scholarly journals The volatility effect across size buckets: evidence from the Indian stock market

2019 ◽  
Vol 16 (3) ◽  
pp. 62-75 ◽  
Author(s):  
Shilpa Peswani ◽  
Mayank Joshipura
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Significant Risk ◽  
Equity Market ◽  
Sharpe Ratio ◽  
Portfolio Construction ◽  
Market Capitalization ◽  
Value Stocks ◽  
High Volatility ◽  
Indian Stock Market

The portfolio of low-volatility stocks earns high risk-adjusted returns over a full market cycle. The annual alpha spread of low versus high-volatility quintile portfolios is 25.53% in the Indian equity market for the period from January 2000 to September 2018. The low-volatility (LV) effect is not an overlap of other established factors such as size, value or momentum. The effect persists across various size buckets (market capitalization). The performance of the low-volatility effect within various size buckets is analyzed using three different portfolio formation methods. Irrespective of the method of portfolio construction, the low-volatility effect exists and it also generates economically and statistically significant risk-adjusted returns. The long-short portfolios across the study deliver exceptionally high and statistically significant returns accompanied by negative beta. The low-volatility effect is not restricted to small or illiquid stocks. The effect delivers the highest risk-adjusted returns for the portfolio consisting of largecap stocks. Though the returns of the portfolio comprising of large-cap LV stocks are lower than the returns of the portfolio comprising of small-cap LV stocks, its Sharpe ratio is higher because of less risky nature of large-cap stocks as compared to small-cap stocks. The LV portfolio majorly comprises of large-cap, growth and winner stocks. But within size buckets, large-cap and mid-cap low LV picks growth and winner stocks, while small-cap LV picks value stocks.

scholarly journals The Volatility Effect: Evidence from India

2016 ◽  
Vol 5 (1) ◽  
pp. 12 ◽  
Author(s):  
Mayank Joshipura ◽  
Nehal Joshipura
Keyword(s):  
Standard Deviation ◽  
High Risk ◽  
Stock Market ◽  
Empirical Evidence ◽  
Risk Measures ◽  
Benchmark Portfolio ◽  
Beta Effect ◽  
High Volatility ◽  
The Difference ◽  
Indian Stock Market

We offer empirical evidence that stocks with low volatility earn higher risk-adjusted returns compared to high volatility stocks in the Indian stock market. The annualised excess returns for the low and high volatility decile portfolios amount to 11.40% and 1.30%, respectively, over the period January 2001 to June 2015. The difference of returns is statistically and economically significant for both low and high-risk stocks. Using risk measures of standard deviation and beta, the volatility effect remains after controlling for size, value and momentum. We uncover that the volatility effect is not statistically significant after controlling for beta effect. Our evidence for volatility effect is not dominated by small and illiquid stocks. Our results show that the low volatility portfolio outperforms benchmark portfolio not only in down market but also in up market conditions.

Risk-Managed Momentum: An Evidence from Indian Stock Market

2021 ◽  
pp. 231971452110230
Author(s):  
Simarjeet Singh ◽  
Nidhi Walia ◽  
Pradiptarathi Panda ◽  
Sanjay Gupta
Keyword(s):  
Stock Market ◽  
Asset Management ◽  
Stock Exchange ◽  
Sharpe Ratio ◽  
Relative Momentum ◽  
Extended Time ◽  
Momentum Strategies ◽  
Momentum Profits ◽  
Indian Stock Market ◽  
Time Frames

Relative momentum strategies yield large and substantial profits in the Indian Stock Market. Nevertheless, relative momentum profits are negatively skewed and prone to occasional severe losses. By taking into consideration 450 stocks listed on the Bombay Stock Exchange, the present study predicts the timing of these huge momentum losses and proposes a simple risk-managed momentum approach to avoid these losses. The proposed risk-managed momentum approach not only doubles the adjusted Sharpe ratio but also results in significant improvements in downside risks. In contrast to relative momentum payoffs, risk-managed momentum payoffs remain substantial even in extended time frames. The study’s findings are particularly relevant for asset management companies, fund houses and financial academicians working in the area of asset anomalies.

scholarly journals RISK-RETURN TRADEOFFS IN INDIAN STOCK MARKET: EVIDENCE FROM GARCH MODEL APPROACH

2021 ◽  
pp. 556-566
Author(s):  
Riteshbhai Patel
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Garch Model ◽  
Sample Period ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Daily Return ◽  
Risk Return ◽  
Indian Stock Market ◽  
Model Approach

The objective is to examine the risk-return tradeoff in the Indian stock market. The sample period of study is from January 4, 2000 to December 31, 2020. The empirical results shows existence of risk-return tradeoff in the BSE. A positive risk-return tradeoff is found for monthly & annual return series. The market has weak risk-return relationship in daily return series. The CGARCH (1,1) captures the asymmetric volatility effect for all the different frequency based returns. The study has implications for the investors. The riskreturn relationship is stronger and significant in longer duration of investment. The market gives higher return when there is a high risk.

scholarly journals Volatility Clustering at a Sector Level in the Chinese Equity Market

2018 ◽  
Vol 9 (3) ◽  
pp. 103
Author(s):  
Gerardo “Gerry” Alfonso Perez
Keyword(s):  
Stock Market ◽  
Statistical Tests ◽  
A Priori ◽  
Equity Market ◽  
Industrial Technology ◽  
Chinese Stock Market ◽  
Volatility Clustering ◽  
High Volatility ◽  
Entire Dataset ◽  
Daily Returns

The issue of volatility clustering i.e., if periods of high volatility on stocks returns are typically followed by other periods of high volatility and vice versa, is analysed in this article at a sector level for the Chinese stock market. This analysis was performed with daily returns for the period from 2008 to 2017. When the entire dataset is analysed the statistical tests are rather consistent indicating that there is volatility clustering for all the major nine sectors (basic materials, communications, consumer cyclical, consumer non-cyclical, energy financial, industrial, technology and utilities). However, when each year is analysed independently the results are much more mixed with some sectors, such as technology companies, that could a priori look as a prime candidate for volatility clustering having less years with such feature present that other sectors such as for instance basic materials. The issue of volatility clustering at a sector level is of clear interest and can be used as another tool to optimize portfolio allocations. It is interesting to see that volatility clustering seems to be present when the statistical tests are performed over long periods of time but less so when the timeframe is shortened.

