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.

scholarly journals Short term stock price forecasting with application of neural network

2021 ◽  
Author(s):  
Jiayin Liu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Price ◽  
Principal Component ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Price Forecast ◽  
Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

scholarly journals PENGARUH MAKROPRUDENSIAL DAN MIKROPRUDENSIAL TERHADAP HARGA SAHAM

El Dinar ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 130-146
Author(s):  
Oktoviana Banda Saputri
Keyword(s):  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Secondary Data ◽  
Economic Life ◽  
Islamic Banking ◽  
Arch Model ◽  
Short Term ◽  
Time Series Regression ◽  
Stock Price Index

The existence of Islamic banking in Indonesia still not as advanced the spirit of Islamic economic life significantly. Based on Indonesia Stock Exchange data, PT Bank Panin Dubai Syariah, Tbk (PNBS) has the first Islamic banking to register on the stock exchange on January 15, 2014. This reasearch’s aim to analyze indicators that have a greater effect on changes in stock price index . There are six independent variables that are predicted to affect the PNBS stock price. Based on the macro indicators of inflation, exchange rate and BI 7day repo rate, while micro indicators consist of total assets, DPK growth, and the amount of profit (loss). Data collection using secondary data obtained from several sources. By using time series regression analysis followed by the ARCH model, while in the short term testing using the ECM method. Based on results, the short-term output is closer to theory and hypothesis compared to long-term testing and macroprudential indicators have a greater effect on the PNBS stock price index than macroprudential indicators. This study provides recommendations that improving financial performance is very important for the Islamic banking industry.

scholarly journals Short term stock price forecasting with application of neural network

2021 ◽  
Author(s):  
Jiayin Liu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Price ◽  
Principal Component ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Price Forecast ◽  
Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

scholarly journals Investigating the use of the method of "nearest neighbors" to predict the behavior of stock prices in the implementation of arbitrage

2014 ◽  
Vol 2 (5) ◽  
pp. 20-30
Author(s):  
Perminov G.
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Price Change ◽  
Nearest Neighbors ◽  
Heuristic Rules ◽  
Short Term ◽  
Sharp Rise ◽  
Two Parameters ◽  
Gains And Losses

In this paper, it was considered one of the types of trading in the stock market - Implementation of arbitrage. The aim of the study was to examine the possibility of using the method of "nearest neighbors" with heuristic rules to predict short-term stock price behavior during arbitrage transactions. In a paper tests the hypothesis that the two parameters - TotalRise (percentage price change over the entire period of growth) and LastChange (percentage change in price over the last day) - are crucial for predicting the behavior of stocks after a sharp rise on positive news. Consequently, an investor might assume, how to behave in the price of the shares, if the result will analyze arbitrage other stocks have close to the value of the shares and TotalRise LastChange. For this action, the risk of loss was defined as the ratio of the neighbors with a loss to all of its neighbors (if 20 neighbors of the action 5 neighbors were unprofitable (i.e. the respective shares rose in price), the riskiness of the operation can be set equal to 25%). Win value was defined as the average of the gains (and losses) of all the neighbors. As a result, developed a model is plausible determines the behavior of the shares.

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 Short-term Fluctuation of Work Engagement

2015 ◽  
Vol 23 (2) ◽  
pp. 268
Author(s):  
Xinxin LU ◽  
Yidong TU
Keyword(s):  
Work Engagement ◽  
Short Term ◽  
Term Fluctuation
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