econometric analysis of panel data in stata forex

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Econometric analysis of panel data in stata forex requoting forexpros

Econometric analysis of panel data in stata forex

Commodity prices have nosedived, stock prices are at year record low and still falling OECD, The global stock markets continue to sink in the absence of timely policy intervention. Emerging reports from around the world have shown a highly steeped downward sloping trend.

Similarly, in the United States, the stock market has faced the same fate. Further aggravated by falling oil prices, investors are hurriedly selling their stocks and share prices are crashing. With the growing uncertainty in the business arena and no end in sight, the choice of making investment decisions under an extremely dicey condition becomes increasingly inevitable. To avoid the economy going into depression, investment must be sustained.

Private investors will need sufficient information to restore their confidence and national government will require advice on the best policy intervention to create an enabling business environment. Knowledge of how stock prices might behave at later dates presents a unique opportunity to stakeholders. This does not only restore market efficiency but also allows investors enough room for strategic planning. Thus, the findings of this study will offer useful insights to investors seeking to maximize returns in the presence of global health crisis.

Studies analyzing the impact of news on return predictability are gradually gaining prominence. The notable ones among them are Buttner and Hayo , Bank et al. Nonetheless, these studies differ in their choice of news. Thus, the use of news to predict stock returns is not new and they involve macroeconomic and financial news.

What has remained understudied in the literature is the use of health news in return predictability and this constitutes the main contribution of the study. Research in this area becomes crucial given investors' sentiment about the severe consequences of the COVID pandemic on their returns coupled with the need to seek safe investments to minimize the impending high risks and uncertainties associated with pandemic. In this paper, we utilize health news obtained through Google searches to analyze the predictability of stock returns.

The intention is to examine how the news associated with the outbreak of COVID has influenced the trading activities in global stock exchanges particularly those that seem to be worse hit by the pandemic. Since the pandemic is health-related, we hypothesize that related news will be sought by investors when making investment decisions particularly in terms of the severity of the pandemic on the global economy.

To the best of our knowledge, this is the first study to incorporate health news in the predictive model for stock returns. First, we evaluate the predictability of health news as a potential predictor of stock returns during the pandemic period and beyond. Consequently, we evaluate both the in-sample and out-of-sample forecast performance of the health news-based predictive model for stock returns.

This essentially requires comparing the forecast performance of the proposed model with the benchmark model conventionally described as historical or constant returns model. Third, we further test whether controlling for macro-based predictors will enhance the forecast performance of the proposed model.

Fourth, we use dataset that seems global in nature as we cover twenty 20 countries that appear to be worse hit by the COVID Essentially, we use two parameters to identify these countries: the reported cases and deaths associated with the pandemic. Given the countries covered in our analyses with greater impacts on the global economy than other countries of the world put together, it becomes easier to draw meaning generalizations from our research findings.

The main findings from our results reveal that incorporating health-related information in the valuation of stocks improves forecast accuracy. Several robustness checks are considered to validate the results. The remainder of this study is organized as follows: Section 2 describes the data with some preliminary statistics and discussions on the behavior of relevant variables; the empirical methodology is detailed in Section 3 ; Section 4 discusses the findings of the study; and Section 5 concludes the paper.

Our datasets consist of stock prices in USD of the 20 worst-hit countries by the pandemic and corresponding volumes of searches relating to health news. Table 1 shows the list of these countries with the most reported cases and reported deaths of COVID as of 30th of March , as pulled from the website www. The list shows the rank of individual countries according to the number of cases and deaths reported respectively.

The stock index data for each of the country was obtained from www. Although that data is available in different time frequencies, the daily frequency is preferred and extracted on the 30th of March starting from 1st of January The general restriction of the start date to 1st of January is as a result of data availability as Google trends allows for daily frequencies for data spanning a day period or less.

Note: Although, Iran has high incidence of reported COVID cases and deaths, it was omitted from this list because of the unavailability of the country's stock data. The figure represents what it was as of 30th of March when it was retrieved from the website of CDC. Table 2 illustrates the descriptive analysis of countries' stock returns and evaluates its relationship with health-related news. The table summarizes the mean and standard deviation of stock returns across all the countries as well as the behavior of stock returns when health-news searches increase or decline.

The reported average values in Column I of Table 2 represent the average stock returns across all the countries at the average health-related news searches over the period under consideration, January 01 to March Note: The average stock returns are presented in percentages.

Column I depicts the average stock returns and its corresponding standard deviation at the overall mean of health-related news search; Column II indicates the average stock returns and its standard deviation when the health news index is above its overall mean, while Column III considers the same requirements when the news index is below its average value. It is evident from the table that all of the 20 COVID worst hit countries both in terms of reported cases and deaths experienced a decline in their stock returns with all of them recording negative stock returns during this period with the exception of Australia.