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

Performance of Socially Responsible Portfolios Across Sectors in Indian Stock Market

Think India ◽  
2016 ◽  
Vol 19 (1) ◽  
pp. 01-09
Author(s):  
Vanita Tripathi ◽  
Varun Bhandari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Factor Model ◽  
Sharpe Ratio ◽  
Market Model ◽  
Socially Responsible ◽  
Cross Sectional ◽  
Responsible Investing ◽  
Indian Stock Market ◽  
Three Factor

The question of whether socially responsible stocks outperform or under-perform general stocks has been of keen interest for various researchers and academicians. This paper seeks to empirically examine the performance of socially responsible portfolios across various sectors and index of socially responsible and general companies in Indian stock market. We have taken up S&P ESG and CNX NIFTY as the indices of socially responsible and general companies respectively. ESG index has been classified into six different sectors on the basis of GICS. Performance has been evaluated in terms of risk, return and various risk-adjusted measures like Sharpe ratio, Treynor ratio, Double Sharpe ratio, Modified Sharpe ratio, M2 measure, Jensens alpha, Famas decomposition measure, etc. We have also checked whether market model is sufficient to explain cross sectional variation in stock returns or we need Fama-French three factor model. The study period ranges from January 1996 – December 2013 and it is further divided into different sub-periods. We find that socially responsible stocks across IT, FMCG and financial sectors are well rewarding in Indian stock market by generating significantly higher returns and outperforming the two indices on the basis of risk-adjusted measures employed during 18 year period and different sub-periods. The results uphold even with the use of market model and Fama-French three factor model by generating highest significant excess returns. There is no empirical evidence on the performance evaluation of socially responsible portfolios across different sectors. Hence this study is first of its kind. This will help investors in selecting best sector for investment in socially responsible companies. Significant higher returns of ESG index and socially responsible stocks across different sectors make Socially Responsible Investing (SRI) a better investment vehicle for investors in India. This is the time when general companies should change their approach and agenda towards CSR and start considering ESG issues as their investment themes. The regulators, policy makers and mutual funds should come up with different socially responsible products and sectoral indices to initiate the movement of SRI across different sectors in India.

scholarly journals A Complex Dynamical Analysis of the Indian Stock Market

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Anoop Sasikumar ◽  
Bandi Kamaiah
Keyword(s):  
Stock Market ◽  
Equity Market ◽  
Recurrence Quantification Analysis ◽  
Conclusive Evidence ◽  
Dynamical Analysis ◽  
Dynamical Structure ◽  
Step Size ◽  
Rolling Window ◽  
Quantification Analysis ◽  
Indian Stock Market

This paper seeks to analyze the dynamical structure of the Indian stock market by considering two major Indian stock market indices, namely, BSE Sensex and CNX Nifty. The recurrence quantification analysis (RQA) is applied on the daily closing data of the two series during the period from January 2, 2002, to October 10, 2013. A Rolling Window of 100 and step size 21 are applied in order to see how both the series behave over time. The analysis based on three RQA measures, namely, % determinism (DET), laminarity (LAM), and trapping time (TT), provides conclusive evidence that the Indian equity market is chaotic in nature. Evidences for phase transition in the Indian equity market around the time of financial crisis are also found.

scholarly journals Signaling Hypothesis and Size Anomaly in Indian Stock Market

2021 ◽  
Vol 14 (9) ◽  
pp. 94
Author(s):  
Nagendra Marisetty ◽  
Pardhasaradhi Madasu
Keyword(s):  
Stock Market ◽  
Size Effect ◽  
Abnormal Returns ◽  
Small Sample ◽  
Market Model ◽  
Market Capitalization ◽  
Dividend Signaling ◽  
Small Sample Sizes ◽  
Indian Stock Market ◽  
Signaling Hypothesis

The dividend signaling hypothesis means that dividend change announcements send signals to the market about its prospects. Market capitalization anomaly or size effect means small-cap stocks variances and returns are different than the large-cap stocks. The sample was tested for dividend change announcement, and the sample was divided into large, medium, and small sample sizes based on the market capitalization of the stocks to test the size effect. Event methodology market model used to calculate the abnormal returns on the dividend announcement day. We found that dividends send signals to the market, and the market reacts positively to the dividend change announcements on event day (Aharony and Swary 1980, Litzenberger and Ramaswamy 1982, Dhillon and Johnson 1994, Below and Johnson 1996), but results may vary with the size of the company. Small-cap companies' variances are higher than the large-cap and mid-cap companies, and also small-cap variances are not equal to other variances results similar to Wong (1989), Bandara and Samarakoon (2002), Sehgal and Tripathi (2006), and Switzer (2010). Finally, we concluded that the dividend signaling hypothesis and market capitalization or size effect anomaly exist in the Indian stock market

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