight ◽  
The Stability

With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.

The real effects of primary and secondary equity markets on firm performance

2017 ◽  
Vol 13 (4) ◽  
pp. 397-418 ◽  
Author(s):  
Andriansyah Andriansyah
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Operating Performance ◽  
Equity Markets ◽  
Secondary Market ◽  
Price Informativeness ◽  
Content Type ◽  
Real Effects ◽  
Stock Price Informativeness

Purpose The purpose of this paper is to investigate the real effects of primary and secondary equity markets on the post-issue operating performance of initial public offering (IPO) firms. Design/methodology/approach The author utilizes the intended use of proceeds as a proxy variable for the primary market and the investment-to-price sensitivity and the informativeness of stock prices as alternative proxy variables for the secondary market. The compositional data, and non-parametric quantile regressions which are more robust to outliers than standard least square regressions, are employed for Indonesian equity market over the period of 1999-2013. Findings While confirming that firm operating performance can be explained by the firm’s motivation to go public, the author also shows that the operating performance is positively affected by investment-to-price sensitivity and negatively affected by stock price informativeness. The stock prices affect investment decisions by the way that the more liquid a stock is, the more informative its price is, and the more relevant stock prices are in investment decisions. These findings still hold after controlling for ownership structure. Originality/value Departing from the existing literature, the author investigates the role of primary and secondary equity markets for firm performance in an integrated framework because both markets interact closely in reality. The author shows that public listed firms can benefit both from the capital-raising function of the primary market and from the informational role of the stock prices of the secondary market. A measure of stock price informativeness, 1−R2, however, must be understood in the context of thin trading in the sense that the level of liquidity affects the level of stock price informativeness.

scholarly journals Forecasting Net Income Estimate and Stock Price Using Text Mining from Economic Reports

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 292 ◽  
Author(s):  
Masahiro Suzuki ◽  
Hiroki Sakaji ◽  
Kiyoshi Izumi ◽  
Hiroyasu Matsushima ◽  
Yasushi Ishikawa
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Net Income ◽  
The Difference

This paper proposes and analyzes a methodology of forecasting movements of the analysts’ net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the pre-experiment, we applied our method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we performed two additional experiments. First, we employed our proposed method for forecasting the movements of analysts’ net income estimates by inputting the opinion and non-opinion sentences into separate neural networks. Besides the reports, we inputted the trend of the net income estimate to the networks. Second, we employed our proposed method for forecasting the movements of stock prices. Consequently, we found differences between security firms, which depend on whether analysts’ net income estimates tend to be forecasted by opinions or facts in the context of analyst reports. Furthermore, the trend of the net income estimate was found to be effective for the forecast as well as an analyst report. However, in experiments of forecasting movements of stock prices, the difference between opinion sentences and non-opinion sentences was not effective.

scholarly journals Pengaruh Informasi Akuntansi dan Permintaan Investor terhadap Underpricing

2020 ◽  
Vol 30 (3) ◽  
pp. 746
Author(s):  
Made Aida Pradnyadevi ◽  
I Made Sadha Suardikha
Keyword(s):  
Stock Prices ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Financial Statements ◽  
Total Sample ◽  
Multiple Linear Regression Analysis ◽  
Secondary Market ◽  
Analysis Technique ◽  
Bid Price ◽  
The Difference

Underpricing is a phenomenon that often occurs from IPO activities on the IDX. Underpricing is the difference in stock prices that occur in the primary .market’ and secondary’ market, where the bid price is lower than the closing price of the first trading day. The purpose of this research is to find out the effect of accounting information and investor demand on underpricing.This research was conducted’.at companies whose IPO on .the’ Stock .Exchange in 2016-2018. Data collection was obtained from the collection of prospectuses and company financial statements. The total sample of 81 companies using a purposive sampling method. The analysis technique used is multiple linear regression analysis. This study proves that profitability and firm size negatively affect underpricing, while financial leverage and investor demand have no effect on underpricing. Keywords: Underpricing; Profitability; Company Size; Investor Demand.

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