What make investors herd while investing in the Indian stock market? A hybrid approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muskan Sachdeva ◽  
Ritu Lehal ◽  
Sanjay Gupta ◽  
Aashish Garg
Keyword(s):  
Stock Market ◽  
Fuzzy Ahp ◽  
Hybrid Approach ◽  
North India ◽  
Second Phase ◽  
Herd Behavior ◽  
Herding Behavior ◽  
Two Phase ◽  
Content Type ◽  
Indian Stock Market

PurposeIn recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.Design/methodology/approachIn this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.FindingsResults of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.Research limitations/implicationsThe current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.Practical implicationsComprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.Originality/valueThe current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.

Herd behavior in the French stock market

2017 ◽  
Vol 16 (4) ◽  
pp. 497-515 ◽  
Author(s):  
Houda Litimi
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Design Methodology ◽  
Garch Model ◽  
Herd Behavior ◽  
Conditional Volatility ◽  
Herding Behavior ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Content Type

Purpose This paper aims to investigate the herding behavior in the French stock market and its effect on the idiosyncratic conditional volatility at a sectoral level. Design/methodology/approach This sample covers all the listed companies in the French stock market, classified by sector, over four major crisis periods. The author modifies the cross-sectional absolute deviation (CSAD) model to include trading volume and investors sentiment as herding triggers. Furthermore, the author uses a modified GARCH model to investigate the effect of herding on conditional volatility. Findings Herding is present in the French market during crises, and it is present in only some sectors during the entire period. The main trigger for investors to embark into a collective herding movement differs from one sector to another. Furthermore, herding behavior has an inhibiting effect on market conditional volatility. Originality/value The author modifies the CSAD model to investigate the presence of herding in the French stock market at a sectoral level during turmoil periods. Furthermore, the particularly designed GARCH model provides new insights on the effect of herding and volume turnover on the conditional volatility.

An examination of herding behavior in Pakistani stock market

2015 ◽  
Vol 10 (3) ◽  
pp. 474-490 ◽  
Author(s):  
Zuee Javaira ◽  
Arshad Hassan
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Stock Exchange ◽  
The Body ◽  
Investor Behavior ◽  
Decision Making Process ◽  
Herd Behavior ◽  
Herding Behavior ◽  
Content Type ◽  
Portfolio Returns

Purpose – The purpose of this paper is to examine the investment behavior of Pakistani stock market participants, specifically with respect to their tendency to exhibit herd behavior. Design/methodology/approach – The study employed two different methodologies suggested by Christie and Huang (1995) and Chang et al. (2000) to test herd formation. Results are based on daily and monthly stock of KSE-100 index for the period 2002-2007. Findings – Results based on daily and monthly stock data from Karachi Stock Exchange indicate the non-existence of herd behavior for the period 2002-2007 and find no support for the rational asset pricing model and investor behavior found inefficient. This study denied proved evidence of herding due to market return asymmetry, high and low trading volume states and asymmetric market volatility. Macroeconomic fundamentals have insignificant role in decision-making process of investor therefore has no impact on herding behavior. However, during liquidity crisis of March 2005, Pakistani stock market exhibit herding behavior due to asymmetry of information among investors, presence of speculator and questionable badla financing-local leverage financing mechanism. Research limitations/implications – In future, this study can be improved by employing stock returns portfolios based on market capitalization or sector wise portfolio returns from KSE-100. Furthermore by identifying those factors that cause market to be overall inefficient and define the pattern of the investor trading activities. Practical implications – For an accurate valuation of assets investors should incorporate the herding factor. Social implications – As the assets are mispriced, investor behavior is uncertain and markets are inefficient, foreign investors should invest with caution, as large numbers of securities are needed to achieve the same level of diversification than in an otherwise normal market. Originality/value – In Karachi Stock Exchange, it is first attempt to uncover the herding behavior. This paper contribute to the body of knowledge by investigating the herding behavior in the emerging markets since most of the previous study concentrate more on the developed markets. Furthermore, the study also analyzed the herding behavior in different economic condition and includes economic variables and examines asymmetric effect. This study presents an integrated model to test herding behavior in Pakistani equity market. Consideration of said behavioral effect in the decision-making process of investor will make the decisions more rational and optimal.

Impact of herding on the returns in the Indian stock market: an empirical study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sunaina Kanojia ◽  
Deepti Singh ◽  
Ashutosh Goswami
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Market Condition ◽  
Individual Investors ◽  
Herd Behavior ◽  
Market Conditions ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Content Type ◽  
Indian Stock Market

PurposeHerd behavior has been studied herein and tested based on primary respondents from Indian markets.Design/methodology/approachThe paper expounds the empirical evidence by applying the cross-sectional absolute deviation method and reporting on herd behavior among decision-makers who are engaged in trading in the Indian stock market. Further, the study attempts to analyze the market-wide herding in the Indian stock market using 2230 daily, 470 weekly and 108 monthly observations of Nifty 50 stock returns for a period of nine years from April 1, 2009 to March 31, 2018 during the normal market conditions, extreme market conditions and in both increasing and decreasing market conditions.FindingsIn a span of a decade witnessing different market cycles, the authors’ results exhibit that there is no evidence of herding in any market condition in Indian stock market primarily due to the dominance of institutional investors and secondly because of low market participation by individual investors.Originality/valueThe results reveal that there is no impact of herd behavior on the stock returns in the Indian equity market during the normal market conditions. It highlights that the participation of individuals who are more prone to herding is more evident for short-run investments, contrary to long-term holdings.

