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 Does Macroeconomic Indicators Influence Stock Price Behavior? Evidence from Nigerian Stock Market

2019 ◽  
Vol 6 (2) ◽  
pp. 26
Author(s):  
Peter Ego Ayunku
Keyword(s):  
Stock Market ◽  
Significant Influence ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Price Behavior ◽  
Market Growth ◽  
Integration Test

This paper investigate whether macroeconomics indicators influences stock price behavior in Nigerian stock market, using an annual time series data spanning from 1985-2015. The study employed some econometric tools such as Augmented Dicker Fuller (ADF) Unit Root test, Johansen’s co integration test, Vector Error Correction Model (VECM) to analyze the variables of interest. The study found out that Money Supply (MS) has an inverse but statistically significant  influence on stock prices in Nigerian stock market also Treasury Bill Rate (TBR) has an inverse and statistically insignificant influence on stock market prices. While on the other hand, Market Capitalization (MCAP) has a positive and statistically significant influence on stock prices while Exchange Rate (EXR) has positive but statistically insignificant relationship with stock prices in the Nigerian Stock Market. In view of the above, the study recommends amongst others that monetary authorities should try as much as possible to implement sound macroeconomic policies that would enhance stock market growth and development in Nigeria. 

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 Daily Stock Price Behavior of Commercial Banks in Nepal

2007 ◽  
Vol 3 (1) ◽  
pp. 100-110 ◽  
Author(s):  
Keshar J. Baral ◽  
Surya Kumar Shrestha
Keyword(s):  
Stock Prices ◽  
Commercial Banks ◽  
Stock Price ◽  
Serial Correlation ◽  
Fiscal Year ◽  
Price Behavior ◽  
Data Set ◽  
Business Studies ◽  
Random Walk Hypothesis ◽  
The Past

Using the data set on daily stock prices during the fiscal year 2005/06 (July 16, 2005 through July 16, 2006), this paper attempts to analyze the stock price behavior of commercial banks in Nepalese markets. The results of serial correlation and run tests conclude that the proposition of Random Walk Hypothesis (RWH) in Nepalese stock markets does not hold true. This conclusion corroborates with the conclusions of the past studies carried out in Nepalese context.Journal of Nepalese Business Studies 2006/III/1 pp. 100-110

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 Prediction of Stock Price using RNN’s LSTM-Based Deep Learning Model

2021 ◽  
Vol 9 (8) ◽  
pp. 2469-2474
Author(s):  
Asmita Pandey
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Recurrent Neural Network ◽  
Stock Price ◽  
Listed Companies ◽  
Stock Market Prediction ◽  
The Past ◽  
The Future

Abstract: Stock Market is referred to as a trading platform where trading of listed companies share price is exchanged. It is a place where individuals can buy or sell shares of the publicly listed companies. The prediction of stock market that how it will perform, its movement is one of the challenging tasks to do. Stock market prediction involves determining the future movement of the stock value of a financial exchange. In this paper the prediction of the stock prices using deep learning's LSTM (Long Short-Term Memory) which is the extension of Recurrent Neural Network is done. The previous two years historical dataset from 31/7/2019 to 13/8/2021 is taken for the prediction purpose. The prediction is based on the time series analysis of data, since it can help us to get an idea of the stock price pattern and also it is considered to be the best tool for understanding the pattern of the previously observed values and make the predictions based on it. For a greater accuracy of the predictions, we should consider past happenings or events as the past affects the future. Since for stock market prediction the data will be in time series and LSTM performs well when the information or the data is of the past and the prediction is to be made for the future then we can say that LSTMs are quite capable of doing the prediction for the stock market values. Keywords: Stock Market, prediction, LSTM, Recurrent Neural Network, time series analysis

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.

scholarly journals The Impact of COVID-19 on the Stock Price of Socially Responsible Enterprises: An Empirical Study in Taiwan Stock Market

2021 ◽  
Vol 18 (4) ◽  
pp. 1398
Author(s):  
Kuo-Jung Lee ◽  
Su-Lien Lu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Recovery Phase ◽  
Socially Responsible ◽  
Cumulative Impact ◽  
Study Approach ◽  
Price Performance ◽  
The Impact

This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

EVOLUTION OF SHANGHAI STOCK MARKET BASED ON MAXIMAL SPANNING TREES

2012 ◽  
Vol 27 (03) ◽  
pp. 1350022 ◽  
Author(s):  
CHUNXIA YANG ◽  
YING SHEN ◽  
BINGYING XIA
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Spanning Tree ◽  
Stock Price ◽  
Spanning Trees ◽  
Turning Points ◽  
Single Step ◽  
P Value ◽  
Power Law Distribution ◽  
Structure Variation

In this paper, using a moving window to scan through every stock price time series over a period from 2 January 2001 to 11 March 2011 and mutual information to measure the statistical interdependence between stock prices, we construct a corresponding weighted network for 501 Shanghai stocks in every given window. Next, we extract its maximal spanning tree and understand the structure variation of Shanghai stock market by analyzing the average path length, the influence of the center node and the p-value for every maximal spanning tree. A further analysis of the structure properties of maximal spanning trees over different periods of Shanghai stock market is carried out. All the obtained results indicate that the periods around 8 August 2005, 17 October 2007 and 25 December 2008 are turning points of Shanghai stock market, at turning points, the topology structure of the maximal spanning tree changes obviously: the degree of separation between nodes increases; the structure becomes looser; the influence of the center node gets smaller, and the degree distribution of the maximal spanning tree is no longer a power-law distribution. Lastly, we give an analysis of the variations of the single-step and multi-step survival ratios for all maximal spanning trees and find that two stocks are closely bonded and hard to be broken in a short term, on the contrary, no pair of stocks remains closely bonded for a long time.

Value and Growth Stock Price Behavior during Stock Market Declines

2017 ◽  
Vol 26 (4) ◽  
pp. 41-52 ◽  
Author(s):  
Daniel Folkinshteyn ◽  
Gulser Meric ◽  
Ilhan Meric
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Price Behavior
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