scholarly journals An Investigation of Low Volatility Anomaly in Indian Stock Market

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Sitaram Pandey ◽  
Amitava Samanta
Keyword(s):  
Stock Market ◽  
Direct Relationship ◽  
Empirical Studies ◽  
International Markets ◽  
Risk Return ◽  
Indian Context ◽  
Normal Behavior ◽  
High Volatility ◽  
Capm Model ◽  
Indian Stock Market

<div><p><em>The objective of this paper is to investigate the presence of low volatility anomaly in Indian stock Market. Anomaly occurs due to the deviation in normal behavior of stocks with respect to risk-return relationship as suggested by CAPM theory. The low volatility anomaly means that portfolio of low volatility stocks provides better returns than the portfolio of high volatility stocks. The anomaly under study is one of the most common technical anomalies detected in various International markets but very few studies are there in Indian context. CAPM theory suggest that there is direct relationship between risk &amp; return but various empirical studies finds that portfolio of low volatility stocks outperforms portfolio of high volatility stocks. In this study, returns of volatility sorted portfolios are used to analyze the low volatility anomaly. The study uses constituent stocks of S&amp;P CNX 100 index of NSE. The data is taken from period January 2001 to December 2014.  The results of this study did not found   any significant low volatility anomaly in India rather results are supporting the CAPM model and thus found that high volatility quintile gives high returns in India and vice-versa.</em></p></div>

Institutional Herding: Causality and Persistence

2021 ◽  
pp. 227797522110402
Author(s):  
S S S Kumar
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Mutual Funds ◽  
Institutional Investors ◽  
International Markets ◽  
Jel Classification ◽  
Trading Strategies ◽  
Trading Activity ◽  
Indian Stock Market ◽  
Institutional Herding

We investigate the causality in herding between foreign portfolio investors (FPIs) and domestic mutual funds (MFs) in the Indian stock market. The estimated herding levels are considerably higher than those observed in other international markets, and herding is prevalent in small stocks. We find that institutional investors follow contrarian-trading strategies, unlike what was documented in most other markets. Analysis of the aggregate herding measure shows a bi-directional causality between FPIs and MFs. Further analysis using directional herding measures indicate no evidence of causality between institutional herds on the sell-side. But we find causality on the buy-side and it is running in both directions between FPIs and MFs, implying a feedback of information. Given the tendency of institutions for herding in small stocks, adopting contrarian-trading strategies, the observed sell-side causality is perhaps having a salubrious effect. As institutional investors are contrarians, their trading activity will lead to price corrections in small stocks aligning with the fundamentals, thereby contributing to market efficiency. JEL Classification: C23, C58, G23, G15, G40

Day Of The Week And Its Effect On Stock Market Volatility: Evidence From An Emerging Market

2013 ◽  
Vol 29 (6) ◽  
pp. 1727 ◽  
Author(s):  
Omar Farooq ◽  
Mohammed Bouaddi ◽  
Neveen Ahmed
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Emerging Market ◽  
International Markets ◽  
Conditional Variance ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Foreign Investors ◽  
High Volatility ◽  
Day Of The Week

This paper investigates the day of the week effect in the volatility of the Saudi Stock Exchange during the period between January 7, 2007 and April 1, 2013. Using a conditional variance framework, we find that the day of the week effect is present in the volatility. Our results show that the lowest volatility occurs on Saturdays and Sundays. We argue that due to the closure of international markets on Saturdays and Sundays, there is not enough activity in the Saudi Stock Exchange. As a result, the volatility is the lowest on these days. Our results also show that the highest volatility occurs on Wednesdays. We argue Wednesday, being the last trading day of the week, corresponds with the start of four non-trading days (Thursday through Sunday) for foreign investors. Fearing that they will be stuck up with stocks in case some unfavorable information enters the market, foreign investors tend to exit the market on Wednesdays. As a result of excessive trading, there is high volatility on Wednesdays.

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.

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.

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.

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.

Risk-return trade-off, information diffusion, and U.S. stock market predictability

2015 ◽  
Vol 02 (04) ◽  
pp. 1550038 ◽  
Author(s):  
Haibin Xie ◽  
Shouyang Wang
Keyword(s):  
Stock Market ◽  
Information Diffusion ◽  
Stock Return ◽  
Empirical Studies ◽  
Market Returns ◽  
Complementary Information ◽  
Stock Market Returns ◽  
Trade Off ◽  
Academic Literature ◽  
Risk Return

Recent academic literature in finance documents both risk-return trade-off and gradual information diffusion (ID). Motivated by these two financial theories, this paper proposes the ARCH-M model augmented by an ID indicator to forecast the U.S. stock market returns. Empirical studies performed on the monthly S&P500 index show that our approach is useful in both statistical and economic sense. Further analysis shows that the ID provides complementary information to risk-return trade-off effect. Our findings confirm that financial theories are valuable for stock return forecasting.

scholarly journals Conditional relation between return and co-moments – an empirical study for emerging Indian stock market

2020 ◽  
Vol 17 (2) ◽  
pp. 308-319 ◽  
Author(s):  
Rashmi Chaudhary ◽  
Dheeraj Misra ◽  
Priti Bakhshi
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Stock Market ◽  
Cross Sections ◽  
Stock Price ◽  
Panel Data Analysis ◽  
Portfolio Returns ◽  
Portfolio Managers ◽  
Capm Model ◽  
Indian Stock Market

Due to many theoretical and practical shortcomings of the traditional CAPM model, this study aims at analyzing the CAPM with possible extensions. The analysis aims to know the empirical soundness of Conditional Higher Moment CAPM in emerging India’s capital market. The sample consists of 69 company’s daily stock price data from April 2004 to March 2019 from NSE 100. Panel data analysis is used on 21 cross-sections. The overall results show that when both up and down markets are incorporated separately, all three moments, namely, co-variance, co-skewness, and co-kurtosis, are priced during the normal Indian economy phase. Further, this study states that including higher moments (co-skewness and co-kurtosis) in the two-moment model provides symmetry in both the up and down markets. This is one of the first studies in the Indian Stock market explaining the variation in portfolio returns through panel data analysis by extending CAPM with conditional higher-order co-moments. The portfolio managers should consider skewness and kurtosis along with variance in constructing the optimal portfolios.

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