Islamic finance and herding behavior: an application to Gulf Islamic stock markets

2018 ◽  
Vol 10 (2) ◽  
pp. 192-206 ◽  
Author(s):  
Imed Medhioub ◽  
Mustapha Chaffai
Keyword(s):  
Saudi Arabia ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Future Research ◽  
Herd Behavior ◽  
Herding Behavior ◽  
Cross Sectional ◽  
Monthly Data ◽  
Content Type

Purpose The purpose of this paper is to examine the herding behavior in GCC Islamic stock markets. Design/methodology/approach The authors followed the methodology developed by Chiang and Zheng (2010) to test herding behavior. Cross-sectional tests have been considered in this paper. The authors use both OLS and GARCH estimations to examine herding behavior by using a sample of GCC Islamic stock markets. Findings By applying monthly data for the period between January 2006 and February 2016 for five Islamic GCC stock returns (Bahrain, Kuwait, Qatar, Saudi Arabia and UAE), results suggest a significant evidence of herd behavior in Saudi and Qatari Islamic stock markets only. When the authors take into account the existence of asymmetry in herd behavior between down- and up-market periods, evidence of herding behavior during down market periods in the case of Qatar and Saudi Arabia was found. In addition, the authors found that Kuwaiti and Emirates Islamic stock markets herd with the local conventional stock market, showing the interdependencies between Islamic and conventional markets. Research limitations/implications In this paper, the authors found an absence of herding behavior in some Islamic stock markets (Bahrain, Kuwait and Emirates). This is not the result of Shariah guidelines in these Islamic markets, but this is mainly due to the weak oscillations of returns which are very close to zero. In our future research, the authors could apply daily data and compare the results to those obtained in this paper by using monthly data. Originality/value This paper provides a practical framework in order to analyze the herding behavior concept for GCC Islamic stock markets. Its originality consists of linking the herding behavior to ethics and morality to verify whether the properties and guidelines of Islam are respected in Islamic stock markets. To the best of the authors’ knowledge, no other paper has treated the case of herding behavior in Islamic stock markets and taking into account the possible influence of the conventional market on the Islamic stock market that may impact herding behavior.

Determinants of Trading Decision: An Experiential Examination

GIS Business ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 1-9
Author(s):  
Gunjan Sharma ◽  
Tarika Singh ◽  
Suvijna Awasthi
Keyword(s):  
Stock Market ◽  
Capital Markets ◽  
Capital Market ◽  
Capital Mobility ◽  
Influential Factors ◽  
Herd Behavior ◽  
Human Beings ◽  
The Past ◽  
The Government ◽  
Indian Stock Market

In the midst of increasing globalization, the past two decades have observed huge inflow of outside capital in the shape of direct and portfolio investment. The increase in capital mobility is due to contact between the different economies across the globe. The growing liberalization in the capital market leads to the growth of various financial products and services. Over the past decade, the Indian capital market has witnessed numerous changes in the direction of developing the capital markets more robust. With the growing Indian economy, the larger inflow of funds has been fetched into the capital markets. The government is continuously working on investor’s education in order to increase retail participation in the Indian stock market. The habits of the risk-averse middle class have been changing where these investors started participating in the Indian stock market. It is an explored fact that human beings are irrational and considering this fact becomes imperative to investigate factors that influence the trading decisions. In this research, ‘an attempt has been made to investigate various factors that affect the individual trading decision’. The data has been collected from various stockbroking firms and from clients of those stockbroking firms their opinions were recorded by means of a questionnaire. Data collected through the structured questionnaire, 33 questions were prepared which was given to the 330 respondents on the basis of convenience sampling out of which 220 individuals filled questionnaire, the total of 200 questionnaires was included in the study after eliminating the incomplete questionnaire. Various factors are being explored from the literature and then with the help of factor analysis some of the most influential factors have been explored. Factors like overconfidence, optimism, cognitive bias, herd behavior, advisory effect, and idealism are the factors which influenced the trading decision of the investors the most. Such kind of a study is contributing in the area of behavioral finance as a trading decision is an important aspect while investing in the stock market. And this kind of study would be helping and assisting financial advisors to strategies for their clients in making the right allocation and also the policy maker and market regulators to come up with better reforms for the Indian stock markets.

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.

