scholarly journals APPLICATION OF SHORT-TERM FORECASTING MODELS FOR ENERGY ENTITY STOCK PRICE (STUDY ON INDIKA ENERGI TBK, JII)

2020 ◽  
Vol 10 (1) ◽  
pp. 294-301
Author(s):  
Rialdi Azhar ◽  
Fajrin Satria Dwi Kesumah ◽  
Ambya Ambya ◽  
Febryan Kusuma Wisnu ◽  
Edwin Russel
Keyword(s):  
Stock Price ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting

scholarly journals Research on Stock Price Forecast Based on ARIMA-GARCH Model

2021 ◽  
Vol 292 ◽  
pp. 02030
Author(s):  
Jie Gao
Keyword(s):  
Stock Price ◽  
Price Change ◽  
Garch Model ◽  
Analysis Data ◽  
Vital Role ◽  
Economic Life ◽  
Short Term ◽  
Price Forecast ◽  
Compound Model ◽  
Term Fluctuation

The stock plays a vital role in economic life, and the economic development of enterprises can be measured by the development and change of stocks. In this paper, the closing price of Ping An stock in China from 2017 to 2019 is selected as the time series empirical analysis data, and the ARIMA-GARCH model is established to predict the law and trend of the stock price change. The results show that the compound model can fit the fluctuation law well, and reasonably predict the short-term fluctuation trend.

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.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

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