Online Appendix: High Frequency Newswire Textual Sentiment Analysis

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High Frequency

Online Appendix: High Frequency Newswire Textual Sentiment Analysis: Evidence From International Stock Markets During The European Financial Crisis

Andreas S. Chouliaras

Luxembourg School of Finance

December 31, 2015

Abstract:

Top value fund managers are ready for the small cap bear market to be done

InvestorsDuring the bull market, small caps haven't been performing well, but some believe that could be about to change. Breach Inlet Founder and Portfolio Manager Chris Colvin and Gradient Investments President Michael Binger both expect small caps to take off. Q1 2020 hedge fund letters, conferences and more However, not everyone is convinced. BTIG strategist Read More

This is the internet appendix for “High Frequency Newswire Textual Sentiment Analysis: Evidence from International Stock Markets during the European Financial Crisis”.

The paper “High Frequency Newswire Textual Sentiment Analysis: Evidence from International Stock Markets during the European Financial Crisis” to which this Appendix applies is available at: http://ssrn.com/abstract=2572597.

Online Appendix: High Frequency Newswire Textual Sentiment Analysis: Evidence From International Stock Markets During The European Financial Crisis – Introduction

Table 1: Portugal Stock Market – 30 minutes. The model I employ the study the effect of the content of news in high frequency stock returns is the following: where Mt takes the value of, the positive (Gt), the negative (Bt), the pessimism (Pt) and the news count (Nt), of the previous 30-minute interval, as defined in Section 3.5 of the paper. I control for five lags of returns (i.e. five 30-minute lagged returns for every stock market) to deal with autocorrelation in the returns. The regressions I perform are robust, using the Huber-White sandwich estimators (Huber (1967), White (1980)) to deal with autocorrelation, heteroskedasticity, heterogeneity and lack of normality.

High Frequency

Table 2: Ireland stock market – 30 minutes. The model I employ the study the effect of the content of news in high frequency stock returns is the following: Screenshot_6 where Mt takes the value of, the positive (Gt), the negative (Bt), the pessimism (Pt) and the news count (Nt), of the previous 30-minute interval, as defined in Section 3.5 of the paper. I control for five lags of returns (i.e. five 30-minute lagged returns for every stock market) to deal with autocorrelation in the returns. The regressions I perform are robust, using the Huber-White sandwich estimators (Huber (1967), White (1980)) to deal with autocorrelation, heteroskedasticity, heterogeneity and lack of normality.

High Frequency

Table 3: Italy stock market – 30 minutes. The model I employ the study the effect of the content of news in high frequency stock returns is the following: Screenshot_6 where Mt takes the value of, the positive (Gt), the negative (Bt), the pessimism (Pt) and the news count (Nt), of the previous 30-minute interval, as defined in Section 3.5 of the paper. I control for five lags of returns (i.e. five 30-minute lagged returns for every stock market) to deal with autocorrelation in the returns. The regressions I perform are robust, using the Huber-White sandwich estimators (Huber (1967), White (1980)) to deal with autocorrelation, heteroskedasticity, heterogeneity and lack of normality.

High Frequency

Table 4: Greece stock market – 30 minutes. The model I employ the study the effect of the content of news in high frequency stock returns is the following: Screenshot_6 where Mt takes the value of, the positive (Gt), the negative (Bt), the pessimism (Pt) and the news count (Nt), of the previous 30-minute interval, as defined in Section 3.5 of the paper. I control for five lags of returns (i.e. five 30-minute lagged returns for every stock market) to deal with autocorrelation in the returns. The regressions I perform are robust, using the Huber-White sandwich estimators (Huber (1967), White (1980)) to deal with autocorrelation, heteroskedasticity, heterogeneity and lack of normality.

High Frequency

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