One possible explanation (pure conjecture) is that this is a consequence of the still prevalent sexist/old-boys-club mentality. Only the women who are so exceptional that there can be no question about their capabilities are the ones who end up on top. And the women who are equally qualified as the typical male CEO simply don't make it through the gauntlet.
This is an effect that minorities face in general. They have to be the absolute best in order to break through the glass ceiling. There are plenty of male CEOs who are utterly mediocre, and mediocre women never get a shot at the CEO spot.
Or, female CEO's are a more risky prospect than male CEO's and companies have different risk/reward motivations based on their situations. If a company's facing a tough grind for a few years they may be risk averse and stick with "safe bets" wherever they can.
Or, years of fighting "the patriarchy" has left females uniquely qualified to lead companies in the fight against changing market conditions.
Or, prospective female executives are probably very aware of the social and political climate we live in and that may make them more risk averse in regards to accepting positions, they may place a higher value on taking positions at companies on the upswing.
We can do this all day but I doubt the data's going to give a clear answer, we'll all see what we want to.
A much more likely explanation is selection bias. There would never have been a post like this if the result would have the other way around. Statistical variation makes it easy to find a starting point to show what you want.
I like your theory. Whenever there's talk of some sort of affirmative action, some people say "Imagine getting your job just because you're a woman". Well with this theory, there are men getting CEO job partially because they are men, even if they aren't very good.
There's no discussion here of survivorship bias [1]. Without a discussion of how it is addressed, these results are highly suspect. Specifically, the benchmark takes into account those companies that lost enough value to fall out of the "bottom" of the index and are therefore not in your current list of companies. However its far from clear that your subgroup includes similar companies.
A naive approach to this analysis is as follows. Take all the companies currently in the Fortune 1000, and look to see which of them has had a female CEO in the period under study. Backtest with buys and sells on the dates of hire/termination.
If this is indeed how it was done, the benchmark will almost assuredly underpeform simply because of the underperformance of components that are no longer in the index today.
Looking at your graph, it's clear that you need to adjust for volatility. Your "Women CEO" portfolio is much more volatile during that period. Compare Sharpe ratios, not total performance.
After that, you may have to worry about dividends. I'm not sure if the platform takes them into account when you send buy sell order, but if it does, make sure you look at a portfolio that purchases SPY over that period, not merely the price, as the appreciation won't reflect the dividends paid.
The Quantopian backtester pays dividends out in cash, and since I am rebalancing the portfolio each time I buy or sell stocks, I think that is covered.
Looking at the volatility is a good suggestion and definitely something to consider. I noticed the trend as well, but haven't dug into it yet.
I finally got the annualized sharpe calculation figured out. You can see what I did here: http://imgur.com/HlcjNYz but it shows my sharpe as 0.66 and the SPY as 0.3....
Honestly, this seems implausible to me...so I will keep digging. :-)
The conclusion I'd draw that female CEOs are a indicator of a company culture likely to be more accepting / more meritocratic / less nepotism internally which likely makes for a for a more agile company.
Regardless, stats like this hopefully help erode long standing views of women and leadership.
If you plot based on blood types of CEO's rather than gender, you may get other type of results. So should we take blood type as another type of parameter like gender?
Point I want to make is, people are different starting from blood type, height, weight to "n" number of parameters and we can get different graphs for different parameters but that won't conclude anything.
Each person is different and just because some one belongs to a category does not make that person any extra attractive for any job.
In my personal opinion, this type of validation rather than competence,track record,experience is not beneficial to organization or society and completely shortsighted. So please stop these generalizations like "women are so and so", "men are so and so" ...etc. There are good and bad elements in every category.
I think you're looking at the trees, and you should step back and look at the forest.
The fraction of CEOs that are women is dramatically smaller than the fraction of the population that are women. There is no qualitative explanation as to why that should be true. So long as that remains true, it's worth looking into why it is true. The relative performance of the group is fair game for investigation.
This reminds me of a study I did on CEOs who came to speak to my university. I found that investing in a company if their CEO was visiting college campuses averaged an annual return of ~20%. The logic was that CEOs of poor performing companies didn't have time to visit schools.
For a disclaimer, the portfolio was also more volatile than most benchmark indexes, so looking at the Sharpe ratio in both instances (mine and OP's) is important.
In the first cut of your analysis (without rebalancing) the portfolio actually goes negative (I'm looking at the first graph in the notebook). An equity portfolio cannot go negative unless you are using leverage. Then from late 2012 to 2015 the portfolio rises by 400%. Looking at the companies in the portfolio, I don't see how they could have generated that kind of return. Is this algorithm using leverage? If not, I don't see how you can get those results. If the algorithm is using leverage, then comparing to a simple investment in the S&P is not valid.
You are absolutely right that the first version of my algo is using tons of leverage. I didn't realize what leverage was when I did this the first time.
The second version of the algorithm factors in leverage and there it hovers around 1.
