scholarly journals An Empirical Note on Delhi Weather Effects in the Indian Stock Market

2019 ◽  
Vol 8 (4) ◽  
pp. 1203-1208

This research paper investigates the dynamic linkage, between three weather factors and two top stock Indices in India, namely, BSE SENSEX and NSE NIFTY. In order to study the weather factor on stock indices, daily weather data of Delhi and daily closing stock price of BSE SENSEX and NSE NIFTY, from January 1st 2001 to 31st December 2017, were collected and analyzed. The study found that the Delhi weather namely humidity influence BSE Sensex returns. The investing community may note the findings, for making intelligent investment decisions. The findings would be useful to investors, speculators and officials managing the Indian Securities Exchanges. This is the first empirical study testing the relationship between stock market returns and weather factors in the City of Delhi in India

GIS Business ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. 1-9
Author(s):  
Dhananjaya Kadanda ◽  
Krishna Raj

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.


2021 ◽  
Vol 15 (2) ◽  
pp. 1-25
Author(s):  
Jifeng Zhang ◽  
Wenjun Jiang ◽  
Jinrui Zhang ◽  
Jie Wu ◽  
Guojun Wang

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.


Author(s):  
Neşe Algan ◽  
Mehmet Balcılar ◽  
Harun Bal ◽  
Müge Manga

This study investigates the impact of terrorism on the Turkish financial market using daily data from Jan 4, 1988 to May 24, 2016. In order to measure the impacts of terrorist attacks in Turkey we test for causality from terrorism index to returns and volatilities of 3 aggregate and 16 sector level stock indices using a recently developed nonparametric causality-in-test test of Balcilar et al. (2016). The results obtained indicate that there is no causality from terrorist activities to stock market returns (1st moment). However, we find significant causality at various quantiles from terrorist activates to volatility (2nd moment) of tourism, food and basic materials sectors.


2019 ◽  
Vol 8 (2) ◽  
pp. 3231-3241

The non-deterministic behavior of stock market creates ambiguities for buyers. The situation of ambiguities always finds the loss of user financial assets. The variations of price make a very difficult task to predict the option price. For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network. The accuracy of prediction is always a challenging task of for individual model and hybrid model. The variation gap of hypothesis value and predicted value reflects the nature of stock market. In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.


2020 ◽  
Vol 12 (7) ◽  
pp. 2664 ◽  
Author(s):  
Yeonwoo Do ◽  
Sunghwan Kim

In this study, we investigate the effects of the level and changes in environmental, social and corporate governance (ESG) rating, an index developed to represent a firm’s long-term sustainability, on the stock market returns of Korea Composite Stock Price Index (KOSPI) listed firms over the period 2011–2018. We find that the changes in ESG ratings have statistically significant short-term effects on their abnormal returns. However, their impacts on short-term abnormal returns decrease some days after the disclosure and become negative in the third year. The results imply that investors in the Korean stock market do not view corporate social responsibility activities as a means of supporting their long-term sustainability, judging from the firm value for a long period after their rating. Rather, based on the effects of the changes on coefficient signs over the period—positive in the year and the year after, no effects in the following year, and negative in the third year and later—we can infer that the short-term oriented market sentiments of investors might worsen their long-term stock performances, thus deteriorating their sustainability and growth opportunities.


2004 ◽  
Vol 29 (3) ◽  
pp. 35-42 ◽  
Author(s):  
S N Sarma

The objective of this paper is to explore the day-of-the-week effect on the Indian stock market returns in the post-reform era. Till the late seventies, empirical studies provided ample evidence as to the informational efficiency of the capital markets advocating futility of information in consistently generating abnormal returns. However, later studies identified certain anomalies in the efficient market postulate. One major anomaly brought forth was the calendar-related abnormal rates of return. Various studies in this domain empirically demonstrated, through parametric and non-parametric tests on the stock returns data, that turn of the year, month, week, and holidays have consistently generated abnormal equity returns in both the developed and emerging markets unrelated to the attendant risks. Studies on the Indian stock markets' calendar anomalies, especially in the post-reform era, are very few. In an attempt to fill this gap, this study explores the Indian stock market's efficiency in the 'weak form' in the context of calendar anomalies, especially in respect of the weekend effect. Daily returns generated by the SENSEX, NATEX, and BSE200 during January 1st 1996 to August 10th 2002 comprising a total of 1,667 observations for each of the indices are considered for testing the seasonality. While most of the studies have considered the returns of one of the major indices based on the closing values, this study examines the multiple indices for possible seasonality. An analysis of returns' pattern of multiple indices is helpful in identifying the presence or otherwise of the stock market seasonality associated with various portfolios and for testing the efficacy of investment game based on the observed patterns of the returns. This study employed the daily mean index value for generating the daily returns to relax the implied assumption of the earlier studies — by considering the closing values of the indices — that trading is done at the closing values. A non-parametric test — Kruskall-Wallis test using 'H' statistic — is employed for testing the seasonality in the Indian stock market returns. The null hypothesis tested is that there are no differences in the mean daily returns across the weekdays. The major findings of the study are as follows: The Indian stock markets do manifest seasonality in their returns' pattern. The Monday-Tuesday, Monday-Friday, and Wednesday-Friday sets have positive deviations for all the indices. The Monday-Friday set for all the indices has the highest positive deviation thereby indicating the presence of opportunity to make consistent abnormal returns through a trading strategy of buying on Mondays and selling on Fridays. The above-mentioned active strategy is found to be beneficial in case of SENSEX The above-mentioned active strategy is found to be beneficial in case of SENSEX alone during the study period while for the others — NATEX and BSE200 — a passive ‘buy and hold’ strategy is more effective. The study concludes that the observed patterns are useful in timing the deals thereby exploring the opportunity of exploiting the observed regularities in the Indian stock market returns.


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