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?
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
Temperature Effect: How to do it?
Weather effect is another interesting research in finance. It is actually an argument for the traditional finance that relies much on rational behavior assumption. So, whenever you write problem statement for this topic, start it with the argument on rational behavior. I firstly read paper of Saunders (1993) then followed by Dowling and Lucey (2005). It really attracted me. How come weather may affect stock price? Right?
Who support it? A lot! For example, Loewenstein (2000) argues that the emotions and feelings influence our decision making process. It may dictate our long-term cost and benefits, hence, it will affect the equity pricing. The logic is simple, when it is rainy day, we tend to feel lazy compared to bright day (we tend to be aggressive). This emotion may affect our decision making. If it is rainy day, we tend to have our leisure time, meanwhile, during sunny day we tend to be rushed out (Yet, this debatable even in psychology field). Continue reading
Moon effect Research: How to Do it?
- NOTE THAT NOW I AM MORE ON BEHAVIOURAL CORPORATE FINANCE –
The moon effect research in economics and business field is interesting and intriguing. I met this topic by coincidence. That time I was solely on weather effect reading papers from Cunningham (1979) and Dowling and Lucey (2005). I remember that I prepared my manuscript for Journal of Bioeconomics, opened this journal to see its scope, but ended up with Herbst (2007) paper. It really attracted me.
When I read it, it was related to my PhD research (that time) and I wanted it to be part of my regressor. The issue is what theory to support it? How to measure it? How to rationale it? It was painstaking, but it has to be done. Continue reading
In accounting and finance (and indeed in other research areas too), there is a possibility for having a categorical dependent variable. For instance, research in auditor opinion or efficiency or board size determinants makes our dependent variable to be binary. However, we are afraid that there is no variance in our categorical dependent variables due to our panel data set. Then the question is “Can we run panel logistic regression?”
By far, my answer is still yes. Indeed, there is possibility of estimation bias due to the variance and standard errors post-estimation. So, how can we run panel logistic regression? Continue reading
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.
Copy paste this to your macros (open your excel –> view –> macros –>view macros –> create –> copy paste –> run)
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