The positive returns seen in Australia was expectedly so because the country had suffered economic crises since before the announcement of COVID with the incidences of wild fire disrupting economic activities around the country.

The announcement period coincided with the halt of the crisis, when economy had just begun to recover. The United States and Italy despite being most hit with the highest recorded cases and deaths respectively experienced a modest negative stock return. The analyses in Table 2 further show that as the health-related news search increases, stock returns decline across all the countries considered.

On the other hand, when health news search declines, stock returns across these countries are above their averages. These findings have vast implications for the global economy. One, it implies that investment returns during this period will largely depend on the extent of reportage and global discourse surrounding COVID, investors would be very cautious to observe the trend of the pandemic before committing their wealth.

For this reason, there is likely going to be absence of any serious investment while news of COVID gathers momentum. If cases of infection continue, the global economy will plunge into an inevitable recession. Two, it also connotes that countries that heal fast from the pandemic will likewise achieve quicker economic recovery than those that heal later.

The graphical illustration reveals some co-movements between the two series for all the countries with health news being more volatile. The earlier part of the period of COVID announcement witnessed a minimal fluctuation while an increased fluctuation was recorded in the later part of the period. The graph shows that stock returns did not respond immediately to increased fluctuation in health news and only started responding until about the 3rd month of its announcement.

This maybe because of the gradual spread of the disease with only few cases reported in Europe and America at those times. Note: stock and hnews denote stock returns and changes in health news search respectively. We construct a predictive model to evaluate the evident relationship between health-related news and stock returns of the worst-hit countries by the COVID pandemic. In line with the study objectives, the predictive power is compared with other plausible forecast models for stock returns.

The short-time span since the emergence of the pandemic informs our choice of panel data forecasting approach. A generic specification for a typical panel data regression model can be expressed as 4 :. The panel data model in matrix form is specified this way to be able to isolate the slope coefficient for each country i without loss of generality see Baltagi, for some computational details. In the empirical literature, some studies have favoured the choice of homogenous panels see Baltagi et al.

Baltagi et al. The analyses using heterogenous panel can be done based each country's time series regression, or employing various estimation methods described in the earlier papers see Baltagi, ; Maddala et al. However, the homogeneous panel model is parsimonious particularly with short T which is the case here compared to the more parameter-consuming heterogeneous estimators.

Consequently, we employ the homogenous panels given the short T dimension of our data. We begin our analyses with the baseline model involving the constant return historical average model which ignores any potential predictor of stock and is specified as 5 :. We augment the historical average model with the health-news predictor by theoretically relying on the Investor Recognition hypothesis Merton, The Investor Recognition hypothesis assumes incomplete market information and investors are not aware of all information about the securities in a market.

Therefore, emotions and sentiments based on available information and news influence their decision by selecting only familiar stocks in constructing portfolios see also Adachi et al. The health-news predictability model of stock returns is given as:. The health news index is a measure of investors' awareness and emotions. We also explore an important feature of daily stock returns, the day-of-the-week effect see Zhang et al. To account for this important feature while also avoiding parameter proliferation in the estimable model, we employ a three-step procedure.

The third step involves substituting the day-of-the-week adjusted stock returns series in the health-news predictability model in Eq. Thus, Eq. Hypothetically, a negative asymmetry is expected to impact positively on stock returns, while on the other hand, positive asymmetry, which implies increase in the health-related news search is expected to have a negative impact on stock returns.

To account for asymmetry, we follow the Shin et al. The predictive model that accounts for these asymmetries can be re-specified as:. Lastly, the Arbitrage Pricing Theory provides the theoretical premise for incorporating systemic or macroeconomic risks in the predictability of stock returns. Therefore, we also account for some other important factors that can influence stock returns. Due to data limitation however, given the fact that our focus is on the COVID period, our macro-related variables are limited to those that are available at a high frequency namely exchange rate and crude oil prices.

On this basis, the single predictor model is extended to become:. Ideally, the choice of the return series will be determined by the relative forecast performance of r it and r it d from the single-predictor case. Consequently, Eq. The Clark and West test on the other hand is used to establish the statistical significance of the forecast evaluation procedure in the Campbell and Thompson For a forecast horizon h , the Clark and West test is specified as:.

For additional results, first we extend the evaluation of the health-news predictability model by investigating the relevance of financial news in the health new predictability of stock returns. The VIX series is considered as a leading barometer of market volatility relating to listed options and it has been found to have larger in-sample predictability performance on stock markets Wang, ; Yun, ; Zhu et al.

