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

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 Contemporaneous Relationship between Trading Volume and Stock Returns Volatility : Evidence from Nepalese Stock Market

2017 ◽  
Vol 10 (1) ◽  
pp. 40-63
Author(s):  
Shivaram Shrestha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Stock Return ◽  
Efficient Market Hypothesis ◽  
Least Square ◽  
Return Volatility ◽  
Monthly Data ◽  
Stock Return Volatility ◽  
Mixture Of Distribution Hypothesis

This paper examines the contemporaneous relation between trading volume and stock returns volatility for Nepalese stock market using monthly data for the period 2005 mid-July to 2017 mid-April. The study uses ordinary least square method and analyzes whether rising price leads to higher volume or vice versa. The study also investigates the association between trading volume and stock returns volatility based on monthly data of NEPSE index and examines the effects of trading volume on stock returns volatility using GARCH (1, 1) model. The study finds positive contemporaneous relationship between trading volume and stock return volatility. The study result indicates that the relationship between trading volume and return volatility is asymmetric. The findings strongly support the hypothesis that higher trading volume is associated with an increase in stock return volatility, but offers little support to the sequential arrival hypothesis and the mixture of distribution hypothesis. Finally, the findings support the weak-form efficient market hypothesis in Nepalese stock market.

scholarly journals Exchange Rate, Gold Price, and Stock Market Nexus: A Quantile Regression Approach

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 86
Author(s):  
Rizwan Ali ◽  
Inayat Ullah Mangla ◽  
Ramiz Ur Rehman ◽  
Wuzhao Xue ◽  
Muhammad Akram Naseem ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Emerging Market ◽  
Empirical Relationship ◽  
Gold Price ◽  
Stock Market Performance ◽  
Regression Approach ◽  
The Exchange Rate

In this study, we examine an empirical relationship between stock market volatility with the exchange rate and gold prices of an emerging market, “Pakistan”, employing daily and monthly data (PSX-100 Index) covering from 2001: Q3 to 2018: Q2. The study explains the average stock returns by applying MGARCH. Further, it investigates that the volatility in the exchange rate (Rs/US $) and gold prices remain equally strong in bearish and bullish conditions of the stock market by using a quantile regression approach (2001–2018). Additionally, the sample period is divided into two split samples that cover (2001–2007) and (2008–2018) respectively, based on global financial crises and applied similar analysis. The overall results show the negative impact of the exchange rate and gold price volatility on the stock market performance daily (monthly), supporting the argument that the stock market considers the exchange rate and gold price fluctuations as an adverse indicator and reacts negatively.

Short-Selling and Behavioral Bias in Korean Stock Market

2017 ◽  
Vol 25 (2) ◽  
pp. 255-278
Author(s):  
Sang Buhm Hahn
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Cloud Cover ◽  
Short Selling ◽  
Weather Factors ◽  
Behavioral Bias ◽  
Short Sale ◽  
Sunshine Hours ◽  
Sale Volume

This study investigates whether or not the short-selling behavioral bias of investors exists in the Korean stock market. We analyze how the weather bias related to climate factors affects short-selling traders, commonly known as informed traders. To do this we estimated the dynamic panel model using daily data and examined the relationship between market variables such as stock returns, short sale volume, non-short sale volume, total trading volume, and weather variables consisting of cloud cover and sunshine hours. This study shows that not only returns but also short selling volumes are all affected by weather factors. In the case of stock returns, both cloud cover and sunshine hours have a statistically significant impact on returns, and its sign is estimated to be inversely proportional to both factors. That is, we find that returns decrease on cloud days, but increase on sunny days. In terms of the trading behavior of the market participants, it is interesting to note that the trading volume decreases when the weather is blunted, But did not show any statistical significances. On the other hand, both the original and the seasonally adjusted weather factors of cloud cover have a statistically significant positive effect on the short-sale volume. This means that as the weather worsens, short-selling traders submit more orders, indicating the presence of behavioral bias.

The linear and non-linear dependence of stock returns and trading volume in the Finnish stock market

1994 ◽  
Vol 4 (2) ◽  
pp. 159-169 ◽  
Author(s):  
Teppo Martikainen ◽  
Vesa Puttonen ◽  
Martti Luoma ◽  
Timo Rothovius
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Linear Dependence ◽  
Trading Volume ◽  
Non Linear

scholarly journals THE VOLATILITY TRANSMISSION OF MAIN GLOBAL STOCK'S RETURN TO INDONESIA

2017 ◽  
Vol 20 (2) ◽  
pp. 229-256
Author(s):  
Linda Karlina Sari ◽  
Noer Azam Achsani ◽  
Bagus Sartono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Stock Return ◽  
Global Financial Crisis ◽  
Symmetric Model ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Volatility Transmission ◽  
The Impact

Stock return volatility is a very interesting phenomenon because of its impact on global financial markets. For instance, an adverse shocks in one country’s market can be transmitted to other countries’ market through a particular mechanism of transmission, causing the related markets to experience financial instability as well (Liu et al., 1998). This paper aims to determine the best model to describe the volatility of stock returns, to identify asymmetric effect of such volatility, as well as to explore the transmission of stocks return volatilities in seven countries to Indonesia’s stock market over the period 1990-2016, on a daily basis. Modeling of stock return volatility uses symmetric and asymmetric GARCH, while analysis of stock return volatility transmission utilizes Vector Autoregressive system. This study found that the asymmetric model of GARCH, resulted from fitting the right model for all seven stock markets, provides a better estimation in portraying stock return volatility than symmetric model. Moreover, the model can reveal the presence of asymmetric effects on those seven stock markets. Other finding shows that Hong Kong and Singapore markets play dominant roles in influencing volatility return of Indonesia’s stock market. In addition, the degree of interdependence between Indonesia’s and foreign stock market increased substantially after the 2007 global financial crisis, as indicated by a drastic increase of the impact of stock return volatilities in the US and UK market on the volatility of Indonesia’s stock return.

scholarly journals Previsibilidade de Retorno das Ações na Bovespa: Um Teste Envolvendo o Modelo de Fator de Retorno Esperado

2004 ◽  
Vol 2 (2) ◽  
pp. 183
Author(s):  
Luciano Martin Rostagno ◽  
Gilberto De Oliveira Kloeckner ◽  
João Luiz Becker
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Factor Model ◽  
Explanatory Power ◽  
Return Predictability ◽  
Expected Return ◽  
Cross Sectional ◽  
Theoretical Assumptions ◽  
History Of

This paper examines the hypothesis of asst return predictability in the Brazilian Stock Market (Bovespa). Evidence suggests that seven factors explain most of the monthly differential returns of the stocks included in the sample. Within the factors that present statistically significant mean, two are liquidity factors (market capitalization and trading volume trend), three refer to price level of stocks (dividend to price, dividend to price trend, and cash flow to price), and two relate to price history of stocks (3 and 12 months excess return). Contradicting theoretical assumptions, risk factors present no explanatory power on cross-sectional returns. Using an expected return factor model, it is contended that stock returns are quite predictable. An investment simulation shows that the model is able to assemble portfolios with statistically significant higher returns. Additional tests indicate that the winner portfolios are not fundamentally riskier suggesting mispricing of assets in the Brazilian stock Market.

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