Artificial intelligence in financial services: a qualitative research to discover robo-advisory services

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ankita Bhatia ◽  
Arti Chandani ◽  
Rizwana Atiq ◽  
Mita Mehta ◽  
Rajiv Divekar
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
Financial Services ◽  
Future Research ◽  
Individual Investors ◽  
Content Type ◽  
Advisory Services ◽  
Wealth Management ◽  
Management Scenario ◽  
Indian Stock Market

Purpose The purpose of this study is to gauge the awareness and perception of Indian individual investors about a new fintech innovation known as robo-advisors in the wealth management scenario. Robo-advisors are comprehensive automated online advisory platforms that help investors in managing wealth by recommending portfolio allocations, which are based on certain algorithms. Design/methodology/approach This is a phenomenological qualitative study that used five focussed group discussions to gather the stipulated information. Purposive sampling was used and the sample comprised investors who actively invest in the Indian stock market. A semi-structured questionnaire and homogeneous discussions were used for this study. Discussion time for all the groups was 203 min. One of the authors moderated the discussions and translated the audio recordings verbatim. Subsequently, content analysis was carried out by using the NVIVO 12 software (QSR International) to derive different themes. Findings Factors such as cost-effectiveness, trust, data security, behavioural biases and sentiments of the investors were observed as crucial points which significantly impacted the perception of the investors. Furthermore, several suggestions on different ways to enhance the awareness levels of investors were brought up by the participants during the discussions. It was observed that some investors perceive robo-advisors as only an alternative for fund/wealth managers/brokers for quantitative analysis. Also, they strongly believe that human intervention is necessary to gauge the emotions of the investors. Hence, at present, robo-advisors for the Indian stock market, act only as a supplementary service rather than a substitute for financial advisors. Research limitations/implications Due to the explorative nature of the study and limited participants, the findings of the study cannot be generalised to the overall population. Future research is imperative to study the dynamic nature of artificial intelligence (AI) theories and investigate whether they are able to capture the sentiments of individual investors and human sentiments impacting the market. Practical implications This study gives an insight into the awareness, perception and opinion of the investors about robo-advisory services. From a managerial perspective, the findings suggest that additional attention needs to be devoted to the adoption and inculcation of AI and machine learning theories while building algorithms or logic to come up with effective models. Many investors expressed discontent with the current design of risk profiles of the investors. This helps to provide feedback for developers and designers of robo-advisors to include advanced and detailed programming to be able to do risk profiling in a more comprehensive and precise manner. Social implications In the future, robo-advisors will change the wealth management scenario. It is well-established that data is the new oil for all businesses in the present times. Technologies such as robo-advisor, need to evolve further in terms of predicting unstructured data, improvising qualitative analysis techniques to include the ability to gauge emotions of investors and markets in real-time. Additionally, the behavioural biases of both the programmers and the investors need to be taken care of simultaneously while designing these automated decision support systems. Originality/value This study fulfils an identified gap in the literature regarding the investors’ perception of new fintech innovation, that is, robo-advisors. It also clarifies the confusion about the awareness level of robo-advisors amongst Indian individual investors by examining their attitudes and by suggesting innovations for future research. To the best of the authors’ knowledge, this study is the first to investigate the awareness, perception and attitudes of individual investors towards robo-advisors.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

scholarly journals A Novel Hybrid Approach for Chronic Disease Classification

2020 ◽  
Vol 15 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Divya Jain ◽  
Vijendra Singh
Keyword(s):  
Chronic Disease ◽  
Classification Accuracy ◽  
Hybrid Approach ◽  
Substantial Reduction ◽  
Disease Classification ◽  
Computational Time ◽  
Second Phase ◽  
Two Phase ◽  
Hybrid Classification ◽  
Diagnostic Framework

A two-phase diagnostic framework based on hybrid classification for the diagnosis of chronic disease is proposed. In the first phase, feature selection via ReliefF method and feature extraction via PCA method are incorporated. In the second phase, efficient optimization of SVM parameters via grid search method is performed. The proposed hybrid classification approach is then tested with seven popular chronic disease datasets using a cross-validation method. Experiments are then conducted to evaluate the presented classification method vis-à-vis four other existing classifiers that are applied on the same chronic disease datasets. Results show that the presented approach reduces approximately 40% of the extraneous and surplus features with substantial reduction in the execution time for mining all datasets, achieving the highest classification accuracy of 98.5%. It is concluded that with the presented approach, excellent classification accuracy is achieved for each chronic disease dataset while irrelevant and redundant features may be eliminated, thereby substantially reducing the diagnostic complexity and resulting computational time.

Stock market predictability

2014 ◽  
Vol 31 (4) ◽  
pp. 354-370 ◽  
Author(s):  
Silvio John Camilleri ◽  
Christopher J. Green
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Serial Correlation ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Lag Effects ◽  
Daily Data ◽  
Indian Stock Market

Purpose – The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design/methodology/approach – The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings – The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market. Research limitations/implications – The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality/value – The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

Sign in / Sign up

Export Citation Format

Share Document