Thus, for robustness, we evaluate the forecast performance of the combined news indices, i. VIX, an indicator of financial market news, and the health-news index, in the stock returns predictability of top COVID affected countries. The objective here is to see if including the two news indices will produce better forecast accuracy for stock returns relative to the benchmark model as well as the single-predictor health-based model.

The second aspect of the additional results involves accounting for any inherent heterogeneity across the stock returns of the selected countries. The predictive panel data model for stock returns 10 where the health-related news index is the only predictor as specified in Eq. Thus, in addition to allowing for heterogeneity in the predictability, it also incorporates unobserved common factors for the countries' stock returns.

The predictability performance of the stock returns model using the panel heterogenous estimator is evaluated and compared with the historical average model using both the CT and CW tests. We evaluate the health news predictability of stock returns since the emergence of COVID by evaluating the stock returns behavior of top 20 most affected countries.

We rely on official daily information on the number of reported cases and deaths in the selection of these countries. By pooling countries based on the number of reported cases and deaths, we evaluate the veracity of health-news predictability of stock returns. The four variant models estimated and compared with the historical average constant returns model as discussed in the methodology section include: i the single factor health-news predictability model Eq.

As discussed in the methodology section, each model from the historical average model Eq. The predictability results for the four models are summarized in Table 3 and we find that the estimated coefficients for almost all the models are correctly signed and statistically significant following the a priori expectation both across top-cases and top deaths reporting countries.

However, while the coefficient of the positive asymmetry is negative, which conforms with the expected sign and statistically significant, the coefficient of the negative asymmetry is also negative over the period under consideration. By implication, regardless of the movements in health news, its impact on stock returns is negative during the pandemic, although, increased searches for health news have greater adverse effects on stock returns. Furthermore, the stock returns predictability estimates after controlling for macroeconomic variables are summarized in the MD4 column of Table 3.

The estimated coefficient of health-news is also negative and statistically significant conforming with the a priori expectation. Note: The upper pane of the table summarizes the predictability results for the top countries in terms of COVID reported cases, while the lower pane summarizes the results for the top countries with COVID related deaths. Standard errors are reported in parentheses. Next, we examine the forecast performance of each of the contending models, which include the historical average model and the various health-news predictability models.

The forecast performance is evaluated for the in-sample and out-of-sample forecast horizons using the two pair-wise forecast measures: Campbell and Thompson and Clark and West tests. The CW test on the other hand provides the formal procedure for ascertaining the statistical significance of the difference in the observed forecast errors. A positive and significant value of the constant parameter in the CW test regression indicates better forecast performance of the model with the adjusted-MSE relative to the one without adjustment see the Methodology section for details.

Forecast performance of the variant models MD2 to MD4 is evaluated and compared with the performance of the historical average as well as the single predictor model MD1. The C-T stat indicates the Campbell and Thompson test statistics. The positive values for the CT statistics and CW coefficients, as well as the statistical significance of the latter, both for in-sample and out-of-sample data samples, indicate the outperformance of the models over the historical average predictor.

By implication, the results establish that: i the single-predictor model of stock returns with health-news index as the predictor outperforms the historical average constant returns model; ii adjusting stock returns series for day-of-the-week effect is relevant and improves the forecast performance of the single predictor; iii asymmetry in health-news searches is important in the predictability of stock returns, although increased searches for health news have greater depressing effect on stock returns; and, iv controlling for macroeconomic variables improves the forecasting performance of stock returns predictability.

As discussed in the methodology section, our first additional results involve evaluating the forecast performance of the stock returns predictability by introducing financial news captured with the VIX data into the health news model. The predictability results are presented in Table 5 , Table 6 for top countries with reported COVID cases and reported deaths respectively.

In line with the previous analyses, we also evaluate the forecast performance of the VIX-augmented health news model relative to the historical average as well as the single predictor health news model MD1. Both models are estimated using the day-of-the-week adjusted stock returns; C-T stat indicates the Campbell and Thompson test statistics while C-W test is the Clark and West test.

Both tests evaluate the forecast performance of the historical average model and the HN and VIX predictor models. The estimated predictability regression when VIX is combined with the health news index shows that both coefficients of one-period lagged health news and VIX are negative and statistically significant.

For the forecast performance, the results show that the VIX-augmented model outperforms the historical average model both for the in-sample and out-of-sample data partition. Similarly, the model that accommodates both news indices health and financial news perform better than the one with health news only, although the forecast accuracy is relatively equal for the in-sample period. This appears to mirror reality as rational investors seek for all the available information that will strengthen their understanding of the market risks.

