Predictable patterns following large price changes and volume

2019 ◽  
Vol 11 (4) ◽  
pp. 393-405
Author(s):  
Srikanth Parthasarathy
Keyword(s):  
Stock Market ◽  
High Volume ◽  
Trading Strategies ◽  
Price Shocks ◽  
Price Changes ◽  
Content Type ◽  
Low Volume ◽  
Short Horizon ◽  
Indian Stock Market ◽  
Stock Behavior

Purpose The purpose of this paper is to examine the short horizon stock behavior following large price shocks in the Indian stock market. Design/methodology/approach The author followed the methodology developed by Pritamani and Singhal (2001) to the short horizon stock behavior following large price shocks. Multivariate regression has also been used to test the robustness of the evidenced results. Findings The abnormal return following large one-day price changes were not found to be important. However, large price one-day changes, conditioned with volume, evidenced significant reversals and momentum over the following 20-day period. Large price changes accompanied by low volume exhibited significant reversals and suggests significant economic profits. The large price changes accompanied by high volume exhibited continuations. Research limitations/implications Large price changes accompanied by low volume exhibited significant reversals and suggested significant economic profits. The large price changes with high volume exhibited continuations. The contrarian strategy of buying low-volume one-day losers and selling one-day winners produced significant short horizon economic profits in the Indian stock market directly contradicting the efficient market hypothesis and has behavioral implications. Practical implications In this paper, the author has unearthed significant simple profitable trading strategies based on reversals and continuation following large one-day price changes with potential for significant economic profits. Originality/value This paper provides a practical framework for profitable trading strategies based on reversals and continuation following large one-day price changes with a potential for significant economic profits. The analysis of short horizon stock behavior following large price shocks conditional on volume based on the chosen methodology has not been attempted so far in the Indian stock market.

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.

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.

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

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.

scholarly journals Daily abnormal price changes and trading strategies in the FOREX

2020 ◽  
Vol 48 (1) ◽  
pp. 211-222
Author(s):  
Guglielmo Maria Caporale ◽  
Alex Plastun
Keyword(s):  
Design Methodology ◽  
Abnormal Returns ◽  
Trading Strategies ◽  
Price Changes ◽  
Content Type ◽  
Cumulative Abnormal Returns ◽  
Market Inefficiency ◽  
Intraday Data ◽  
Abnormal Profits ◽  
New Evidence

PurposeThis paper explores abnormal price changes in the FOREX by using both daily and intraday data on the EURUSD, USDJPY, USDCAD, AUDUSD and EURJPY exchange rates over the period 01.01.2008–31.12.2018.Design/methodology/approachIt applies a dynamic trigger approach to detect abnormal price changes and then various statistical methods, including cumulative abnormal returns analysis, to test the following hypotheses: the intraday behaviour of hourly returns on overreaction days is different from that on normal days (H1), there are detectable patterns in intraday price dynamics on days with abnormal price changes (H2) and on the following days (H3).FindingsThe results suggest that there are statistically significant differences between intraday dynamics on days with abnormal price changes and normal days respectively; also, prices tend to change in the direction of the abnormal change during that day, but move in the opposite direction on the following day. Finally, there exist trading strategies that generate abnormal profits by exploiting the detected anomalies, which can be seen as evidence of market inefficiency.Originality/valueNew evidence on abnormal price changes and related trading strategies in the FOREX.

Investor attention using the Google search volume index – impact on stock returns

2019 ◽  
Vol 11 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Vighneswara Swamy ◽  
Munusamy Dharani
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Volume Index ◽  
Trading Behavior ◽  
Excess Returns ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Indian Stock Market ◽  
Google Search

Purpose The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market. Design/methodology/approach The authors employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange in the Indian stock market. The authors are motivated by Tetlock (2007) and Bijl et al. (2016) to employ regression approach of econometric estimation. Findings The authors find that high Google search volumes lead to positive returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. The GSVI performs as an useful predictor of the direction as well as the magnitude of the excess returns. The higher quantiles of the GSVI have corresponding higher excess returns. The authors notice that the domestic investor searches are correlated with higher excess returns than the worldwide investor searches. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies. Originality/value To the best of the authors knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The authors use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market.

Momentum anomaly: evidence from the Indian stock market

2017 ◽  
Vol 14 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Supriya Maheshwari ◽  
Raj Singh Dhankar
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Investment Strategy ◽  
Momentum Effect ◽  
Momentum Strategies ◽  
Content Type ◽  
Momentum Profits ◽  
The Usa ◽  
Short Run ◽  
Indian Stock Market

Purpose The purpose of this paper is to provide insights into the profitability of momentum strategies in the Indian stock market. This study further evaluates whether the momentum effect is a manifestation of size, value or an illiquidity effect. Design/methodology/approach Monthly stock return data of 470 BSE listed stocks over the sample period from January 1997 to March 2013 were used to create extreme portfolios (winner and loser). The returns of extreme portfolios were evaluated using t-statistics and a risk-adjusted measure. Further checks were imposed by controlling for other potential sources of risk including size, value and illiquidity. Findings The study provides support in favor of momentum profitability in the Indian stock market. In contrast to the literature, momentum profitability is driven by winning stocks, and hence, buying past winning stocks generates higher returns than shorting loosing stocks in the Indian stock market. Strong momentum profits were observed even after controlling for size, value and trading volume of stocks. This suggests that the momentum effect in the Indian stock market is not a manifestation of small size effect, value effect or an illiquidity effect. Practical implications From the practitioner’s perspective, the study indicates that a momentum-based investment strategy in the short run is still persistent and can generate potential profits in the Indian stock market. Originality/value There is little empirical evidence on the momentum profitability, especially in the Indian stock market. The study contributes toward the literature by analyzing the momentum profitability even after controlling for size, value and an illiquidity effect. Some aspects of the momentum effect were observed to be dissimilar from those observed in literature for the USA and other countries. Such findings justify the need for testing the momentum profitability in stock markets other than the USA.

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