scholarly journals Evaluating Profitability of Shooting Star Candlestick Pattern in Indian Stock Market

2019 ◽  
Vol 8 (2) ◽  
pp. 5412-5417
Keyword(s):  
Stock Market ◽  
T Test ◽  
Trading Strategies ◽  
Superior Performance ◽  
Binomial Test ◽  
Daily Data ◽  
Indian Stock Market

This paper investigates the profitability of a candlestick reversal pattern ‘Shooting Star’ in Indian stock market. Its profitability is investigated by using daily data of Nifty 50 component stocks over the period from January 1, 2000 till December 31, 2017. Seven different trading strategies based on shooting star are tested using bootstrapped skewness adjusted t-test and binomial test. The superior performance of three trading strategies for one- and two-day holding periods demonstrates that it can be used to generate profits in Indian stock market. It is recommended that one should trade shooting stars of significantly smaller size and exit short positions within two days.

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.

scholarly journals Does the Croatian Stock Market Have Seasonal Affective Disorder?

2021 ◽  
Vol 14 (2) ◽  
pp. 89
Author(s):  
Tihana Škrinjarić ◽  
Branka Marasović ◽  
Boško Šego
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Affective Disorder ◽  
Seasonal Affective Disorder ◽  
Stock Exchange ◽  
Trading Strategies ◽  
Future Research ◽  
Winter Solstice ◽  
Daily Data ◽  
Depressive Episodes

This paper explores mood anomalies, specifically the seasonal affective disorder (SAD) effect on the Zagreb Stock Exchange (ZSE). SAD is defined as a syndrome of depressive episodes in human behavior due to the changing of the season. Thus, the motive of this research is to gain better insights into the investors’ sentiment regarding SAD effects. The purpose of the research is to observe how investors’ sentiment affects the return and risk series on ZSE and if this could be exploitable. Using daily data on stock market return CROBEX for the period January 2010—February 2021, SAD effects are tested to explore if seasonal changes affect the stock returns and risk. Besides the SAD variable in the model, some control variables are included as well: Monday, tax, and COVID-19 effect. The results indicate that SAD effects exist on ZSE, even with controlling for mentioned effects; and asymmetries around winter solstice exist. Implications of such findings can be found in simulating trading strategies, which could incorporate such information to gain profits. Limitations of the research focus on one market, observing static parameters of the estimated models, and observing simple trading strategies. Thus, future research should focus on international diversification possibilities, time-varying models, and fully exploring the exploitation possibilities of such findings.

scholarly journals Force majeure events and stock market reactions in Ukraine

2019 ◽  
Vol 16 (1) ◽  
pp. 334-345 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Alex Plastun ◽  
Inna Makarenko
Keyword(s):  
Stock Market ◽  
Abnormal Returns ◽  
Trading Strategies ◽  
Social Force ◽  
Stock Market Index ◽  
Market Reactions ◽  
Technological Disasters ◽  
Daily Data ◽  
Force Majeure ◽  
Student’S T

This paper examines reactions in the Ukrainian stock market to force majeure events, which are divided into four groups: economic force majeure, social force majeure, terrorist acts, natural and technological disasters. More specifically, using daily data for the main Ukrainian stock market index (namely PFTS) over the period from January 1, to December 31, 2018 this study investigates whether or not force majeure events create (temporary) inefficiencies and there exist profitable trading strategies based on exploiting them. For this purpose, cumulative abnormal returns and trading simulation approaches are used in addition to Student’s t-tests. The results suggest that the Ukrainian stock market absorbs new information rather fast. Negative returns in most cases are observed only on the day of the event. The only exception is technological disasters, the market needing up to ten days to react fully in this case. Despite the presence of a detectable pattern in price behavior after force majeure events (namely, a price decrease on the day of the event) no profitable trading strategies based on it are found as their outcomes do not differ from those generated by random trading.