The second additional results involve estimating the health news predictability model of stock returns using the heterogeneous panel model approach in order to account for unobserved common factors among the cross sections. The results are summarized in Table 7. The coefficients conform with our a priori expectation that stock returns responds negatively to increasing health-news searches and it is in tune with earlier results using the homogenous panel estimator.

Further, the in-sample and out-of-sample forecast performance evaluation further confirms that using health news as a predictor in stock returns predictability will outperform the historical average model, using both the Campbell and Thompson and Clark and West tests. Health-news predictability results and forecast evaluation using heterogenous panel estimator. For the two categories of countries, i. Lastly, we account for the possible influence of extreme observations or outliers in the predictability models.

Therefore, as an additional robustness test to check for the presence and influence of possible outliers in the dataset, we re-estimated the health-news predictability model of stock returns using robust-to-outliers panel estimator.

We employed the robust least squares procedure which addresses both the potential outliers in the predictor and predicted variables and the results are summarized in Table A1 in the Appendix. We find that the estimated predictability results and the forecast performance are consistent after accounting for outliers. Although the magnitude of impact of health news on stock returns declined after adjusting for outliers, the sign is still negative and statistically significant see Table A1.

In addition, both the forecast measures confirm that the single predictor health news model outperforms the historical average. By implication, predicting stock returns using health news index consistently outperforms the benchmark model regardless of the underlying assumptions for the parameter estimates. This study derives its motivation from the current global pandemic, COVID, to explore the significance of health news Google searches in predicting stock returns.

Our analyses cover top most affected countries during the pandemic in terms of reported cases and deaths. However, the role of health news in the return predictability is less understudied, this is the main contribution of the study. Given the limited time dimension of available data since the emergence of the novel coronavirus, we employ panel data forecasting approach to evaluate the performance of health-news for stock return predictability.

Alternative variants of the health news-based models are considered for robustness. We find that health-news has a negative and statistically significant effect on stock returns, indicating that returns decline as more information is sought on health issues since the pandemic outbreak.

On the implication of findings, rational investors seeking to maximize returns may need to evaluate the extent of uncertainty associated with infectious diseases before taking any investment decision in the stock market and perhaps other financial markets. By way of suggestion for future research, extending the analyses to other financial market such as the commodity, foreign exchange, bond and money markets would offer more insightful outcomes.

The former utilizes oil price news while we focus on health-related news. With more than 46 million monthly users, and over million sessions, the platform is one of the top three global financial websites according to both Similar Web and Alexa. On the other hand, the panel data approach involves the use of panel data procedures and the estimates may eliminate certain biases that may plague country by country estimates.

What is however new is the use of panel data i. The approach followed in the estimation of this model is similar in spirit to that of Westerlund et al. One major attraction to this approach is that it does not require integration property of the common factors used in the predictive model. Health-news predictability results and forecast evaluation using panel robust least squares estimator.

Both models are estimated using panel robust least squares estimators which accounts for inherent outliers in the predictor and predicted series. National Center for Biotechnology Information , U. International Review of Financial Analysis.

Published online Jun Afees A. Author information Article notes Copyright and License information Disclaimer. All rights reserved. Elsevier hereby grants permission to make all its COVIDrelated research that is available on the COVID resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.

Abstract This study derives its motivation from the current global pandemic, COVID, to evaluate the relevance of health-news trends in the predictability of stock returns. Nenova, and A. Shleifer, Norris, and D. Zinnbauer, Ahrend, Nam, Bandyopadhyay, Bhattacharyya and R.

Hodler, Dutta and S. Roy, Popescu and G. Toka, Dewenter, M. Linder, and T. Thomas, Kostadinova, Melnick and R. Eldor, Benesch, S. Loretz, D. Stadelmann, and T. Capriotti, Engelberg and C. Parsons, Pinar and E. Volkan, Kim, H. Zhang, L. Li, and G. Tian, Masrorkhah and T. Lehnert, Ding, W. Hou, Y. Liu, and J.

Zhang, Chang, H. Shim, and T. Yi, Farooq and C. Mertzanis, Gehlbach, and K. Sonin, Stiglitz, S. Amartya, and J. Fitoussi, New York. Henderson, A. Storeygard, and D. Weil, Bahrini and Qaffas, A. North, Kraay, D. Kaufmann, and M.

Mastruzzi, Moudatsou, Borensztein, J. Gregorio, and J. Lee, Alfaro, Carbonell and R. Werner, Baltagi and S. Song, Elhorst,

KUWAIT INVESTMENT AUTHORITY CAREERSAFE

Several robustness checks are considered to validate the results. The remainder of this study is organized as follows: Section 2 describes the data with some preliminary statistics and discussions on the behavior of relevant variables; the empirical methodology is detailed in Section 3 ; Section 4 discusses the findings of the study; and Section 5 concludes the paper. Our datasets consist of stock prices in USD of the 20 worst-hit countries by the pandemic and corresponding volumes of searches relating to health news.

