Triangulation between Bernoulli Distribution and Laplacian Autoregressive Model to Predict Probability of Increase in Stock Price

2021 ◽  
Vol 9 (4) ◽  
pp. 588-593
Author(s):  
Suparman Suparman ◽  
A. M. Diponegoro ◽  
Mahyudin Ritonga ◽  
Yahya Hairun ◽  
Tedy Machmud ◽  
...  
Keyword(s):  
Autoregressive Model ◽  
Stock Price ◽  
Bernoulli Distribution

scholarly journals Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based

2018 ◽  
Vol 73 ◽  
pp. 13008 ◽  
Author(s):  
Hasbi Yasin ◽  
Budi Warsito ◽  
Rukun Santoso ◽  
Suparti
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Autoregressive Model ◽  
Stock Price ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Vector Autoregressive ◽  
Price Data

Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each number of neurons in hidden layer, the looping process is performed several times in order to get the best result. The best one is chosen by the least of Mean Absolute Percentage Error (MAPE) criteria. In this study, the model is applied in the two series of stock price data from Indonesia Stock Exchange. Evaluation of VAR-NN performance was based on train-validation and test-validation sample approach. Based on the empirical stock price data it can be concluded that VAR-NN yields perfect performance both in in-sample and in out-sample for non-linear function approximation. This is indicated by the MAPE value that is less than 1% .

scholarly journals Correlation Analysis between Stock Price and Accounting Profit Based on a Vector Autoregressive Model

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Yanlin Guo
Keyword(s):  
Statistical Analysis ◽  
Stock Prices ◽  
Autoregressive Model ◽  
Stock Price ◽  
Listed Companies ◽  
Vector Autoregressive Model ◽  
Accounting Earnings ◽  
Vector Autoregressive ◽  
Research Results ◽  
The Relationship

The study of accounting profitability was initiated by the famous American scholars Ball and Brown in the 1960s. In recent years, with the continuous development of market economy, the continuous improvement of the accounting legal system and accounting standards for enterprises has promoted the research on accounting profit in capital market in China. Due to the restriction of some objective conditions, there are not many valuable research results on the relationship between accounting earnings and stock price changes, and the research methods suitable for the study of accounting earnings still need to be explored and summarized. The China Securities Regulatory Commission (CSRC) has required listed companies to publish quarterly financial and accounting reports since 2002, and the condition of using the regression analysis method to study the accounting profit of listed companies is available. In this context, this paper designs a vector autoregressive model to study the correlation between stock price and accounting profit. First, combining the literature and the research results of accounting profit at home and abroad, this paper expounds the statistical analysis of accounting profit. Then, this paper analyzes the accounting profitability of listed companies in China from static and dynamic perspectives. Finally, according to the accounting profit status and profitability statistical analysis of accounting information, accounting profit and growth relationship, and accounting profit information and the relationship between stock prices, this paper is concluded. Also, this paper shows how to improve the profitability of listed companies and how can investors effectively use the accounting earnings information of listed companies for stock investment and put forward corresponding policy suggestions.

scholarly journals Modeling South African Banks closing stock prices: a Markov-Switching Approach

2016 ◽  
Vol 8 (1(J)) ◽  
pp. 36-40
Author(s):  
Diteboho Xaba ◽  
Ntebogang Dinah Moroke ◽  
Johnson Arkaah ◽  
Charlemagne Pooe
Keyword(s):  
Stock Prices ◽  
Autoregressive Model ◽  
Stock Price ◽  
Stock Exchange ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Johannesburg Stock Exchange ◽  
Augmented Dickey Fuller ◽  
National Bank

In this paper, we provide evidence that the five variables used in the study were nonlinear in nature, while finding a better Markov-switching model. The study used dailydata obtained from the Johannesburg Stock Exchange over the period from January 2010 to December 2012. An extension of Markov Switching with autoregressive model was used for empirical analysis. Prior to using this model, the series were tested for nonlinear unit root with modified Kapetanois-Shin-Snell nonlinear Augmented Dickey-Fuller (KSS-NADF) test which successfully provided positive results.Other preliminary tests selected the first lag as optimal and confirmed that stock prices may switch between two regimes. Further empirical findings proved that stock prices can be successfully modelled with Markov Switching Autoregressive model of order one. First National bank was found to have 99.64% longer stock price stability if adjustments regards tofinancialpolicies are made. Capitec Bank was the least favoured among the banks.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

Family Firms and Stock Price Crash Risk

2019 ◽  
Vol 10 (4) ◽  
pp. 77-86
Author(s):  
Hae-Young Ryu ◽  
Soo-Joon Chae
Keyword(s):  
Stock Price ◽  
Family Firms ◽  
Crash Risk

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

Analisis Kinerja Keuangan Perusahaan dengan Menggunakan Pendekatan Economic Value Added (EVA) dan Market Value Added (MVA) (Studi Kasus pada PT Astra International, Tbk. Periode Tahun 20082012)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Hamid
Keyword(s):  
Financial Performance ◽  
Financial Services ◽  
Stock Price ◽  
Economic Value ◽  
Composite Index ◽  
Value Added ◽  
Secondary Data ◽  
Study Approach ◽  
Market Value Added ◽  
The Interest Rate

This study is a qualitative study using a case study approach to the PT. Astra International, Tbk. The object of this research is PT. Astra International, Tbk. PT. Astra International, Tbk is a company engaged in six business sectors, namely: automotive,financial services, heavy equipment, mining and energy, agribusiness, information technology, infrastructure and logistics. Researchers chose PT. Astra International, Tbk as research objects due in the year 2012, PT. Astra International, Tbk managed to rank first in the list of 100 Best Companies to Go Public by the 2011 financial performance of Fortune magazines Indonesia. The data used in this research is secondary data, the financial statements. Astra International, Tbk 20082012. Other secondary data used is the interest rate of Bank Indonesia Certificates (SBI), the Jakarta Composite Index (JCI), and thecompanys stock price began the year 20082012. This study aims to determine the companys financial performance by the use of EVA and MVA approach, therefore the data analysis technique used is the EVA and MVA. Based on the value EVA of the year 2008 2012, PT. Astra International, Tbk has good financial performance that managed to meet the expectations of the company and the investors. Based on the value of MVA during the years 20082012, PT. Astra International, Tbk managed to create wealth and prosperity for companies and investors. It concluded that financial performance. AstraInternational, Tbk for five years was satisfactory.

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