Herding in financial Market: How to do it? – Herding/ Mengekor di Pasar keuangan: Bagaimana melakukannya?

Herding in financial Market: How to do it?

Remember a folk story about pied piper of Hamelin (https://en.wikipedia.org/wiki/Pied_Piper_of_Hamelin) ? Once upon a time, Hamelin town was full of rats. Yet, the pied piper has solution for this rat issue. He promised to drowned all rats and the city has to pay him 1000 golds. After mayor of Hamelin agreed, the piper played his pipe and lured the rats into Weser river, and all rats were drowned (except one rat). How? All rats HERD him because of the music. I don’t want to continue the story because it is a sad story.

This herd behavior has also occurred in the financial market. Yes, herding is the trading behavior of investor by copying the behavior of other investors. Continue reading

Women on Board Effect: How to do it? Perempuan dalam Dewan Direksi: Bagaimana melakukan risetnya?

Women on Board Effect: How to do it?

Women on Board research is an interesting research considering the issue of gender equality. However, the role of women in firms has not extensively and empirically discussed like other corporate governance issues. Research has found that women may change the board room situation because women are more cautious than male on board in corporate decisions making (Huang and Kisgen, 2013 and Levi et al., 2013). Laura Tyson, a former economic adviser to US President Bill Clinton, recommended the improvement of board diversity to increase firm’s financial performance through the number of women on board (Tyson, 2003). Adams and Ferreira (2009) studied 1500 companies at United States from 1996-2003, and found female directors have significant impact on corporate performance of firms with weak shareholders right. They stated that female directors are more likely to monitor their committees. As the greater the gender diversity, the directors have fewer attendance problem. Therefore, this issue should be explored more in the future
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How to transpose your rich data into panel (Data Stacking / Transpose)

If you are in finance research area, and you are using panel data, the most annoying part is stacking/transpose your time series into panel data. Hereby, I give you the macros in excel (even though I know many software such Tableau can do it faster) to do in a click.

Yet, when you want to run it, adjust it with the number of your column. Mine is 6, and the data that has to be stacked/transposed is from column 2. Therefore, the i = 2 to 6.

Happy trying

Copy paste this to your macros (open your excel –> view –> macros –>view macros –> create –> copy paste –> run)

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Why You should attend Statistical / Econometrics Class before Mastering a Software

I just read comments from 2 reviewers of a journal. The first reviewer gives a minor correction because of English editing, copy editing, and elaboration in the discussion section. The first reviewer gives good comments too, for instance, we have to have another robustness test, which is a very good idea to make our paper better. The second reviewer gives a rejection because two issues. First, he/she suspect we just wrote down the R-Square (R2) before running the factor analysis. I just stunned, and read it again and again. R2 from factor analysis? Second issue is about the data collection. He/she addresses our manuscript is too weak as it does not mention the philosophical data collection. I was confused that time thinking what the reviewer means by philosophical data collection. After reading it again, reviewer mentions that I have to write down, not only whether we adopt or adapt the items of questionnaire, but also the grounded theory of the data collection and the phenomenological of items construction. I was very surprised because actually grounded theory data collection and phenomenological are qualitative approach which is totally different view from our approach which is quantitative method. I just smile facing this type of reviewing process. But it makes me think what the root of this “evil” is. Why nowadays people are good in click-click the software interface but fail in understanding comprehensively the statistic. Continue reading