Sensitivity to discretisation window offset: robustness results for message

The criteria for asset selection for this test of the robustness of the study’s results are the same as for Chapter 7.1.1.

Financial-instrument/Twitter-Filter combination A:

 For which a minimal quantity of information is added by message sentiment over what is attainable from message volumes in the study’s findings as listed in Chapter 6.3.2:

o The Home Depot, Inc. CFDs from the Ticker-ID AND/OR Company Name Twitter Filter;

o Information added by sentiment over message volume when evaluated against asset returns: 0.79%;

o Information added by sentiment over message volume when evaluated against absolute asset returns: 0.58%.

Financial-instrument/Twitter-Filter combination B:

 For which maximal information is added by message sentiment over what is attainable from message volumes in the study’s findings as listed in Chapter 6.3.2:

o J.P. Morgan, Inc. CFDs from the Ticker-ID AND/OR Company Name Twitter Filter;

o Information added by sentiment over message volume when evaluated against asset returns: 2.73%;

o Information added by sentiment over message volume when evaluated against absolute asset returns: 2.57%.

132

Financial-instrument/Twitter-Filter combination C:

 For which an intermediate amount of information is added by message sentiment over what is attainable from message volumes in the study’s findings as listed in Chapter 6.3.2:

o Apple, Inc. CFDs from the Ticker-ID only Twitter Filter;

o Information added by sentiment over message volume when evaluated against asset returns: 2.46%;

o Information added by sentiment over message volume when evaluated against absolute asset returns: 2.41%.

The results of this sensitivity experiment are given in the table below:

Present study's results

(On-hour discretisation offset)

Sensitivity experiment (+30-min discretisation offset)

Instrument and Twitter filter 

Maximum Information added by sentiment over message volume when evaluated against asset returns: Maximum Information added by sentiment over message volume when evaluated against absolute asset returns: Maximum Information added by sentiment over message volume when evaluated against asset returns: Maximum Information added by sentiment over message volume when evaluated against absolute asset returns: Combination A:

The Home Depot, Inc. CFDs. Ticker- ID AND/OR Company Name Filter:

0.79% 0.58% 0.78% 0.58%

Combination B:

J. P. Morgan, Inc. CFDs. Ticker-ID AND/OR Company Name Filter:

2.73% 2.57% 2.71% 2.57%

Combination C:

Apple, Inc. CFDs. Ticker-ID only Filter:

2.46% 2.41% 2.43% 2.39%

TABLE 23: SENSITIVITY TO DISCRETISATION WINDOW OFFSET: ROBUSTNESS RESULTS FOR MESSAGE SENTIMENT ADDING INFORMATION OVER WHAT IS ATTAINABLE FROM MESSAGE VOLUMES

Table 23 compares the results of experiments using on-hour data discretisation to comparative results under parameter variation (i.e., using a +30-minute discretisation window offset), for the Financial-instrument/Twitter-Filter combinations which are of relevance to this robustness experiment (as detailed in List 3). This is in relation to the

133

generalisation that message sentiment adds information over what is attainable from message volumes. The following observations are identified:

 For the asset for which a minimal quantity of information is added by message sentiment over what is attainable from message volumes in the study’s findings as listed in Chapter 6.3.2, i.e., The Home Depot, Inc. CFDs from the Ticker-ID AND/OR Company Name Twitter Filter, results from the +30-minute discretisation window offset experiments show that:

o Sentiment continues to add information over message volumes, as is the case with the on-hour discretisation window size experiments;

o More information is provided by the evaluation of message volumes against absolute asset returns, in comparison to evaluation of message volumes against actual asset returns, as is the case with the on-hour discretisation window size experiments;

o A similar quantity of information is added by sentiment over message volume when evaluating data discretised using a +30-minute offset, compared to when the data are discretised to on-hour windows. The percentage change between the quantities of information added by sentiment over message volume when using a +30-minute offset is - 1.27%, when compared to the quantities of information added by sentiment over message volume when using on-hour discretisation (0.79% to 0.78%, respectively). Therefore, it can be shown that discretisation of the study’s dataset using a +30-minute discretisation window offset data offset has minimal effect on the study’s findings for an asset for which minimal information is added by message sentiment over what is attainable from message volumes.

 For the asset for which a maximal quantity of information is added by message sentiment over what is attainable from message volumes in the study’s findings as listed in Chapter 6.3.2, i.e., J.P. Morgan, Inc. CFDs from the Ticker-ID AND/OR Company Name Twitter Filter, results from the +30-minute discretisation window offset experiments show that:

o Sentiment continues to add information over message volumes, as is the case with the on-hour discretisation window size experiments;

134

o More information is provided by the evaluation of message volumes against absolute asset returns, in comparison to evaluation of message volumes against actual asset returns, as is the case with the on-hour discretisation window size experiments;

o A similar quantity of information is added by sentiment over message volume when evaluating data discretised using a +30-minute offset, compared to when the data are discretised to on-hour windows. The percentage change between the quantities of information added by sentiment over message volume when using a +30-minute offset is - 0.73%, when compared to the quantities of information added by sentiment over message volume when using on-hour discretisation (2.73% to 2.71%, respectively). Therefore, it can be shown that discretisation of the study’s dataset using a +30-minute discretisation window offset data offset has minimal effect on the study’s findings for an asset for which maximal information is added by message sentiment over what is attainable from message volumes.

 For the asset for which an intermediate amount of information is added by message sentiment over what is attainable from message volumes in the study’s findings as listed in Chapter 6.3.2, i.e., Apple, Inc. CFDs from the Ticker-ID only Twitter Filter, results from the +30-minute discretisation window offset experiments show that:

o Sentiment continues to add information over message volumes, as is the case with the on-hour discretisation window size experiments;

o More information is provided by the evaluation of message volumes against absolute asset returns, in comparison to evaluation of message volumes against actual asset returns, as is the case with the on-hour discretisation window size experiments;

o A similar quantity of information is added by sentiment over message volume when evaluating data discretised using a +30-minute offset, compared to when the data are discretised to on-hour windows. The percentage change between the quantities of information added by sentiment over message volume when using a +30-minute offset is -

135

1.22%, when compared to the quantities of information added by sentiment over message volume when using on-hour discretisation (2.46% to 2.43%, respectively). Therefore, it can be shown that discretisation of the study’s dataset using a +30-minute discretisation window offset data offset has minimal effect on the study’s findings for an asset for which an intermediate quantity of information is added by message sentiment over what is attainable from message volumes.

Therefore, given the spectrum of assets considered by this robustness experiment (the criteria for which are identified in List 3), the study’s results for message sentiment adding information to what is available from message volumes are robust against variation in the discretisation window offset parameter when evaluated using a +30- minute offset. Even at +30-minute discretisation window offset, the study continues to identify that sentiment adds information over what is attainable from message volumes across the range of assets’ characteristics within the study’s dataset. Furthermore, this robustness experiment has shown that a discretisation window offset yields little change in the information added by message sentiment relative to message volume, regardless of quantity of information added by sentiment to message volume when originally discretised to on-hour windows. This indicates that the study’s results for message sentiment adding information over what is attainable from message volumes are robust against variation in the discretisation-window offset parameter under the conditions of the robustness experiment.

136

7.2.2 Sensitivity to discretisation window offset: robustness results for a greater