Table 1 shows the list of these countries with the most reported cases and reported deaths of COVID as of 30th of March , as pulled from the website www. The list shows the rank of individual countries according to the number of cases and deaths reported respectively. The stock index data for each of the country was obtained from www. Although that data is available in different time frequencies, the daily frequency is preferred and extracted on the 30th of March starting from 1st of January The general restriction of the start date to 1st of January is as a result of data availability as Google trends allows for daily frequencies for data spanning a day period or less.

Note: Although, Iran has high incidence of reported COVID cases and deaths, it was omitted from this list because of the unavailability of the country's stock data. The figure represents what it was as of 30th of March when it was retrieved from the website of CDC. Table 2 illustrates the descriptive analysis of countries' stock returns and evaluates its relationship with health-related news. The table summarizes the mean and standard deviation of stock returns across all the countries as well as the behavior of stock returns when health-news searches increase or decline.

The reported average values in Column I of Table 2 represent the average stock returns across all the countries at the average health-related news searches over the period under consideration, January 01 to March Note: The average stock returns are presented in percentages. Column I depicts the average stock returns and its corresponding standard deviation at the overall mean of health-related news search; Column II indicates the average stock returns and its standard deviation when the health news index is above its overall mean, while Column III considers the same requirements when the news index is below its average value.

It is evident from the table that all of the 20 COVID worst hit countries both in terms of reported cases and deaths experienced a decline in their stock returns with all of them recording negative stock returns during this period with the exception of Australia. The positive returns seen in Australia was expectedly so because the country had suffered economic crises since before the announcement of COVID with the incidences of wild fire disrupting economic activities around the country.

The announcement period coincided with the halt of the crisis, when economy had just begun to recover. The United States and Italy despite being most hit with the highest recorded cases and deaths respectively experienced a modest negative stock return. The analyses in Table 2 further show that as the health-related news search increases, stock returns decline across all the countries considered. On the other hand, when health news search declines, stock returns across these countries are above their averages.

These findings have vast implications for the global economy. One, it implies that investment returns during this period will largely depend on the extent of reportage and global discourse surrounding COVID, investors would be very cautious to observe the trend of the pandemic before committing their wealth.

For this reason, there is likely going to be absence of any serious investment while news of COVID gathers momentum. If cases of infection continue, the global economy will plunge into an inevitable recession. Two, it also connotes that countries that heal fast from the pandemic will likewise achieve quicker economic recovery than those that heal later.

The graphical illustration reveals some co-movements between the two series for all the countries with health news being more volatile. The earlier part of the period of COVID announcement witnessed a minimal fluctuation while an increased fluctuation was recorded in the later part of the period.

The graph shows that stock returns did not respond immediately to increased fluctuation in health news and only started responding until about the 3rd month of its announcement. This maybe because of the gradual spread of the disease with only few cases reported in Europe and America at those times.

Note: stock and hnews denote stock returns and changes in health news search respectively. We construct a predictive model to evaluate the evident relationship between health-related news and stock returns of the worst-hit countries by the COVID pandemic.

In line with the study objectives, the predictive power is compared with other plausible forecast models for stock returns. The short-time span since the emergence of the pandemic informs our choice of panel data forecasting approach. A generic specification for a typical panel data regression model can be expressed as 4 :.

The panel data model in matrix form is specified this way to be able to isolate the slope coefficient for each country i without loss of generality see Baltagi, for some computational details. In the empirical literature, some studies have favoured the choice of homogenous panels see Baltagi et al. Baltagi et al. The analyses using heterogenous panel can be done based each country's time series regression, or employing various estimation methods described in the earlier papers see Baltagi, ; Maddala et al.

However, the homogeneous panel model is parsimonious particularly with short T which is the case here compared to the more parameter-consuming heterogeneous estimators. Consequently, we employ the homogenous panels given the short T dimension of our data. We begin our analyses with the baseline model involving the constant return historical average model which ignores any potential predictor of stock and is specified as 5 :.

We augment the historical average model with the health-news predictor by theoretically relying on the Investor Recognition hypothesis Merton, The Investor Recognition hypothesis assumes incomplete market information and investors are not aware of all information about the securities in a market.

Therefore, emotions and sentiments based on available information and news influence their decision by selecting only familiar stocks in constructing portfolios see also Adachi et al. The health-news predictability model of stock returns is given as:. The health news index is a measure of investors' awareness and emotions. We also explore an important feature of daily stock returns, the day-of-the-week effect see Zhang et al.