"Ethical" investment funds also did well for a while. Even if the women in this study are better CEOs (and that share price reflects this) any attempt to capitalize on this will change the population. ie assume equal talent pools, top 30 from each beat top 60 for either.
This is really cool, but I'd love to see this narrowed down to something super scientific. Cause I can already hear the haters, screaming about control groups, sample sizes, and all that stuff. Which is actually important.
Right now I take it for what it is a quick mock up of some shallow data. But it seems compelling enough to take a deeper Nate Silver ( http://fivethirtyeight.com/ ) style deep dive.
I’d love suggestions about how to make this more scientific. I think the biggest potential pitfall is the variability related to the stock size. My approach is pretty simple right now. I just took all of the companies from the Fortune 1000 that had female CEOs since 2002, and bought their stock when they were women-led, and sold it when they no longer were.
From an investment perspective, I think the best thing to do next would be to make sure I have the right benchmark. Ideally they Fortune 1000 would be the best one to use, but I need the historical Fortune 1000 companies for the last 12 years….that will take some manual work to pull together.
At some point, though, the control group is an academic exercise. If the strategy makes money - invest.
gizmo|11 years ago
This is an effect that minorities face in general. They have to be the absolute best in order to break through the glass ceiling. There are plenty of male CEOs who are utterly mediocre, and mediocre women never get a shot at the CEO spot.
stolio|11 years ago
Or, years of fighting "the patriarchy" has left females uniquely qualified to lead companies in the fight against changing market conditions.
Or, prospective female executives are probably very aware of the social and political climate we live in and that may make them more risk averse in regards to accepting positions, they may place a higher value on taking positions at companies on the upswing.
We can do this all day but I doubt the data's going to give a clear answer, we'll all see what we want to.
BoardsOfCanada|11 years ago
rmc|11 years ago
gjem97|11 years ago
A naive approach to this analysis is as follows. Take all the companies currently in the Fortune 1000, and look to see which of them has had a female CEO in the period under study. Backtest with buys and sells on the dates of hire/termination.
If this is indeed how it was done, the benchmark will almost assuredly underpeform simply because of the underperformance of components that are no longer in the index today.
[1] http://en.wikipedia.org/wiki/Survivorship_bias
murbard2|11 years ago
After that, you may have to worry about dividends. I'm not sure if the platform takes them into account when you send buy sell order, but if it does, make sure you look at a portfolio that purchases SPY over that period, not merely the price, as the appreciation won't reflect the dividends paid.
karenrubin|11 years ago
Looking at the volatility is a good suggestion and definitely something to consider. I noticed the trend as well, but haven't dug into it yet.
karenrubin|11 years ago
I finally got the annualized sharpe calculation figured out. You can see what I did here: http://imgur.com/HlcjNYz but it shows my sharpe as 0.66 and the SPY as 0.3....
Honestly, this seems implausible to me...so I will keep digging. :-)
thorwaaonawngo|11 years ago
fuzzywalrus|11 years ago
Regardless, stats like this hopefully help erode long standing views of women and leadership.
mkr-hn|11 years ago
patio11|11 years ago
jjoonathan|11 years ago
q2|11 years ago
Point I want to make is, people are different starting from blood type, height, weight to "n" number of parameters and we can get different graphs for different parameters but that won't conclude anything.
Each person is different and just because some one belongs to a category does not make that person any extra attractive for any job.
In my personal opinion, this type of validation rather than competence,track record,experience is not beneficial to organization or society and completely shortsighted. So please stop these generalizations like "women are so and so", "men are so and so" ...etc. There are good and bad elements in every category.
dunster|11 years ago
The fraction of CEOs that are women is dramatically smaller than the fraction of the population that are women. There is no qualitative explanation as to why that should be true. So long as that remains true, it's worth looking into why it is true. The relative performance of the group is fair game for investigation.
jayshahtx|11 years ago
For a disclaimer, the portfolio was also more volatile than most benchmark indexes, so looking at the Sharpe ratio in both instances (mine and OP's) is important.
unknown|11 years ago
[deleted]
bokonist|11 years ago
karenrubin|11 years ago
The second version of the algorithm factors in leverage and there it hovers around 1.
cowardlydragon|11 years ago
cowardlydragon|11 years ago
dolant|11 years ago
tenpoundhammer|11 years ago
Right now I take it for what it is a quick mock up of some shallow data. But it seems compelling enough to take a deeper Nate Silver ( http://fivethirtyeight.com/ ) style deep dive.
karenrubin|11 years ago
From an investment perspective, I think the best thing to do next would be to make sure I have the right benchmark. Ideally they Fortune 1000 would be the best one to use, but I need the historical Fortune 1000 companies for the last 12 years….that will take some manual work to pull together.
At some point, though, the control group is an academic exercise. If the strategy makes money - invest.
erikpukinskis|11 years ago
sample_size == population_size