ING, shoes, and are the big guys completely honest with their customers?

In one of my last entries, I was briefly noticing that (grad) students should not invest in stocks - unless they are financially strong. Being such implies having the financial security - as defined by Bodo Shaefer - i.e. the quantity of money, liquid, that would be enough for one person to live for a year without working or receiving money from other sources.

This is an essential thing to do many people neglect and, say, in Russia almost no one cares about. When I first came to Catalonia four years ago, the first thing I learned was to save. I was reading a lot of Kiyosaki back then, as well.

In money terms, the financial security amount differs from person to person, but basically it is calculated as a sum enough to provide some elemental living during one month multiplied by 12. No luxuries, travelling or gifts are included into this quantity - unless they are vital for your existence.

This money must be never touched, spent or invested into anything implying any risk - you cannot lose it. It must be always there for you in case of an emergency, e.g. job loss (that hopefully will never occur).

I am sorry if I am explaining the very basics to you, but you won't believe how many people are actually unaware of this simple truth.

What is the best way to accumulate this quantity? Saving. The recommended share is 10% of monthly incomes. There are three possible ways to save that I see, may be, you can name more. One is a la antigua: you can have your treasure stashed under a mattress. The other one is to give it to your Mum/friend/significant other who you completely trust and who, unlike yourself, would never fall into temptation to spend this money. The third one, and the one that I personally recommend, is to open a savings account. Not only will it keep your money protected from loss, robbery or fire but also it will generate interests.

In Spain, the best bank for the purpose - according to the word of mouth - is ING Direct. This mogul of  the Dutch origin has its branches across the globe. In Spain, it offers a very attractive product called  la Cuenta Naranja ("The Orange Account"). Back when I contracted it in 2011, it provided 4% APR (TAE in Spanish) during the first 4 months with a decrease down to 1.20% APR during all the consecutive months. Now, according to the latest ads, Cuenta Naranja promises 3.30% APR during the first 4 months, and this less attractive stake must be the consequence of so very often mentioned Spanish crisis.

Spain is in Europe, so you would imply that due to the heavy corporate and governmental regulations no fraud can occur anywhere - with the exception of the very high level politicians. However, a recent event of my life made my disbelief in the latter statement even stronger.

I was shopping in the city, and I popped into a store of an Inditex brand retailer, Uterqüe, and I saw some fancy boots. The was a sign right next to them indicating that they were on 50% sale. Winter sales start on January, 7, in Spain, a month from then, so I was eager to treat myself with a little bargain before the global shopping spree.  When I approached the pay desk, I have discovered that the announced price did not make up 50% of the initial amount, it was more. When I asked the cashier if I had some problems with arithmetic, or was it them,  she turned all shy and said: "Yeah, may be the discount is slightly less than 50%..." I bought the shoes anyway.

The next day, however, I came back to the shop for the proofs of that the promotion was misleading, and they had already covered the initial price with an opaque seal. No matter what, I made some photos with my iPhone and vanished rapidly. Here are the proofs:

Well, after coming across this little cheat, I was a little disgusted, and the next thing that occurred to me was to check whether my ING bank account pays me the interests it promises. I must say that I don't really care about the interests because I am intended to make my financial security with my savings, and I am close to my goal. Nonetheless, it was a tempting basic finance exercise, and I had some free time so I did it.

I've modeled my personal wealth dynamics and the interests compounding on the basis of the promised: 4% TAE (3.928% nominal interest) within the first 4 months, and 1.20% TAE (1.196% nominal interest) afterwards. I believe you understand why I don't provide the actual money values on these graphs.

The fall in wealth curve in March 2011 is explained by the fact that I paid for my studies in the UK from this account which I shouldn't have done but still did. 

In a nutshell, almost for every month, the paid interests were slightly smaller that those promised. It was not exactly 4 per cent the first four months, and not 1.20 per cent later - it was a little less. However, as you can see, in the global perspective the difference is negligible. Also, there may be something in the ING Direct's interest calculation methodology that I perhaps don't know. My calculations were pretty straightforward: initial wealth, interests compounding, new contribution, interests compounding and so on. The monthly contribution has almost always been pretty constant: 10% of my monthly income. By the end of october 2012, the difference between the actual wealth and the modeled wealth, i.e. between what I owned and what I should have been owning, made up just 0.45% of the actual accumulated wealth.   

