scholarly journals Long Memory in Stock-Market Trading Volume

2000 ◽  
Vol 18 (4) ◽  
pp. 410 ◽  
Author(s):  
Ignacio N. Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Trading Volume ◽  
Stock Market Trading

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

2022 ◽  
Author(s):  
Ignacio N Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Long Memory ◽  
Trading Volume ◽  
Moving Average ◽  
Single Step ◽  
Arma Process ◽  
Dow Jones Industrial Average ◽  
Fractional Models ◽  
Stock Market Trading

Abstract We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

scholarly journals Long Memory in Stock-Market Trading Volume

2000 ◽  
Vol 18 (4) ◽  
pp. 410-427 ◽  
Author(s):  
Ignacio N. Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Trading Volume ◽  
Stock Market Trading

scholarly journals Language Consistency and Stock Market Trading Volume

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402091977
Author(s):  
Jameson K. M. Watts
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Positive Influence ◽  
Biotechnology Industry ◽  
Trade Journal ◽  
Journal Articles ◽  
Information Theoretic ◽  
Stock Market Trading

An information-theoretic measure of language consistency is constructed from the text of 13 years of trade journal articles on the biotechnology industry. This measure is then related to the trading volume of a representative portfolio of biotechnology stocks. Findings indicate that language consistency and trading volume have a joint (positive) influence on each other over the long term; however, sharp drops in consistency are also predictive of transient spikes in trading volume. The significance of these findings is discussed in relation to modern theories of legitimation and the economics of surprise.

Relationship Between FIIs’ Herding and Returns in the Indian Equity Market: Further Empirical Evidence

2019 ◽  
pp. 097215091984522
Author(s):  
Kapil Choudhary ◽  
Parminder Singh ◽  
Amit Soni
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Trading Volume ◽  
Negative Relationship ◽  
Positive Association ◽  
Vital Role ◽  
Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Stock Market Trading

Empirical evidence indicates that foreign institutional investors (FIIs) play a vital role in financial markets, and being the major players, they demonstrate positive feedback trading behaviour and usually follow one another’s actions. In order to examine this phenomenon, the present study endeavoured to unearth the relationship between foreign institutional investments (FIIs) and returns in the Indian stock market, trading volume and volatility. The return of the Nifty50 index has surrogated market returns, while volatility is represented by conditional volatility computed from Nifty50, from January 1999 to May 2017. The vector autoregression (VAR) results indicate a positive association between herding among FIIs and lagged market returns, while information asymmetry has no impact on herding. On the other hand, previous-day volatility has a significant bearing on the herding measure. Overall, the results portray a significant relationship between herding and stock market returns in India. The results of multivariate regression exhibit that market return was a primary factor for FII herding during the study period under consideration, while trading volume bore no relationship with herding. In case of market volatility, the empirical results are in congruence with the fact that during the period of the volatile market, FIIs prefer to not indulge in herding. Furthermore, the results of three sub-periods, that is, before, during and after the crisis, are similar to the results of the whole study period which indicates that the return is a prime and vital force for herding; on the contrary, market volatility appears to have a negative relationship with herding.

scholarly journals A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

2013 ◽  
Vol 4 (3) ◽  
pp. 25-31 ◽  
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Tahmineh Haddadi Bonab ◽  
Seyyed Reza Khaze
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Trading Volume ◽  
Regression Approach ◽  
Stock Market Trading

The behavior of daily stock market trading volume

1989 ◽  
Vol 11 (4) ◽  
pp. 331-359 ◽  
Author(s):  
Bipin B. Ajinkya ◽  
Prem C. Jain
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Stock Market Trading

scholarly journals Analyzing Microblogging Activity and Stock Market Behavior through Artificial Neural Networks

2020 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Piñeiro-Chousa Juan ◽  
López-Cabarcos M Ángeles ◽  
Pérez-Pico Ada M ◽  
Vizcaíno-González Marcos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Risk Premium ◽  
Trading Volume ◽  
Network Activity ◽  
Future Research ◽  
Artificial Neural ◽  
Negative Sentiment ◽  
Stock Market Trading

This paper attempts to analyze the relationship between social network activity (message sentiment) and stock market (trading volume and risk premium). We used Artificial Neural Networks to analyze 87,511 stock-related microblogging messages related to S&P500 Index posted between October 2009 and October 2014. The results obtained suggest that there is a direct relationship between trading volume and negative sentiment, and between risk premium and negative sentiment. The paper concludes with several directions for future research.

The use of open source internet to analysis and predict stock market trading volume

2017 ◽  
Vol 41 ◽  
pp. 399-411 ◽  
Author(s):  
Faten Moussa ◽  
Olfa BenOuda ◽  
Ezzeddine Delhoumi
Keyword(s):  
Stock Market ◽  
Open Source ◽  
Trading Volume ◽  
Stock Market Trading
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