scholarly journals Penerapan Model Dynamic Conditional Correlation GARCH Pada Data Saham

2021 ◽  
pp. 80-87
Author(s):  
Ika Fitriana ◽  
Erna Tri Herdiani ◽  
Georgina Maria Tinungki
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Model Equation ◽  
Garch Model ◽  
Time Dependent ◽  
Conditional Correlation ◽  
Stock Volatility ◽  
Dynamic Conditional Correlation ◽  
A Company

Stock is one of the popular financial market instruments. Issuing shares are one of the company's choices when deciding to fund a company. The uncertainty of stock prices in the stock market is an important event to be taken into consideration in making a decision by investors so that a model is needed to describe a stock event. GARCH Dynamic Conditional Correlation (DCC) is a model with a conditional and variance time-dependent that describes the dynamics of stock volatility. This study discusses the DCC GARCH model equation which is applied to the LQ 45 data. The model obtained for BCA shares 𝑸t = +  +  so it can be concluded that DCC GARCH is more appropriate for BCA shares.

scholarly journals Understanding the Interdependency between the Nifty 50 Future Index and the Advanced Future Stock Market through Econometrics

2019 ◽  
Vol 8 (4) ◽  
pp. 3660-3664
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Granger Causality ◽  
Foreign Exchange ◽  
Financial Market ◽  
Stock Prices ◽  
Economic Condition ◽  
The Future ◽  
Bidirectional Movement ◽  
High Degree

In recent times the stock market is accepted as a tool to measure the economic condition of a nation. It is found that the Indian financial market as highly volatile due to the lower value of rupees in foreign exchange with the dollar. This motivated the researchers to measure the interdependencies of [Nifty 50 future (India), Nikkei 225(Japan), NASDAQ 100 Futures (USA), Dow Jones 30 (USA), SSEC (China), Hang Seng Future (Hong Kong), and FTSE 100 (London)]. The analysis covers monthly stock prices for a period of 10years from April 2008 to March 2018. The measurement of interdependencies is studied through granger causality and correlation after the confirmation of the non-normality of data and stationary of data. The result shows a high degree of correlation between NASDAQ and Dow Jones shows 98.76% followed by 96.89% between Nifty 50 future and NASDAQ. The co-movement result of Nifty 50 future through granger causality states Nifty 50 future can explain the future stock market of Nikkei (Japan) and SSEC (China) and the Hang Seng future (Hong Kong) has a bidirectional movement with Nifty 50 futures. The study is useful for the investors to identify the interdependencies of the indices and understand the movement in a significant manner.

scholarly journals Contagion of the shareholder market: cases of Colombia, México, Perú, Chile and Argentina

2019 ◽  
Vol 18 ((1)) ◽  
Author(s):  
Marcos Vera Leyton
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Time Window ◽  
Sum Of Squares ◽  
Conditional Correlation ◽  
Cumulative Sum ◽  
Crisis Period ◽  
Dynamic Conditional Correlation ◽  
Highly Sensitive ◽  
Before And After

This document study the existence of financial crisis contagion, it defined like the transmission of the shocks between countries, which translates in increasing in the correlation anything beyond or fundamental link, taking as a source of contagion by EEUU, Brasil, and analyzing Mexico, Colombia, Peru, Chile and Argentina like “Infected” countries, for the period covered between July 3 of 2001, date of unification of the Colombia Stock Market, to July 3 of 2010. To identify crisis period, and to evoid volatility overestimation, it used the algorithm iterative cumulative sum of squares ICCS, developed by Inclan y Tiao (1994), additionally calculated the dynamic conditional correlation (DCC) Engle Model (2002). The document includes a review of several studies, concepts, and transmission (Contagion) methodologies, and it constitutes one of the few studies that includes Colombia like analysis source.  So this study verifies the existence of contagion in the countries studies, except Argentina, but warns that the measure of impact that a crisis in a given country has over other countries is highly sensitive to the way we choose the time window before and after the crisis.

