scholarly journals The dynamic relationship between stock returns and trading volumes in Nepalese stock market

2020 ◽  
Vol 23 (2) ◽  
pp. 161-172
Author(s):  
Prem Lal Adhikari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Time Series Data ◽  
Stock Exchange ◽  
Causal Effect ◽  
Empirical Relationship ◽  
Insurance Companies ◽  
Series Data ◽  
Dynamic Relationship

 In finance, the relationship between stock returns and trading volume has been the subject of extensive research over the past years. The main motivation for these studies is the central role that trading volume plays in the pricing of financial assets when new information comes in. As being interrelated and interdependent subjects, a study regarding the trading volume and stock returns seem to be vital. It is a well-researched area in developed markets. However, very few pieces of literature are available regarding the Nepalese stock market that explores the association between trading volume and stock return. Realizing this fact, this paper aims to examine the empirical relationship between trading volume and stock returns in the Nepalese stock market using time series data. The study sample is comprised of 49 stocks traded on the Nepal Stock Exchange (NEPSE) from mid-July 2011 to mid-July 2018. This study examines the Granger Causality relationship between stock returns and trading volume using the bivariate VAR model used by de Medeiros and Van Doornik (2008). The study found that the overall Nepalese stock market does not have a causal relationship between trading volume and return on the stock. In the case of sector-wise study, there is a unidirectional causality running from trading volume to stock returns in commercial banks and stock returns to trading volume in finance companies, hydropower companies, and insurance companies. There is no indication of any causal effect in the development bank, hotel, and other sectors. This study also finds that there is no evidence of bidirectional causality relationships in any sector of the Nepalese stock market.

scholarly journals The Determinant of Indonesian Stock Returns’ Volatility: Evidence from Islamic and Conventional Stock Market

2021 ◽  
Vol 6 (3) ◽  
pp. 277-296
Author(s):  
Septiana Indarwati ◽  
Agus Widarjono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Money Supply ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Distributed Lag ◽  
Macroeconomic Indicators

Islamic stock market is apparently different from the conventional stock market due to the prohibition of unlawful goods and excessive risk-taking behavior. This study explores the extent to which the Indonesian Islamic and conventional stock returns' volatility responds to the macroeconomic indicators. This study employs Jakarta Islamic Index (JII) and Indonesian Stock Exchange (IDX) and uses monthly time-series data covering 2001: M1 - 2019: M12. The volatility of stock returns is measured using Generalized Autoregressive Conditional Heteroskedasticity (GARCH). By employing the Autoregressive Distributed Lag Model (ARDL), the results validate the evidence of the long-run relationship between the stock market's volatility and macroeconomic variables. A rising in money supply and an economic upturn reduce the volatility of conventional stock returns but only an expansionary money supply diminishes the volatility of Islamic stock returns. Conversely, high inflation and sharp depreciation of the Rupiah boost the stock returns' volatility. The results further show an interesting finding that the Islamic stock market's volatility is more responsive to changes in macroeconomic indicators than the volatility of their counterpart conventional stock market. Policymakers should take strict rules during the worst economic conditions to minimize the negative impact of the instability of macroeconomic variables.

scholarly journals Stock Returns And Volatility İn An Emerging Equity Market. Evidence From Kenya

2016 ◽  
Vol 12 (4) ◽  
pp. 79 ◽  
Author(s):  
David Ndwiga ◽  
Peter W Muriu
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Significant Risk ◽  
Series Data ◽  
Volatility Clustering ◽  
Cross Listing ◽  
Daily Returns

This study investigates volatility pattern of Kenyan stock market based on time series data which consists of daily closing prices of NSE Index for the period 2ndJanuary 2001 to 31st December 2014. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. The study provides evidence for the existence of a positive and significant risk premium. Moreover, volatility shocks on daily returns at the stock market are transitory. We do not find any significant leverage effect. Introduction of the new regulations on foreign investors with a 25% minimum reserve of the issued share capital going to local investors (in 2002), introduction of live trading, cross listing in Uganda and Tanzania stock exchange (in 2006) and change in equity settlement cycle from T+4 to T+3 (in 2011) significantly reduce volatility clustering. The onset of US tapering increase the daily mean returns significantly while reducing conditional volatility.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

scholarly journals Does Equity Market Integration Exist Between Turkey and the Eurozone?

