scholarly journals Validating The Assumptions of Residuals in ARIMA Model for Daily Stock Price Data By using R

2021 ◽  
Vol 9 (11) ◽  
pp. 971-978
Author(s):  
Akasam Srinivasulu
Keyword(s):  
Correlation Function ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Arima Model ◽  
The Past ◽  
Price Data ◽  
Auto Correlation ◽  
Auto Correlation Function ◽  
Auto Regressive

Abstract: Identifying the past data and plannig for future is very important for every organization . Now a days Stock market playes a major role for the development of economy. For the countries economic development, stock market plays a vital role. For this modelling, forecasting is the best way to know the future stock prices based on the past stock prices data. In stock price data, forecasting of closed price plays a major role in financing economic decisions. The Arima model has developed and implemented in many applications .So the researchers utilize arima model in forecasting the closed prices of AMAZON stock price data for future which have been collected from AMAZON 2007-01-03, to 2020-10-12.In this paper the researcher aim is to forecast by using the ARIMA time series model with particular reference to Box and Jenkins approach on daily stock prices of AMAZON With open statistical software R. The validity of ARIMA model is tested by using the standard statistical tests. Keywords: Auto Regressive Integrated Moving Average, Auto Correlation Function, Partial Auto Correlation Function, Akaikae Information Criterion, Auto Regressive Conditional Heteroscedasticity

scholarly journals Comparing Two ARIMA Models for Daily Stock Price Data

2021 ◽  
Vol 9 (11) ◽  
pp. 1606-1610
Author(s):  
M. V. Narayana Murthi
Keyword(s):  
Correlation Function ◽  
Stock Market ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Price Data ◽  
Forecasting Methods ◽  
Auto Correlation ◽  
Auto Correlation Function

Abstract: Analyzing the past data and planning for future is very important for every public and private organizational decisions. Now a days individuals also using forecasting methods to invest in Stock market. Investments in mutual funds and in registered companies in companies in stock market is the order of the day. In this paper, advanced forecasting methods are fitted to the time related stock price data to study its effectiveness in forecasting future events. Auto correlation and standard models have been analyzed before fitting this model to the above data. The forecasting can be done by using the ARIMA time series(using auto. arima) model. A particular reference have been made to Box and Jenkins approach for day to day stock price data values of Exxon Mobile Corporation from '1995-01-01 to 2020-03-01. With usual statistical software R. Here, ARIMA(1,1,1,) is fitted to this data, These results are compared with the model ARIMA(1,1,1,) by using accuracy measures. Keywords: ARIMA: Auto Regressive Integrated Moving Average ACF: Auto Correlation Function PACF: Partial Auto Correlation Function AIC: Akaikae Information Criterion RMSE: Root mean square error XOM: Exxon Mobil Corporation

Studies on the Price Analysis and Forecasting of Gram Crop in Bhiwani District of Haryana

2020 ◽  
pp. 239-245
Keyword(s):  
Correlation Function ◽  
Lower Limit ◽  
Moving Average ◽  
Arima Model ◽  
Statistical Techniques ◽  
Regulated Markets ◽  
Auto Correlation ◽  
Auto Correlation Function ◽  
Auto Regressive ◽  
Price Analysis

The present study was conducted in Bhiwani district, Siwani and Tosham blocks of Haryana which was selected purposively on the basis of maximum production under gram crop. Further, four regulated markets (Siwani, Dadri, Tosham and Bhiwani) from the Bhiwani district were purposively selected. Average prices in Haryana, data for the period of 2005 to 2016 were analyzed the time series methods. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were calculated for the data. Appropriate Box-Jenkins Auto-Regressive Integrated Moving Average (ARIMA) model was fitted. The validity of the model was tested using standard statistical techniques. ARIMA (0, 1, 1) and ARIMA (1, 1, 3) model and used to forecast average prices in Bhiwani for one leading year. The results showed that the average prices forecast for 2017 to be about `4769 per quintal with upper and lower limit `4769 to 4604 per quintal in the Siwani market, `4719 per quintal with upper and lower limit`4719 to 4622 per quintal in Dadri market, `4766 per quintal with upper and lower limit `4766 to4639 per quintal in the Tosham market and `4798 per quintal with upper and lower limit `4798 to 4648 per quintal in the Bhiwani market, respectively.