scholarly journals Stock Price Prediction Using Machine Learning

2021 ◽  
pp. 386-389
Author(s):  
Warade Kalyani Gopal ◽  
Jawale Mamta Pandurang ◽  
Tayade Pratiksha Devaram ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Mathematical Functions ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Future Value ◽  
A Company ◽  
Stock Values

In Stock Market Prediction, the aim is to predict for future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market by training on their previous values. Machine learning itself employs different models to make prediction easier. The paper focuses on Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. In order to predict market movement, the stock prices and stock indicators in addition to the news related to these stocks. Most of the previous work in this industry focused on either classifying the released market news and demonstrating their effect on the stock price or focused on the historical price movement and predicted their future movement. In this work, we propose an automated trading system that integrates mathematical functions, machine learning, and other external factors such as news’ sentiments for the purpose of a better stock prediction accuracy and issuing profitable trades. The aim to determine the price of a certain stock for the coming end-of-day considering the first several trading hours of the day.

scholarly journals A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3094
Author(s):  
Li-Chen Cheng ◽  
Yu-Hsiang Huang ◽  
Ming-Hua Hsieh ◽  
Mu-En Wu
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Trading Volume ◽  
Prediction Models ◽  
Trading Strategies ◽  
Q Learning ◽  
Price Prediction ◽  
Market Trends ◽  
Stock Market Investors

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

scholarly journals Stock Price Prediction Using LSTM on Indian Share Market

10.29007/qgcz ◽  
2019 ◽  
Author(s):  
Achyut Ghosh ◽  
Soumik Bose ◽  
Giridhar Maji ◽  
Narayan Debnath ◽  
Soumya Sen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Investor Sentiment ◽  
Spot Price ◽  
Physical Factors ◽  
Stock Price Prediction ◽  
The Impact ◽  
A Company

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

scholarly journals Optimizing Stock Market Prediction using Long Short Term Memory

2019 ◽  
Vol 9 (1) ◽  
pp. 2819-2823
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Market Price ◽  
Data Set ◽  
Stock Market Prediction ◽  
Company Stock ◽  
Financial Gain ◽  
And Mathematics

Stock market prediction has been an important issue in the field of finance, engineering and mathematics due to its potential financial gain. Stock market prediction is a process of predicting the future value of a company stock or other financial instrument traded in financial market. Stock market prediction brings with it the challenge of proving whether the financial market is predictable or not, since stock market data is of high velocity. This project proposes a machine learning model to predict stock market price based on the data set available by using LSTM model for performing prediction by de-noising the data using wavelet transform and performing auto-encoding on the data. The process includes removal of noise, preprocessing, feature selection, data mining, analysis and derivations. This project focuses mainly on the use of LSTM algorithm along with a layer of neural network to forecast stock prices and allocate stocks to maximize the profit within the risk factor range of the stock buyers and sellers.

scholarly journals Stock Market Anomaly: Day of the Week Effect in Bombay Stock Exchange with the Application of GARCH Model

2020 ◽  
Vol 9 (5) ◽  
pp. 2244-2249
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Exchange ◽  
Garch Model ◽  
Effect Analysis ◽  
Day Of The Week ◽  
Index Value ◽  
The Garch Model ◽  
Daily Returns

It has observed from many stock markets around the world that index value used to vary due to fluctuation in stock prices. One of the most important factors of variation in the stock prices is the day of the week effect, which indicates calendar irregularities in stock markets. Investment in the stock market is the most uncertain; therefore investors get worried regarding the appropriate day to trade in the financial market. The main objective of the present study is to find out the appropriate day of the week effect of developing the stock market of an emergent nation like India from 1st January 2000 to 31st December 2018. For fulfilling the objectives of the study, the daily closing value of four major indices of the Bombay Stock Exchange has been taken into consideration. To test the equality between average returns to different days and to examine the distribution pattern of daily returns series that measure the day of the week analysis, the parametric tools alike Mean and Standard deviation have employed. Apart from the parametric test, t-test has also applied to the daily returns in order to test the hypothesis. In this study, descriptive statistics and the GARCH model has also used with the purpose of measuring the day of the week effect analysis. Conferring to the results, the coefficients express that the return among different days of the week are statistically significant

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