The group aggregated RWA calcs for a global tier 1 bank run substantially in arrears - perhaps as much as 2 months, and IIRC are submitted to the regulators quarterly.
The author is also using the word "spreadsheet" as shorthand to mean "the entire modelling infrastructure". For the trading book, this can be non-trivial.
"But Lazo thinks that neither the archival analysis nor the psychological experiments support the team’s conclusions. For a start, they analysed hurricane data from 1950, but hurricanes all had female names at first. They only started getting male names on alternate years in 1979. This matters because hurricanes have also, on average, been getting less deadly over time. 'It could be that more people die in female-named hurricanes, simply because more people died in hurricanes on average before they started getting male names,' says Lazo.
Jung’s team tried to address this problem by separately analysing the data for hurricanes before and after 1979. They claim that the findings 'directionally replicated those in the full dataset' but that’s a bit of a fudge. The fact is they couldn’t find a significant link between the femininity of a hurricane’s name and the damage it caused for either the pre-1979 set or the post-1979 one (and a 'marginally significant interaction' of p=0.073 doesn’t really count). The team argues that splitting the data meant there weren’t enough hurricanes in each subset to provide enough statistical power. But that only means we can’t rule out a connection between gender and damage; we can’t soundly confirm one either."
And BTW they just excluded Katrina as an outlier. While not necessarily a totally unwarranted decision, it makes already marginal conclusions that much more so.
Isn't this conclusion more significant because Katrina is excluded?
Edit: I meant the layman's sense of "significant" here. I just don't see how excluding Katrina "makes already marginal conclusions that much more so". I read your "but" statement as just saying that they haven't necessarily proven causality, but I don't see what that has to do with my statement.
Well, the word "significant" is a loaded one in this discussion -- in this context, it typically means "statistically significant," which has a very narrow (and sometimes questionable[1]) meaning.
You're right that leaving Katrina in the dataset (assuming the same techniques were used -- there are other ways to deal with outliers other than dropping them) would bias the result further towards indicating that female-named hurricanes are more dangerous. But the persistence of a significant finding in that regard in the absence of that data point does not prove a causal link, much less the specific one the author suggests.
It was intended more as a general point about picking and choosing of data. You're of course correct that including Katrina would buttress the female hurricane deadliness theory but would also open up the study to charges that it was influenced by a single outlier.
Whoa now, Lazo wants to say the experiment isn't useful because it uses undergrads who may not represent the actual demographic of interest? Does he want to abandon human-subject experiments altogether?
Abandoning the inexpensive-but-heavily-skewed "grab some of the local population" (of our university town) (in the Western world) wouldn't be a bad idea for psychology, no.
Dunno why you're being downvoted. That could well be the case. Back in my Accenture days, I recall senior managers going for over £2k a day, and that was 10 years ago. This is a very high-level executive, who can jump into the CIO role like this... they could well be paid more than £2k a day of net pay! (by Capita, not by the government)
I really did not expect that. That's one of the best unexpected funny things I've experienced this week. I don't think I would have found it as funny if I had stumbled on it randomly; the discussions here provided the perfect context for this link.
Interesting discussion here. A few things spring to mind:
- Consulting training still has an element of programming, but I suspect that will go when Core Analyst School moves to Bangalore later this year. The value is not that those guys will ever code, but to give them a feel for the types of problems that the engineers face.
- Solutions delivery (offshore or on) has pretty much taken development work off the plate of a consulting analyst.
- Consulting recruitment is swinging to favour engineering and technical disciplines more than it has historically.
- This group naturally favours high risk/high reward, deep technical competance, and engineering as a craft. This is antithetical to firms like Accenture that favour low risk (imply your own corrollary), relationships and business knowledge, and engineering as an industry. This is also favoured by our clients, which is why it's a very successful business.
- I've seen some projects in pretty dire straits, and I've seen over-committments. I've also seen some very effective cross-discipline teams dealing very well with difficult client situations. I've met a lot of very impressive people, and I've learnt a great deal from them.
- It's a truism that Accenture wouldn't be there unless there was a difficult business problem that the client felt that they couldn't solve on their own. Sometimes they couldn't have, but more frequently, in my experience, they could have done it themselves if it weren't for a paralysing fear of change.
- Internally, the firm changes org structure most years. This results in a very strong culture of personal network above business organisation. People are astonishingly willing to help someone they've never met, even when they are on the other side of the planet, and there's absolutely nothing in it for themself.
- I've never seen behaviour that I would regard as remotely unethical.
- The comment about NHS is right - Accenture UK took a massive financial hit, which resulted in a promotion freeze, and pay rises of less than inflation that year.
- My feeling at the moment is that Management Consulting will become much more distinct from Technology, which in turn will become more like a "normal" technology company.
Finally, it strikes me as a bit ironic that no one has yet highlighted the similarities between a "classic" Accenture project team and a startup. Both arrogantly believe that they can change things for the better by working very hard, learning a lot as they go and blending a variety of hard and soft skills. Sound familiar?
It sounds like you work for Accenture, out of interest, have you ever worked at a startup?
[Edit: I'm not asking to be snarky, just curious as I know a few people who have worked for big consulting firms and who now work for early stage companies and who relish the difference in environment. I was curious to see if anyone has gone in the other direction.]
I worked at Accenture for 10 years and am on my second yC-funded company after leaving there. While I would never go back to that environment, I praise it for what it is to this day. I strongly believe more than ever you just need to understand yourself and "how you're built" and choose the environment that is most appropriate for you. And that may change over time like it did for me. But some people don't like the risks inherent in startups, or they need more structure to their day and know they don't do good with minimal direction and if that's the case, a place like Accenture is a pretty good place to work.
I wouldn't give back the skills I learned at Accenture and they've been invaluable for working on Inkling, our first B2B business. From day 1 I knew how to deal with large corporate cultures, understood the procurement process, how to manage projects, create a budget, write proposals, deal with various personalities, run a meeting and conduct workshops, conference call etiquette. It may sound simplistic, but I can't tell you the number of deals I've seen blown up simply because people don't really understand the business of business. Those skills are a lot harder to pick up in a startup.
I've done what I'd call an "internal startup", which was successful, but with which I no longer have a great deal of involvement. I used a few hundred k of seed funding and a team of 4 to build a web app over about 4 months. It's in use at about 20 customers internationally, and is now delivered as a service model, sold internally through cross charging. i.e. we deal with internal partners and they do their own deal with the customer. I like to think it gave me a feel for some of the issues faced when doing a startup, but without many of the risks.