So, to make a long story short, as a Russian person who does not care much about the small things, I give the Cuenta Naranja my seal of approval, and I am not intended to switch the bank.  

Shoes price, however, is a philosophy question. Name a fair price for a pair of shoes. 

Dr. What the thesis was about

On Tuesday, I've defended my thesis, and therefore I've become a doctor. They've graded me with an "Excellent", but I still have no idea what that would mean. Neither have I an idea whether it is relevant for my future career. I only know this is a European doctorate program, and therefore - a European PhD title, which means that I've spent roughly 1/4 of my PhD time abroad, learned a lot, met great people and had a lot of fun. The other 1/4 of my PhD time it was summer in Spain, so you can imagine, right? In a nutshell, these last three years have been pretty awesome.
Hereby, I provide a presentation with some brief highlights of the matter of my thesis. This is a shortened version of my actual PhD thesis presentation, a half of it, to be precise. My report lasted approxinately for an hour followed by another hour of questions.

Short version of the PhD thesis presentation by Olga Ivina

The basic idea of the dissertation is to provide valid predictions of air pollution concentrations even in conditions of a severe lack of observed data. This is no news that air pollution influences adversely people's health and well-being. Both adults and children are affected, and both short- and long-term exposures lead to health effects. This is why air pollution assessment methodology is being constantly updated. There is a bunch of methods nowadays that serve for the purpose, but, roughly, they can be divided into two groups: cheap to implement, and expensive to implement yet extremely precise. The latter is a desiderata, of course, for all the methods. Sometimes, the prediction may lack validity. Especially when the actual concentrations at some points of the map (say, specifically where a person with some health effect lives) are not available. Then, the prediction error cannot be properly assessed. 

In my case, I had a tiny data set of mean annual concentrations of some contaminants. Those major contaminants can be altogether referred to as "criteria pollutants". Two most investigated of them are nitrogen dioxide and fine particulate matter, and these are the two that I have taken up for my study. For each of them, I had the annual values measured at the monitoring stations across the Barcelona Metropolitan Region. There are 49 stations in total, and for every year and for every pollutant the measurements were available roughly at 24. So, there were 24 points on the average for every year and pollutant. 

In order to provide a valid prediction for pollution surface for every year and pollutant, conformal predictors have been employed. It is a technique that has been recently developed by people at the Royal Holloway University of London, more precisely, in its unit called Computer Learning Research Centre. This is a machine learning method, and it comes from statistical learning theory. A conformal predictor is always valid, and it can be build upon almost any statistical algorithm, including, of course, regression - the one that is majorly used for air pollution modeling. 

A conformal predictor has been derived on the basis of a classic kriging: this method has been chosen because of the given data configuration. The next step is to derive an anisotropic approach for  kriging, once more data is available. Also, a conformal predictor on the basis of the most (I'd say "pop", but it is a serious blog) frequently used algorithm, land use regression is on the way.

If you have any questions, please do not hesitate to ask.

P.S. Now I am back to financial data analysis. My next post will be about  personal income savings, the most popular bank in Spain for this purpose (in my perception)and its efficiency as such. 

What I Did Last Saturday (feat. Banc Sabadell daily data)

So, some time ago I've made a decision that my ideal job situation would be to stay in Barcelona and to work as a quantitative analyst, otherwise known as quant.  However, it seems that in Barcelona the term "quant" barely exists. These people are as rare as white truffles, and the algorithm of becoming such in this city is nondeducible.

A context search through LinkedIn has elicited that quants mostly inhabit at a Spanish fourth largest bank, Banc Sabadell (a Catalonia-based bank).

Banc Sabadell does not seem to be needing extra quants, or at least, it does not post such offers online. But what it does post is some amazing piece of marketing featuring my glorious compatriot Yuri Gagarin:

photo - courtesy Flickr of Banc Sabadell

The text below Gagarin's photo says that Banc Sabadell is the first Spanish bank to offer 24/7 customer service via twitter. So, they're not only the first to hire quants. The ads is impressive, right? I headed to Banc Sabadell's webpage in order to try to apply for a job. I haven't discovered any relevant offer, but what I did discover was the open-source data on their stock prices. Which is valuable since you cannot get this data from Yahoo!Finance: the ticker "SAB" is used for another company on global markets.