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 The performance of the Indian stock market during COVID-19

2020 ◽  
Vol 17 (3) ◽  
pp. 133-147
Author(s):  
Rashmi Chaudhary ◽  
Priti Bakhshi ◽  
Hemendra Gupta
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Stock Exchange ◽  
Health Care Information ◽  
Crisis Period ◽  
Daily Data ◽  
Composite Indices ◽  
Health Care Information Technology ◽  
Indian Stock Market ◽  
The Impact

The current empirical study attempts to analyze the impact of COVID-19 on the performance of the Indian stock market concerning two composite indices (BSE 500 and BSE Sensex) and eight sectoral indices of Bombay Stock Exchange (BSE) (Auto, Bankex, Consumer Durables, Capital Goods, Fast Moving Consumer Goods, Health Care, Information Technology, and Realty) of India, and compare the composite indices of India with three global indexes S&P 500, Nikkei 225, and FTSE 100. The daily data from January 2019 to May 2020 have been considered in this study. GLS regression has been applied to assess the impact of COVID-19 on the multiple measures of volatility, namely standard deviation, skewness, and kurtosis of all indices. All indices’ key findings show lower mean daily return than specific, negative returns in the crisis period compared to the pre-crisis period. The standard deviation of all the indices has gone up, the skewness has become negative, and the kurtosis values are exceptionally large. The relation between indices has increased during the crisis period. The Indian stock market depicts roughly the same standard deviation as the global markets but has higher negative skewness and higher positive kurtosis of returns, making the market seem more volatile.

scholarly journals Foreign Institutional Investment and Stock Market Volatility in India: An Empirical Analysis

1970 ◽  
Vol 3 (4) ◽  
pp. 295-304
Author(s):  
Sharanjit S. Dhillon ◽  
Manjinder Kaur
Keyword(s):  
Stock Market ◽  
Risk Premium ◽  
Stock Exchanges ◽  
Stock Market Volatility ◽  
Institutional Investment ◽  
Daily Data ◽  
Increased Risk ◽  
Depository Receipts ◽  
Current Price ◽  
Indian Stock Market

The two major capital market reforms of (i) entry of Foreign Institutional Investors (FIIs) in Indian stock market (ii) permission to Indian companies for raising capital from foreign stock exchanges by means of American Depository Receipts (ADRs) / Global Depository Receipts (GDRs). Further introduction of two-way fungibility in these instruments of ADRs / GDRs leads to reduction of the sovereignty of Indian stock market. As such, Indian stock market now, is not only sensitive to national events but also more sensitive to international events. Due to the speculative motive of FIIs investment, investment by FIIs is subject to frequent reversals. Volatility is a measure of how far the current price of an asset deviates from its average past prices. Investors demand higher risk premium as a compensation for increased risk due to volatility. A higher risk premium implies higher cost of capital and thus lowers investment. The prevailing inefficiency in emerging securities markets including India further magnifies the problem of volatility.  In this paper, an effort is made to predict stock return volatility and contribution of FIIs investment to that volatility using high frequency data (daily data).

scholarly journals Impact Of Union Budget On Indian Stock Market

2015 ◽  
Vol 2 (11) ◽  
pp. 21 ◽  
Author(s):  
Divya Verma Gakhar ◽  
Neha Kushwaha ◽  
Vinita Ashok
Keyword(s):  
Stock Market ◽  
T Test ◽  
Medium Term ◽  
Short Term ◽  
Statistical Tools ◽  
F Test ◽  
Indian Stock Market ◽  
The Impact ◽  
Paired T Test

This paper analyzes the impact of Union budget on NSE’s CNX NIFTY Index. The impact is measured in terms of daily average returns and volatility over the short term, medium term and long term period in pre and post budget period. The data has been collected for five budget periods from 2011 to 2015. The statistical tools used are paired T-test and F-test. Paired T-test is conducted on average returns and F-test is conducted on variances over the period i.e., 3, 10 and 30 days in pre and post budget period. The maximum impact of budget is seen in short term then it gradually decreases in medium term and finally diminishes in the long term. The implication of this paper is that the investor should fear from investing in the stock market around the budget period.

Impact of Weather on Return and Volatility: Evidence from Indian Stock Market

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Vijayakumar N. ◽  
Dharani M. ◽  
Muruganandan S.
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Return ◽  
Return Volatility ◽  
Weather Factors ◽  
Stock Return Volatility ◽  
Daily Data ◽  
Indian Stock Market ◽  
The Impact

This study examines the impact of Weather factors on return and volatility of the Indian stock market. The study uses the daily data of top four metros and tests its impact on the return and volatility of S&P CNX Nifty index from January 2008 to December 2013. This study applies GARCH (1, 1) model and find that the stock returns are influenced by temperature in Chennai and the stock return volatility influenced by the temperature in Mumbai, Delhi and Kolkata.

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.

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