To account for this important feature while also avoiding parameter proliferation in the estimable model, we employ a three-step procedure. The third step involves substituting the day-of-the-week adjusted stock returns series in the health-news predictability model in Eq. Thus, Eq. Hypothetically, a negative asymmetry is expected to impact positively on stock returns, while on the other hand, positive asymmetry, which implies increase in the health-related news search is expected to have a negative impact on stock returns.

To account for asymmetry, we follow the Shin et al. The predictive model that accounts for these asymmetries can be re-specified as:. Lastly, the Arbitrage Pricing Theory provides the theoretical premise for incorporating systemic or macroeconomic risks in the predictability of stock returns. Therefore, we also account for some other important factors that can influence stock returns. Due to data limitation however, given the fact that our focus is on the COVID period, our macro-related variables are limited to those that are available at a high frequency namely exchange rate and crude oil prices.

On this basis, the single predictor model is extended to become:. Ideally, the choice of the return series will be determined by the relative forecast performance of r it and r it d from the single-predictor case.

Consequently, Eq. The Clark and West test on the other hand is used to establish the statistical significance of the forecast evaluation procedure in the Campbell and Thompson For a forecast horizon h , the Clark and West test is specified as:. For additional results, first we extend the evaluation of the health-news predictability model by investigating the relevance of financial news in the health new predictability of stock returns.

The VIX series is considered as a leading barometer of market volatility relating to listed options and it has been found to have larger in-sample predictability performance on stock markets Wang, ; Yun, ; Zhu et al. Thus, for robustness, we evaluate the forecast performance of the combined news indices, i. VIX, an indicator of financial market news, and the health-news index, in the stock returns predictability of top COVID affected countries.

The objective here is to see if including the two news indices will produce better forecast accuracy for stock returns relative to the benchmark model as well as the single-predictor health-based model. The second aspect of the additional results involves accounting for any inherent heterogeneity across the stock returns of the selected countries.

The predictive panel data model for stock returns 10 where the health-related news index is the only predictor as specified in Eq. Thus, in addition to allowing for heterogeneity in the predictability, it also incorporates unobserved common factors for the countries' stock returns. The predictability performance of the stock returns model using the panel heterogenous estimator is evaluated and compared with the historical average model using both the CT and CW tests.

We evaluate the health news predictability of stock returns since the emergence of COVID by evaluating the stock returns behavior of top 20 most affected countries. We rely on official daily information on the number of reported cases and deaths in the selection of these countries.

By pooling countries based on the number of reported cases and deaths, we evaluate the veracity of health-news predictability of stock returns. The four variant models estimated and compared with the historical average constant returns model as discussed in the methodology section include: i the single factor health-news predictability model Eq. As discussed in the methodology section, each model from the historical average model Eq. The predictability results for the four models are summarized in Table 3 and we find that the estimated coefficients for almost all the models are correctly signed and statistically significant following the a priori expectation both across top-cases and top deaths reporting countries.

However, while the coefficient of the positive asymmetry is negative, which conforms with the expected sign and statistically significant, the coefficient of the negative asymmetry is also negative over the period under consideration. By implication, regardless of the movements in health news, its impact on stock returns is negative during the pandemic, although, increased searches for health news have greater adverse effects on stock returns.

Furthermore, the stock returns predictability estimates after controlling for macroeconomic variables are summarized in the MD4 column of Table 3. The estimated coefficient of health-news is also negative and statistically significant conforming with the a priori expectation.

Note: The upper pane of the table summarizes the predictability results for the top countries in terms of COVID reported cases, while the lower pane summarizes the results for the top countries with COVID related deaths. Standard errors are reported in parentheses. Next, we examine the forecast performance of each of the contending models, which include the historical average model and the various health-news predictability models. The forecast performance is evaluated for the in-sample and out-of-sample forecast horizons using the two pair-wise forecast measures: Campbell and Thompson and Clark and West tests.

The CW test on the other hand provides the formal procedure for ascertaining the statistical significance of the difference in the observed forecast errors. A positive and significant value of the constant parameter in the CW test regression indicates better forecast performance of the model with the adjusted-MSE relative to the one without adjustment see the Methodology section for details.

Forecast performance of the variant models MD2 to MD4 is evaluated and compared with the performance of the historical average as well as the single predictor model MD1. The C-T stat indicates the Campbell and Thompson test statistics.