I downloaded the daily close prices for the last 4 years. Within this time frame, the price of a share has decreased from €5.57 to €1.26. Which is understandable considering the general recession of Spanish economy. Banc Sabadell is one of the 35 enterprises to be considered in the main Spanish market index, IBEX35. Here is the graph performance of both during the last 4 years:

Banc Sabadell daily stock prices show a constant decrease over time. IBEX35 movements have followed a similar path but with more pronounced ups and downs. This is explained by the fact that IBEX35 considers performance of another 34 companies. Also, the index is based on pondered market capitalization of the businesses, not their stock prices. 

As I have already mentioned before, I've been doing a course on financial econometrics on Coursera. While the majority of methods is a repetition of what I had already studied as part of my university courses on financial mathematics and econometrics, some approaches are really new to me. For instance, the constant expected return model was the thing I have first come across during this course.  I decided to test the approach on the Sabadell data. 

First, I have derived the continuously compounded returns for the shares. The data as it comes from Banc Sabadell server contains missed values - in the sense that observations are absent for some days. Therefore, to obtain regular time series (suitable for prediction and fitting models like ARIMA), I have converted the daily returns data to monthly averages: 

It is seen that the mean value of continuously compounded returns is a tiny bit below zero, but practically these returns are almost zero. Which can be confirmed by a t-test: 

t = -0.5601, df = 48, p-value = 0.578

true mean: -0.0005466

The 95% confidence interval for the mean is: -0.0025 to  0.0014. The plots for autocorrelation and partial autocorrelation functions reveal no significant dependencies on previous observations: 

Thus, these monthly cc returns behave like a realisation of a white noise process. ???

Is it s Gaussian white noise?

If I would like to fit a constant expected return model for this data, I would check the assumption of normality of the residuals, otherwise denoted as "random news shocks". The Jarque-Bera test suggests the rejection of the hypothesis of normality: 

Chi-squared = 44.65, df = 2, p-value = 2.02e-10

A Gaussian process with the same mean value (-0.00055) and the same standard deviation (0.0068) as those of the monthly cc returns can be simulated. And a constant expected return model can be fitted: 

Then, the histograms of the real and the simulated data can be compared. It is clearly seen that the Banc Sabadell monthly continuously compounded returns do not behave as normal. 

While the simulate data is skewed but still oscillates around zero, the real data does not. It's sample mean, median and mode are a tiny dat but below zero. Almost every month, the observed average continuously compounded return is about -0.1%, which  explains the slow but constant decrease in stock prices. The maximum observed monthly return is 2.6%.

  

If I would have purchased some Bank Sabadell shares for €1000 in October 2008 - and I had not yet even started grad school then, so I couldn't afford a bigger investment anyway - what would this investment provide me right now?

First, lets check the historical VaR:

The 1% and 5% quantiles for daily continuously compounded returns are equal to -0.055 and -0.034 respectively, and thus the (daily) VaR values would make up €54.03 and €33.59. For monthly continuously compounded returns, monthly 1% and 5% historical VaR is €11.9 and €8.47 respectively which is way smaller.

The actual performance of the shares can be tracked back using a simple loop. If I would have invested €1000 in these shares in October, 2008, i.e. 49 months ago, considering real monthly returns, now I would have some €984. This seems not bad but somewhat doubtful since the real price of the shares has decreased more than 4 times in this 4 years. 

Indeed, if the daily real returns data is used, and the performance of the €1000 investment is evaluated for the 1016 days time frame (the number of available daily observations is 1017), on October 29, 2012 I would have €373 and 30 cents. Which seems correct.  So, having invested €1000 back then, I would have lost €627. Which for a grad student is a solid amount. 

Moreover, I am inclined to think that students should not invest their money in stocks at all. Well, unless they are filthy rich. Instead, they should find a bank that offers them a savings account with a fixed and stable interest rate, they should pay some 10% of their income into this account every month, and not ever touch the invested until... well, I'm going to dig into this in a separate blog entry, perhaps.  

RESUME: Banc Sabadell's shares perform no worse than the Spanish economy does. Average monthly returns data is confounding since it is too smoothed to yield correct loss estimates. Also, the volatility/bias of the average monthly returns is to be better investigated. Daily data as it is, however, contains missings and thus is unsuitable for regular time series modeling. This is a problem of a data scientist, however, not the bank. I know.

Doctor in less than a month!

Announcement 1: on November, 20, I will be presenting my PhD thesis that goes under the headline "Conformal prediction of air pollution concentrations for the Barcelona Metropolitan Region". If you happen to be in Girona that day, you are so very welcome to come.

