scholarly journals Stock returns prediction using kernel adaptive filtering within a stock market interdependence approach

2020 ◽  
Vol 160 ◽  
pp. 113668 ◽  
Author(s):  
Sergio Garcia-Vega ◽  
Xiao-Jun Zeng ◽  
John Keane
2017 ◽  
Vol 93 (3) ◽  
pp. 25-57 ◽  
Author(s):  
Eli Bartov ◽  
Lucile Faurel ◽  
Partha S. Mohanram

ABSTRACT Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion from individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion from individual tweets when assessing a stock's future prospects and value.


2021 ◽  
pp. 097226292098839
Author(s):  
Pankaj Sinha ◽  
Priya Sawaliya

When the accessibility of external finance prohibits a firm from taking the optimum decision related to investment, that firm is called financially constrained. By applying the methodology of Kaplan and Zingales (1997) and Lamont et al. (2001), the current study has created a construct to gauge the level of financial constraints (FC) of the companies which emanate from quantitative information. The study explores whether FC factor is present in the Indian stock market and explores whether the security returns of those firms that are financially constrained move in tandem. The study also attempts to establish the association between security returns and R&D of financially constrained firms. On a sample of 63 R&D reporting companies of S&P BSE 500, traded over the period March 2008 to February 2019, the study used the Fama–French methodology, fixed effect model and the ordered logistic regression. The study finds that firms that are highly constrained earn more returns than low constrained firms. Second, the security returns of firms that are financially constrained move in tandem because these firms are affected by common shocks. This suggests that the FC factor exists in the Indian stock market. Finally, when R&D interacts with the level of FC, then this interaction effect has a negative effect on returns.


Risks ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 89
Author(s):  
Muhammad Sheraz ◽  
Imran Nasir

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.


2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”


2021 ◽  
pp. 031289622110102
Author(s):  
Mousumi Bhattacharya ◽  
Sharad Nath Bhattacharya ◽  
Sumit Kumar Jha

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


Sign in / Sign up

Export Citation Format

Share Document