JEJAK ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 29-48
Author(s):  
Berto Usman ◽  
Nega Muhabaw Kassie ◽  
Fitra Wahyudi
Keyword(s):  
Market Integration ◽  
Time Series Data ◽  
Stock Exchange ◽  
Equity Market ◽  
Series Data ◽  
Causality Test ◽  
Stock Market Integration ◽  
Vector Autoregression Model ◽  
Dynamic Relationship ◽  
Residual Matrix

This research investigates the existence of stock market integration between Turkey and the Eurozone. In this study, the performance of Turkey’s stock exchange is proxied by the BIST100, and the EURO STOXX50 is employed as a proxy for the Eurozone index. We hypothesize that there is a dynamic relationship between Turkey and the Eurozone. Methodologically, our research was conducted by employing monthly time series data obtained from EIKON datastream International. In order to demonstrate the extent of equity market integration between Turkey and Eurozone, a vector autoregression model (VAR) was utilized. According to the results, there is no co-integration between these two equity markets. This is in line with the output of residual matrix test, where the correlation between these two market indices was found to be low. However, a Granger causality test indicated that there was a low one-way contribution from Turkey to the Eurozone index during the observation period.

scholarly journals Portfolio Learning Based on Deep Learning

2020 ◽  
Vol 12 (11) ◽  
pp. 202
Author(s):  
Wei Pan ◽  
Jide Li ◽  
Xiaoqiang Li
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Composite Index ◽  
Series Data ◽  
High Dimensional ◽  
Chinese Stock Market ◽  
High Return ◽  
Stock Information

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

scholarly journals Anticipated Volatility, Unanticipated Volatility Shocks, Excess Monthly Returns and Stock Market Behaviour of Pakistan

2021 ◽  
Vol 7 (1) ◽  
pp. 77-91
Author(s):  
Muhammad Ramzan Sheikh ◽  
Sahrish Zameer ◽  
Sulaman Hafeez Siddiqui
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Portfolio Choice ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Financial Assets ◽  
Exchange Market ◽  
Risk Return

An investor considers various factors to choose the financial assets. The portfolio theory suggests that risk, return, taxes, information and liquidity are vital factors in portfolio choice. The study is based on risk premium, uncertainty, shocks and volatility of Pakistan stock exchange market. The study has used monthly time series data of returns of ten sectors of Pakistan stock market ranging from 2006 to 2014 to measure the anticipated and unanticipated factors of risk, return and uncertainty. Using CAPM, it is pointed out that volatility factor is present and high in overall stock market and the level of volatility in different sectors of the market moves in the same direction which suggest that speculative activities are widely spread in every sector and in overall market as well.

scholarly journals The Empirical Relationship Between Stock Returns, Return Volatility and Trading Volume in the Brazilian Stock Market

2008 ◽  
Vol 5 (1) ◽  
pp. 01-17 ◽  
Author(s):  
Otavio Ribeiro de Medeiros ◽  
Bernardus Ferdinandus Nazar Van Doornik
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Empirical Relationship ◽  
Return Volatility

Bull and Bear Phases: An Empirical Perusal of Indian Stock Market NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Sunaina Kanojia ◽  
Neha Arora
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Time Series Data ◽  
Series Data ◽  
Bear Market ◽  
Bull Markets ◽  
Indian Stock Market ◽  
Increases In Risk ◽  
Considerable Support

In general, any one known to stock market is acquainted with the phenomenon of bull and bear phases, but whether the traders or investors put air to these phases while making a decision to buy, sell, or stay invested. The present paper attempts to identify and analyze the two most popular market phases, i.e. bull and bear, for better investment decisions with the use of Bry and Boschan Algorithm and time series data. Further, it seeks to analyze the distributional characteristics of the variances in stock returns and search evidence of asymmetries, if any, in volatility under different market conditions which may help to shed light on the bull and bear phases of Indian equity market. The study arrange for evidence that in bull markets, stock prices run far ahead of earnings and for fairly long periods of time. The paper indicates 12 bull and bear phases in the Sensex and Nifty during the sample period of 19 years with the associated factors responsible for the shift of bull and bear market phases. The results provide considerable support for the view that markets choose to ignore adverse possibilities and react with zest to favorable possibilities and market declines can partly be explained by increases in risk.

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