scholarly journals Predictive Ability of Three Different Estimates of “Cay” to Excess Stock Returns – A Comparative Study for South Africa and USA

2014 ◽  
Vol XVII (Issue 1) ◽  
pp. 3-18
Author(s):  
Noha Emara
Keyword(s):  
South Africa ◽  
Comparative Study ◽  
Stock Returns ◽  
Predictive Ability ◽  
Excess Stock Returns

How Does Google Search Affect the Stock Market? Evidence from Indian Companies

2021 ◽  
pp. 097226292098536
Author(s):  
Tariq Aziz ◽  
Valeed Ahmad Ansari
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Predictive Ability ◽  
Positive Influence ◽  
Market Sentiment ◽  
Price Pressure ◽  
Search Data ◽  
Online Searches ◽  
Excess Stock Returns ◽  
Google Search

Google search data has received considerable attention for its predictive ability in various social and economic outcomes. In the arena of investments, a surge in online searches indicates an enhanced interest of investors, particularly retail, in that company. In this article, we have examined the association between Google search and stock prices in a sample of Indian companies. The results suggest that an increase in Google search is positively related to future excess stock returns, liquidity and volatility. The positive influence of Google search on stock prices, however, is temporary and reverses in the next week. We further show that the market sentiment moderates the interconnection between Google searches and future excess stock returns. The findings are in consonance with the ‘price pressure hypothesis’ of Barber and Odean (2008, Review of Financial Studies, 21(2), 785–818).

scholarly journals Can Multistep Nonparametric Regressions Beat Historical Average in Predicting Excess Stock Returns?

2021 ◽  
Vol 12 (5) ◽  
pp. 71
Author(s):  
Najrin Khanom
Keyword(s):  
Stock Returns ◽  
Predictive Power ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Ordinary Least Squares ◽  
Nonparametric Model ◽  
Predictive Variables ◽  
Out Of Sample ◽  
Excess Stock Returns ◽  
Non Parametric

Several economic and financial variables are said to have predictive power over excess stock returns. Empirically there is little consensus among academics, whether these variables have predictive power or not. Results are often sensitive to the econometric model of choice. The econometric models can produce biased results due to the high degree of persistence in predictive variables. Apart from high persistence, the relationship between stock return and the predictive variable may also be misspecified in the model. In order to address possible non-linearities and endogeneity between the residuals and persistent independent variables in predictive regressions, multi-step non-parametric and semiparametric regressions are explored in this paper. In these regressions, the conditional mean and the residuals are estimated separately and then added to obtain the predicted excess stock returns. Goyal and Welch's (2008) predictive variables are used to predict excess S&P 500 returns. The predictive performance of both in-sample and out-of-sample of the two proposed models are compared with the historical average, Ordinary Least Squares (OLS) and non-parametric regressions. The performance of the models is evaluated using Root Mean Squared Errors (RMSEs). The explored models, particularly the two-step nonparametric model, outperform the compared models in-sample. Out-of-sample several variables are found to have predictive ability.

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Predictive Ability ◽  
Least Square ◽  
Projection To Latent Structures ◽  
Preprocessing Method ◽  
Spectral Residual ◽  
Latent Structures

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

scholarly journals Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 620
Author(s):  
Ioannis Kyriakou ◽  
Parastoo Mousavi ◽  
Jens Perch Nielsen ◽  
Michael Scholz
Keyword(s):  
Stock Returns ◽  
Simultaneous Optimization ◽  
Investment Risk ◽  
Short Term ◽  
Fundamental Interest ◽  
Long Term Stability ◽  
Risk Appetite ◽  
Short And Long Term ◽  
Excess Stock Returns

The fundamental interest of investors in econometric modeling for excess stock returns usually focuses either on short- or long-term predictions to individually reduce the investment risk. In this paper, we present a new and simple model that contemporaneously accounts for short- and long-term predictions. By combining the different horizons, we exploit the lower long-term variance to further reduce the short-term variance, which is susceptible to speculative exuberance. As a consequence, the long-term pension-saver avoids an over-conservative portfolio with implied potential upside reductions given their optimal risk appetite. Different combinations of short and long horizons as well as definitions of excess returns, for example, concerning the traditional short-term interest rate but also the inflation, are easily accommodated in our model.

RESPONSE OF STOCK MARKET DURING COVID-19 AND 2008 FINANCIAL CRISIS: A COMPARATIVE EVIDENCE FROM BRICS NATIONS

2021 ◽  
pp. 1-24
Author(s):  
SANJEEV KUMAR ◽  
JASPREET KAUR ◽  
MOSAB I. TABASH ◽  
DANG K. TRAN ◽  
RAJ S DHANKAR
Keyword(s):  
South Africa ◽  
Financial Crisis ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
2008 Financial Crisis ◽  
First Case ◽  
Egarch Model ◽  
The 2008 Financial Crisis ◽  
The Impact

This study attempts to examine the response of stock markets amid the COVID-19 pandemic on prominent stock markets of the BRICS nation and compare it with the 2008 financial crisis by employing the GARCH and EGARCH model. First, average and variance of stock returns are tested for differences before and after the pandemic, t-test and F-test were applied. Further, OLS regression was applied to study the impact of COVID-19 on the standard deviation of returns using daily data of total cases, total deaths, and returns of the indices from the date on which the first case was reported till June 2020. Second, GARCH and EGARCH models are employed to compare the impact of COVID-19 and the 2008 financial crisis on the stock market volatility by using the data of respective stock indices for the period 2005–2020. The results suggest that the increasing number of COVID-19 cases and reported death cases hurt stock markets of the five countries except for South Africa in the latter case. The findings of the GARCH and EGARCH model indicate that for India and Russia, the financial crisis of 2008 has caused more stock volatility whereas stock markets of China, Brazil, and South Africa have been more volatile during the COVID-19 pandemic. The study has practical implications for investors, portfolio managers, institutional investors, regulatory institutions, and policymakers as it provides an understanding of stock market behavior in response to a major global crisis and helps them in taking decisions considering the risk of these events.

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