scholarly journals FUNDAMENTAL AND TECHNICAL ANALYSIS OF THE STOCK PRICE

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Denis Spahija ◽  
Seadin Xhaferi
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Full Time ◽  
Equal Opportunities ◽  
Factors Affecting ◽  
The Past ◽  
Real Value

Trading with stocks in developed market conditions for some is fun, for others it is a way to preserve the real value of the asset, while for the most is a challenge to gain bigger profits quickly and easily. Dreams on stock market alchemy rely on the development and upgrading of special systems whose ultimate goal is to uncover stock price secrets and their changes. What are the chances of this happening? Chances are minimal, according to experiences from the world’s leading stock exchanges in the past. The stock market complexity, the number and unpredictability of factors affecting stock prices and unexpected changes or stability do not give much hope to those who know what’s going to happen in the future. In such endeavors there are equal opportunities for both stock exchange experts and full-time amateurs. For all this, if the stock market cannot be defeated or deceived, then it is better to join it. So this means: to create a diversified portfolio of securities that provides a safe income, slightly higher than annual inflation, minimizing the risk.

scholarly journals Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

2021 ◽  
Author(s):  
Yigit Alparslan ◽  
Edward Kim
Keyword(s):  
Computer Vision ◽  
Stock Market ◽  
Current Literature ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Market Prediction ◽  
Convolution Operation ◽  
Price Data ◽  
Dollar Amount

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

scholarly journals Was There a Bubble in the 1929 Stock Market?

1993 ◽  
Vol 53 (3) ◽  
pp. 549-574 ◽  
Author(s):  
Peter Rappoport ◽  
Eugene N. White
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Price Behavior ◽  
The Past ◽  
Cointegration Tests ◽  
The Future ◽  
Historical Accounts

In contrast to historical accounts of the boom and crash of the 1929 stock market, recent econometric studies have concluded that there were no bubbles in the American stock market over the past one hundred years. Examining the pricing of loans to stock brokers, we find information on the lenders' perceptions of the future course of stock prices in 1929. From this market, we extract an estimate of the bubble in stock prices. This bubble component contributes significantly to explain stock price behavior, even though standard cointegration tests suggest that there was no bubble in the market.

scholarly journals Growth Status and ARIMA Model Forecast of Area, Production of Wheat Crop in India

2020 ◽  
pp. 117-125
Author(s):  
Jhade Sunil ◽  
Dagam Sindhu
Keyword(s):  
Growth Rate ◽  
Correlation Function ◽  
Arima Model ◽  
Linear Growth Rate ◽  
Wheat Crop ◽  
Production Forecast ◽  
Growth Status ◽  
Upper Limit ◽  
Auto Correlation ◽  
Auto Correlation Function

This research is a study of growth status and forecasting area, production of wheat India. Data for the period of 1949-50 to 2017- 18 were analyzed by time series methods. 1949-50 to 2014-15 was used for the model building and forecasting. The data of 2015-16 to 2017-18 was used for validation of the model. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were calculated for the data. Appropriate Box-Jenkins Auto Regressive Integrated Moving Average (ARIMA) model was fitted. Validity of the model was tested using standard statistical techniques. The performance of model was validated by comparing with percentage deviation from the values and mean absolute percent error (MAPE). For forecasting area, production ARIMA (0, 1, 1) and (0, 1, 1) model respectively were used to forecast few leading years. The results also show area forecast for the year 2021 to be about 28276.63 thousand hectares with lower and 34138.09 thousand hectares upper limit respectively, production forecast to be about 94087.32 thousand tonnes with lower limit and 114388.9 thousand tonnes of upper limit respectively. The growth pattern was examined by fitting an exponential function, linear function. The result showed that the linear growth rate compound growth rate is a positive significant trend for area, production. Production shows double of area in the case of compound growth function for the study periods.

scholarly journals Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

2021 ◽  
Author(s):  
Yigit Alparslan ◽  
Ethan Moyer ◽  
Edward Kim
Keyword(s):  
Computer Vision ◽  
Stock Market ◽  
Current Literature ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Market Prediction ◽  
Convolution Operation ◽  
Price Data ◽  
Dollar Amount

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

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