scholarly journals Stock market trading volumes and economic uncertainty dependence: before and during Sino-U.S. trade friction

2020 ◽  
Vol 33 (1) ◽  
pp. 1711-1728
Author(s):  
Yuxin Cai ◽  
Yuyu Tao ◽  
Zhengxuan Yan
Keyword(s):  
Stock Market ◽  
Economic Uncertainty ◽  
Trade Friction ◽  
Trading Volumes ◽  
Stock Market Trading

scholarly journals Artificial Intelligence Applied to Stock Market Trading: A Review

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30898-30917 ◽  
Author(s):  
Fernando G. D. C. Ferreira ◽  
Amir H. Gandomi ◽  
Rodrigo T. N. Cardoso
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
Stock Market Trading

scholarly journals Trading Volume as a Predictor of Market Movement

2019 ◽  
Vol 8 (2) ◽  
pp. 57-69
Author(s):  
Edson Kambeu
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Regression Model ◽  
Trading Volume ◽  
Logistic Regression Model ◽  
Predictive Analytics ◽  
Stock Exchange ◽  
Stock Market Index ◽  
Trading Volumes ◽  
Popular Language

A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

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.

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