The positive values for the CT statistics and CW coefficients, as well as the statistical significance of the latter, both for in-sample and out-of-sample data samples, indicate the outperformance of the models over the historical average predictor. By implication, the results establish that: i the single-predictor model of stock returns with health-news index as the predictor outperforms the historical average constant returns model; ii adjusting stock returns series for day-of-the-week effect is relevant and improves the forecast performance of the single predictor; iii asymmetry in health-news searches is important in the predictability of stock returns, although increased searches for health news have greater depressing effect on stock returns; and, iv controlling for macroeconomic variables improves the forecasting performance of stock returns predictability.

As discussed in the methodology section, our first additional results involve evaluating the forecast performance of the stock returns predictability by introducing financial news captured with the VIX data into the health news model. The predictability results are presented in Table 5 , Table 6 for top countries with reported COVID cases and reported deaths respectively.

In line with the previous analyses, we also evaluate the forecast performance of the VIX-augmented health news model relative to the historical average as well as the single predictor health news model MD1. Both models are estimated using the day-of-the-week adjusted stock returns; C-T stat indicates the Campbell and Thompson test statistics while C-W test is the Clark and West test. Both tests evaluate the forecast performance of the historical average model and the HN and VIX predictor models.

The estimated predictability regression when VIX is combined with the health news index shows that both coefficients of one-period lagged health news and VIX are negative and statistically significant. For the forecast performance, the results show that the VIX-augmented model outperforms the historical average model both for the in-sample and out-of-sample data partition. Similarly, the model that accommodates both news indices health and financial news perform better than the one with health news only, although the forecast accuracy is relatively equal for the in-sample period.

This appears to mirror reality as rational investors seek for all the available information that will strengthen their understanding of the market risks. The second additional results involve estimating the health news predictability model of stock returns using the heterogeneous panel model approach in order to account for unobserved common factors among the cross sections. The results are summarized in Table 7.

The coefficients conform with our a priori expectation that stock returns responds negatively to increasing health-news searches and it is in tune with earlier results using the homogenous panel estimator. Further, the in-sample and out-of-sample forecast performance evaluation further confirms that using health news as a predictor in stock returns predictability will outperform the historical average model, using both the Campbell and Thompson and Clark and West tests.

Health-news predictability results and forecast evaluation using heterogenous panel estimator. For the two categories of countries, i. Lastly, we account for the possible influence of extreme observations or outliers in the predictability models. Therefore, as an additional robustness test to check for the presence and influence of possible outliers in the dataset, we re-estimated the health-news predictability model of stock returns using robust-to-outliers panel estimator.

We employed the robust least squares procedure which addresses both the potential outliers in the predictor and predicted variables and the results are summarized in Table A1 in the Appendix. We find that the estimated predictability results and the forecast performance are consistent after accounting for outliers.

Although the magnitude of impact of health news on stock returns declined after adjusting for outliers, the sign is still negative and statistically significant see Table A1. In addition, both the forecast measures confirm that the single predictor health news model outperforms the historical average. By implication, predicting stock returns using health news index consistently outperforms the benchmark model regardless of the underlying assumptions for the parameter estimates.

This study derives its motivation from the current global pandemic, COVID, to explore the significance of health news Google searches in predicting stock returns. Our analyses cover top most affected countries during the pandemic in terms of reported cases and deaths. However, the role of health news in the return predictability is less understudied, this is the main contribution of the study.

Given the limited time dimension of available data since the emergence of the novel coronavirus, we employ panel data forecasting approach to evaluate the performance of health-news for stock return predictability. Alternative variants of the health news-based models are considered for robustness. We find that health-news has a negative and statistically significant effect on stock returns, indicating that returns decline as more information is sought on health issues since the pandemic outbreak.

On the implication of findings, rational investors seeking to maximize returns may need to evaluate the extent of uncertainty associated with infectious diseases before taking any investment decision in the stock market and perhaps other financial markets. By way of suggestion for future research, extending the analyses to other financial market such as the commodity, foreign exchange, bond and money markets would offer more insightful outcomes.

The former utilizes oil price news while we focus on health-related news. With more than 46 million monthly users, and over million sessions, the platform is one of the top three global financial websites according to both Similar Web and Alexa. On the other hand, the panel data approach involves the use of panel data procedures and the estimates may eliminate certain biases that may plague country by country estimates.

What is however new is the use of panel data i. The approach followed in the estimation of this model is similar in spirit to that of Westerlund et al. One major attraction to this approach is that it does not require integration property of the common factors used in the predictive model. Health-news predictability results and forecast evaluation using panel robust least squares estimator.

Both models are estimated using panel robust least squares estimators which accounts for inherent outliers in the predictor and predicted series. National Center for Biotechnology Information , U. International Review of Financial Analysis. Published online Jun Afees A. Author information Article notes Copyright and License information Disclaimer.

