scholarly journals An Exotic Long-Term Pattern in Stock Price Dynamics

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e51666 ◽  
Author(s):  
Jianrong Wei ◽  
Jiping Huang
Keyword(s):  
Stock Price ◽  
Price Dynamics

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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 Is Establishing a New Partnership Useful Information for Investors in the Smartphone Supply Market?

2021 ◽  
Vol 13 (10) ◽  
pp. 5573
Author(s):  
Insung Son ◽  
Sihyun Kim
Keyword(s):  
South Korea ◽  
Stock Price ◽  
Individual Investors ◽  
Trading Patterns ◽  
Market Impacts ◽  
The Us ◽  
Signaling Effect ◽  
Parts Suppliers ◽  
Country Specific

This study analyzed partner volatility (new, old, revocation partners) and country-specific signal effects (United States (US), Taiwan, Japan, and South Korea) for Apple iPhone parts suppliers from 2007 to 2018. Mid- to long-term stock price movements were also analyzed to define trading patterns by investor type. The results using logit regression analysis revealed that new partners and revocation partners each have a signaling effect perceived as positive and negative information in the short term, and the excess returns by country showed a positive signaling effect in the order of the US, Taiwan, South Korea, and Japan. The findings also suggest that the change in the new partners’ stock price after the preannouncement of new products was useful investment information. Moreover, information asymmetry was found between individual investors, institutions, and foreigners. Results indicate that new partner selection in the smartphone market impacts corporate value and serves as useful investment information.

Long run apartment price dynamics in Swedish and German cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sviatlana Engerstam
Keyword(s):  
Banking Sector ◽  
Cointegration Analysis ◽  
Price Dynamics ◽  
Long Term Effects ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Price Elasticities ◽  
Long Run ◽  
German Cities

PurposeThis study examines the long term effects of macroeconomic fundamentals on apartment price dynamics in major metropolitan areas in Sweden and Germany.Design/methodology/approachThe main approach is panel cointegration analysis that allows to overcome certain data restrictions such as spatial heterogeneity, cross-sectional dependence, and non-stationary, but cointegrated data. The Swedish dataset includes three cities over a period of 23 years, while the German dataset includes seven cities for 29 years. Analysis of apartment price dynamics include population, disposable income, mortgage interest rate, and apartment stock as underlying macroeconomic variables in the model.FindingsThe empirical results indicate that apartment prices react more strongly on changes in fundamental factors in major Swedish cities than in German ones despite quite similar development of these macroeconomic variables in the long run in both countries. On one hand, overreactions in apartment price dynamics might be considered as the evidence of the price bubble building in Sweden. On the other hand, these two countries differ in institutional arrangements of the housing markets, and these differences might contribute to the size of apartment price elasticities from changes in fundamentals. These arrangements include various banking sector policies, such as mortgage financing and valuation approaches, as well as different government regulations of the housing market as, for example, rent control.Originality/valueIn distinction to the previous studies carried out on Swedish and German data for single-family houses, this study focuses on the apartment segment of the market and examines apartment price elasticities from a long term perspective. In addition, the results from this study highlight the differences between the two countries at the city level in an integrated long run equilibrium framework.

scholarly journals Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction

2020 ◽  
Author(s):  
Qianggang Ding ◽  
Sifan Wu ◽  
Hao Sun ◽  
Jiadong Guo ◽  
Jian Guo
Keyword(s):  
Financial Markets ◽  
Stock Price ◽  
Financial Time Series ◽  
Multi Scale ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Movement Prediction ◽  
Novel Approach ◽  
Hierarchical Features

Predicting the price movement of finance securities like stocks is an important but challenging task, due to the uncertainty of financial markets. In this paper, we propose a novel approach based on the Transformer to tackle the stock movement prediction task. Furthermore, we present several enhancements for the proposed basic Transformer. Firstly, we propose a Multi-Scale Gaussian Prior to enhance the locality of Transformer. Secondly, we develop an Orthogonal Regularization to avoid learning redundant heads in the multi-head self-attention mechanism. Thirdly, we design a Trading Gap Splitter for Transformer to learn hierarchical features of high-frequency finance data. Compared with other popular recurrent neural networks such as LSTM, the proposed method has the advantage to mine extremely long-term dependencies from financial time series. Experimental results show our proposed models outperform several competitive methods in stock price prediction tasks for the NASDAQ exchange market and the China A-shares market.

scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

scholarly journals The Effect of Macroeconomic Variables On the Saudi Stock Market

2019 ◽  
Vol 7 (12) ◽  
pp. 126-152
Author(s):  
Amani Mohammed Aldukhail
Keyword(s):  
Stock Market ◽  
Performance Indicators ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Market Value ◽  
Short Term ◽  
Macroeconomic Variables ◽  
Time Deposits

This study aimed at exploring the effect of macroeconomic variables on the activity of the Saudi stock market for the period 1997-2017. Macroeconomic variables were: GDP, interest rate on time deposits, inflation rate. The variables of the Saudi stock market activity were: stock price index, market value of shares, value of traded shares. To achieve this objective, the researcher used the ARDL model for the self-regression of the lagged distributed time gaps. The most important results of the research are: The effect of macroeconomic variables on the performance indicators in the Saudi stock market is not important in the short term and is statistically significant in the long term according to the proposed models, so investors in this market can rely on macroeconomic variables in Predict the movement of the stock market and predict long-term profits and losses.

scholarly journals ANALISIS HARGA SAHAM INDEX KOMPAS 100 DENGAN PENDEKATAN ERROR CORRECTION MODEL

2021 ◽  
Vol 4 (1) ◽  
pp. 406-414
Author(s):  
Amir Hamzah
Keyword(s):  
Error Correction ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Financial Statements ◽  
Error Correction Model ◽  
Short Term ◽  
Correction Model ◽  
Positive Effect

The purpose of this research is to analyze the short term and long term relationship between ROI, EPS, PER ,inflation, SBI, exchange rate,and GDP on Stock Price. The data in this research is company financial statements which included Compas 100 Index on the Indonesia Stock Exchange. statistical analysis in this research used stasionarity test, The Classical Assumptions Test, Cointegration Test, Error Correction Model Test. This research found that partially ROI, EPS, PER variables a positive effect on stock prices in the short term and long term, KURS and SBI a positive effect on stock prices in the short term, but there is no effect in the long term, inflation and GDP do not affect the stock price both in the short term and long term. Simultaneously affected the stock prices significantly affect on stock price both in the short term and long term.

Sign in / Sign up

Export Citation Format

Share Document