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 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 Penerapan Model ARIMA-ARCH untuk Meramalkan Harga Saham PT. Indofood Sukses Makmur Tbk

2021 ◽  
Vol 3 (3) ◽  
pp. 171-177
Author(s):  
Yulvia Fitri Rahmawati ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

Abstract– The stock price is the value of the stock in the market that fluctuates from time to time. Time series data in the financial sector generally have quite high volatility which can cause heteroscedasticity problems. This study aims to model and to predict the stock price of PT Indofood Sukses Makmur Tbk using the ARIMA-ARCH model. The data used is daily stock prices from 2nd June 2020 to 15th February 2021 as training data, while from 16th February 2021 to 1st March 2021 as testing data. ARIMA-ARCH model is a model that combines Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH), which can be used to overcome the residues of the ARIMA model which are indicated to have heteroscedasticity problems. The result showed that the model that could be used was ARIMA(1,1,2)-ARCH(1). This model can provide good forecasting result with a relatively small MAPE value of 0.515785%. Abstrak– Harga saham adalah nilai saham di pasar yang berfluktuasi dari waktu ke waktu. Data runtun waktu di sektor keuangan umumnya memiliki volatilitas cukup tinggi yang dapat menyebabkan masalah heteroskedastisitas. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga saham PT Indofood Sukses Makmur Tbk menggunakan model ARIMA-ARCH. Data yang digunakan adalah harga saham harian dari 2 Juni 2020 hingga 15 Februari 2021 sebagai data training, sedangkan dari 16 Februari 2021 hingga 1 Maret 2021 sebagai data testing. Model ARIMA-ARCH merupakan suatu model yang menggabungkan Autoregressive Integrated Moving Average (ARIMA) dan Autoregressive Conditional Heteroscedasticity (ARCH), yang dapat digunakan untuk mengatasi residu dari model ARIMA yang terindikasi memiliki masalah heteroskedastisitas. Hasil penelitian menunjukkan bahwa model yang dapat digunakan adalah ARIMA(1,1,2)-ARCH(1). Model tersebut mampu memberikan hasil peramalan yang baik dengan perolehan nilai MAPE yang relatif kecil yaitu 0,515785%.

Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 16 (8) ◽  
pp. 3519-3524
Author(s):  
Loh Chi Jiang ◽  
Preethi Subramanian
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Stock Index ◽  
Series Data ◽  
Autoregressive Integrated Moving Average

Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.

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 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.

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 testing in forecasting Socially Responsible Investment Index in Indonesia Stock Exchange

2018 ◽  
Vol 15 (4) ◽  
pp. 135-143
Author(s):  
Dolly Parlagutan Pulungan ◽  
Sugeng Wahyudi ◽  
Suharnomo Suharnomo ◽  
Harjum Muharam
Keyword(s):  
Stock Prices ◽  
Average Method ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Transformation Process ◽  
Socially Responsible ◽  
Responsible Investment ◽  
Autoregressive Integrated Moving Average ◽  
Index Data

This study aims to examine whether the Autoregressive Integrated Moving Average (ARIMA) model is appropriate to be applied in the Indonesia Stock Exchange, especially for the socially resposible investment stocks. For the ARIMA model combines the autoregressive and moving average method, so it is viewed as a useful tool to predict the stock prices. Those methods are frequently used methods to forecast the stock prices. The data used in this study were daily SRI-KEHATI Index during the period of June 8, 2009 to July 17, 2017. The results showed that the daily SRI-KEHATI Index data were not stationary data, thus this data needed to be transformed. The transformation was done by using the first seasonal differencing transformation process. After being transformed, those data became stationary. Furthermore, this study found that ARIMA (3,1,1) was a model, which might be appropriate and fit with the data condition. This method was also relevant to be applied in the Indonesia Stock Exchange in order to forecast the stock prices.

scholarly journals PENGARUH ARUS KAS TERHADAP HARGA SAHAM PADA PERUSAHAAN MANUFAKTUR DI BURSA EFEK INDONESIA PERIODE 2015-2018

AJAR ◽  
2020 ◽  
Vol 3 (01) ◽  
pp. 21-47
Author(s):  
Muchriana Muchran ◽  
M. Fajrin A. Thaib
Keyword(s):  
Cash Flow ◽  
Stock Prices ◽  
Stock Price ◽  
Descriptive Analysis ◽  
Cash Flows ◽  
Quantitative Descriptive Analysis ◽  
Quantitative Descriptive ◽  
Simultaneous Influence ◽  
Data Analysis Methods ◽  
Operating Activities

This study aims to analyze the effect of cash flow from operating activities, investment activities, funding activities that have an impact on stock prices and analyze the effect of cash flows from operating activities, investment activities, funding activities simultaneously affecting stock prices. Data analysis methods used are quantitative descriptive analysis and multiple regression analysis. Based on the results of a partial test, the effect of cash flow from operating activities on stock prices has a positive and significant effect, thus the first hypothesis is accepted. Based on the results of a partial test between investment cash flow to stock prices, it was found that cash flow from investment activities was not significant, thus the second hypothesis was rejected. Based on the results of a partial test between the cash flows of funding activities against the stock price, it was found that the funding cash flow was not significant. This can be interpreted that the funding cash flow has no effect on stock prices, thus the third hypothesis is rejected. And the results of simultaneous tests prove that there is a simultaneous influence between cash flow operating activities, investment activities and funding activities on stock prices. Thus the fourth hypothesis is accepted.

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.

Creation of Stock Analysis System Depend on the Prediction Research of Individual Stock Price Behavior by Moving Average Method

2012 ◽  
Vol 601 ◽  
pp. 547-553
Author(s):  
Pang Wen Ling
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Average Method ◽  
Moving Average ◽  
Market Potential ◽  
General Rules ◽  
Data Source ◽  
Stochastic Data ◽  
Analysis System

The Taiwan stock market has millions of investors. If each investor spends $5,000 NTD each month on a professional technical analysis system and it is conservatively estimated 100,000 investors would use the system, the market potential will be $500 million NTD. If we can seek out irregular general rules of stock price data on stock market and further make predictions to some extent, the return on investment may be effectively raised, and investment loss reduced for the investors. This study will take Taiwan stock market as data source and use Moving Average Method (MA) to conduct study and analysis of stochastic data of stock price. One stocks (TSMC) typical in Taiwan’s industries and with large-cap index weights are studied to explore whether there are any specific changes and regularities in seemingly irregular stock prices.. We should depend on the front research to develop an effective stock trading analysis system, and we can help investors obtain profits. At the same time, the system users can also make more considerable profits.

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