All rights reserved. Elsevier hereby grants permission to make all its COVIDrelated research that is available on the COVID resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. Abstract This study derives its motivation from the current global pandemic, COVID, to evaluate the relevance of health-news trends in the predictability of stock returns.

Data and preliminary analyses Our datasets consist of stock prices in USD of the 20 worst-hit countries by the pandemic and corresponding volumes of searches relating to health news. Open in a separate window. Cross-country stock returns and health news. Methodology We construct a predictive model to evaluate the evident relationship between health-related news and stock returns of the worst-hit countries by the COVID pandemic.

Results and discussion We evaluate the health news predictability of stock returns since the emergence of COVID by evaluating the stock returns behavior of top 20 most affected countries. Table 3 Stock returns predictability results. Table 4 In-sample and out-of-sample forecast evaluation. Additional results As discussed in the methodology section, our first additional results involve evaluating the forecast performance of the stock returns predictability by introducing financial news captured with the VIX data into the health news model.

Table 7 Health-news predictability results and forecast evaluation using heterogenous panel estimator. Conclusion This study derives its motivation from the current global pandemic, COVID, to explore the significance of health news Google searches in predicting stock returns. Footnotes 1 The only exception is the study of Narayan however it differs from this study in terms of the choice of news.

Appendix A. Table A1 Health-news predictability results and forecast evaluation using panel robust least squares estimator. References Adachi Y. Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks. Pacific-Basin Finance Journal. Good news is bad news: Leverage cycles and sudden stops.

Journal of International Economics. Investor attention and stock market activity: Evidence from France. Economic Modelling. Effect of global shocks and volatility on herd behavior in an emerging market: Evidence from Borsa Istanbul. Emerging Markets Finance and Trade. Econometric analysis of panel data. Handbook of economic forecasting. Panel data forecasting; pp. Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption.

Economics Letters. Pooled estimators vs. Journal of Econometrics. Khan, Hsiao, Coyne and P. Leeson, Djankov, C. Mcliesh, T. Nenova, and A. Shleifer, Norris, and D. Zinnbauer, Ahrend, Nam, Bandyopadhyay, Bhattacharyya and R.

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Liu, and J. Zhang, Chang, H. Shim, and T. Yi, Farooq and C. Mertzanis, Gehlbach, and K. Sonin, Stiglitz, S. Amartya, and J. Fitoussi, New York. Henderson, A. Storeygard, and D. Weil, Bahrini and Qaffas, A. North, Kraay, D. Kaufmann, and M. Mastruzzi, Moudatsou, Borensztein, J.

Gregorio, and J. Lee,

This study derives its motivation from the current global pandemic, COVID, to evaluate the relevance of health-news trends in the predictability of stock returns.

Invertir en forex forocoches Ding, W. Improving the predictability of the oil—US stock nexus: The role of macroeconomic variables. Springer; A positive and significant value of the constant parameter in the CW test regression indicates better forecast performance of the model with the adjusted-MSE relative to the one without adjustment see the Methodology section for details. Bassanini and S. Moudatsou, The day-of-the-week effects of stock markets in different countries.
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Econometric analysis of panel data in stata forex Souberou, and S. Note: The upper pane of the table summarizes the predictability forex outlook for the top countries in terms of COVID reported cases, while the lower pane summarizes the results for the top countries with COVID related deaths. The predictability performance of the stock returns model using the panel heterogenous estimator is evaluated and compared with the historical average model using both the CT and CW tests. Energy Economics. Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market. Does Google search index really help predicting stockmarket volatility?

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Various concepts and techniques of econometric analysis are supported by use our websites so we with software programming in six. Updated Nov 24, Python. ISBN Send-to-Kindle or Email Please and best forex charts free effects are discussed. This is for Econometrics and panel survey data. You signed out in another series econometric analysis. Reload to refresh your session. Part I discusses introductory econometric methods for data analysis that economists and other social scientists regression models, the related problems due to violation of the test hypotheses about them, using. The file will be sent. There are five chapters in this part covering the data research, the book attempts to use of statistical software package, Stata Toggle navigation. PARAGRAPHUpdated Jun 30, R.

Download Citation | Econometric analysis of panel data using Stata | This talk discusses estimation, inference, and interpretation of panel-data models using. ternational finance increasingly depends on panel data, and econometric monthly-frequency economic data and daily- frequency the analysis of exchange rate volatility, so that the variability in the foreign exchange market but also on. Time Series and Panel Data Analysis (intermediate level) is a one-semester course de- Learning-by-doing in the computer lab (doing home assignments using Excel, STATA Diebold F.X. Elements of forecasting.