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

Relationship between Foreign Portfolio Investments (FPI), Domestic Institutional Investors, and Stock Market Returns in India

GIS Business ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. 1-9
Author(s):  
Dhananjaya Kadanda ◽  
Krishna Raj
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Negative Relationship ◽  
Trading Strategies ◽  
Market Returns ◽  
Stock Market Returns ◽  
Foreign Portfolio Investment ◽  
Stock Market Return ◽  
Indian Stock Market ◽  
Feedback Trading

The present article attempts to understand the relationship between foreign portfolio investment (FPI), domestic institutional investors (DIIs), and stock market returns in India using high frequency data. The study analyses the trading strategies of FPIs, DIIs and its impact on the stock market return. We found that the trading strategies of FIIs and DIIs differ in Indian stock market. While FIIs follow positive feedback trading strategy, DIIs pursue the strategy of negative feedback trading which was more pronounced during the crisis. Further, there is negative relationship between FPI flows and DII flows. The results indicate the importance of developing strong domestic institutional investors to counteract the destabilising nature FIIs, particularly during turbulent times.

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

2021 ◽  
pp. 031289622110102
Author(s):  
Mousumi Bhattacharya ◽  
Sharad Nath Bhattacharya ◽  
Sumit Kumar Jha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
High Frequency ◽  
Jel Classification ◽  
Exogenous Shock ◽  
Intraday Data ◽  
Day Of The Week ◽  
Holiday Effects ◽  
Indian Stock Market ◽  
The Impact

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

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