In a month, hopefully, I'll be a doctor. I'll be able to update my LinkedIn profile and buy the relevant Ryanair tickets. They normally ask how to treat you: Ms. Mrs. or Dr., and it is a good tradition amongst the EU freshly made doctors to buy the "Dr." Ryanair tickets right after becoming such.

I will be travelling to Germany, Berlin, if you may ask.

Announcement 2: Owing to the current events of my life, I have changed my focus from epidemiology and health back to financial mathematics. I am a quant now. I've loved being green for a while, though. I've contributed to better world and cleaner air. Now I can go pure capitalist.

For about two months, I've been enrolled to the Coursera's course (sorry) named "Introduction to Computational Finance and Financial Econometrics" I am so impressed with the course and Dr. Eric Zivot himself! An amazing, very comprehensive, course explained by a gifted lecturer, and also the content is so aplicable to real life - what cannot be always said about the "classical" math courses. It is like the kitchen recepies: just pour your data in the pot and follow the instructions provided.

Above that, I've been reading a lot about all of the branches of quantitative analysis: starting from the options pricing theory to the V@R modeling techniques. It feels like going back to the University  years when I still remembered what "vanilla options" are and how to program on C++.

Exciting!

This book is next on my reading list: just found it in the UdG library.

Common Air Quality Indicator for everybody?

At the present moment, I've been intensively working upon a comparative analysis of air pollution in various places in the world. J'adore voyager, so I've been wondering how's the air around the globe - and why. It is interesting to learn, why does the air quality vary from country to country, and what factors influence this variance.

I absolutely know that traffic is the major contributor to air pollution - this is what the evidence states. Nonetheless, higher mobility implies higher development, and higher development, in its turn, implies concern about health and environment. "Sustainability" and "concern" are two keywords for explaining the issue, no doubt. However, these two factors are latent, and there is no formula to describe them analytically. And they surely have a connection to the quality of the environment.

Not only there is no direct manner to explain why one nations inhale cleaner air than the others, but also there is no common measure for air quality! Various countries have various indices to report on air pollution, and, although there are similarities in the methodology of their construction, these indices are difficult to be narrowed down to one global measure and scale.

The disparity in methods  aimed to keep track of air pollution in the world can be caused by the difference in concerns of the societies, as well as by governmental policies. Well-developed countries invest generously in research, and therefore they have their well-developed measures for air pollution, and they would probably not give up willingly on their findings and adopt some coefficient developed by another country. Less-developed countries simply have other issues to care about rather than creation of a global index to encompass and correctly reflect the whole air pollution in one scale. In a nutshell, the reasoning is comprehensible, but it is complicated to directly compare air pollution in the world.

A researcher might have an urge to do so - to analyse the cross-country environmental situation in context of competitiveness or just for some other reasons. "Normal people" might be curious about the condition of breathable air around the world as a factor to consider while changing the place of residence. No justification needed to explain, why is it desirable to be able to straightforwardly compare the air in different places.

It is a good thing that the concentrations of the criteria pollutants are recorded almost everywhere. This measurements are essential yet not sufficient. Those pollutants are tracked that are easier to monitor. Say, the concentrations of particles with the aerodynamic radius of 10 micrometers (PM10) are vastly reported, but the ultrafine matter (PM2.5 and smaller) is not so abundantly captured, because it is technically more complex. However, PM2.5 is a dangerous substance for human health, and its adverse effect is also connected to the ultra fine size of the particles, since they penetrate into tissues of the human body and may get into blood vessels.

Nitrogen dioxide (NO2), PM10 and CO2 concentrations can be mined for almost any country for almost any period of time, and this data is open source. Mean annual (quarterly, monthly) concentrations of any of the three can be used as a reference indicator to compare global air pollution, but, of course, they do not provide a sufficient informative coverage.

As for more sophisticated indicators currently used to depict air quality, quite a bunch of them can be named. The U.S. EPA reports on the Air Quality Index  (AQI) - an indicator showing the threat that 5 criteria pollutants pose to public health.  The range of the values is broad: 0 to 500. Canada has a similar measure named Air Quality Health Index (AQHI), which is calculated on the basis of the combined impact of three pollutants: groundlevel ozone (O3), particulate matter (PM10/ PM2.5) and nitrogen dioxide (NO2), and the range of its values is from 1 to 10+. In the UK, the use is made of an indicator called Daily Air Quality Index (DAQI), which on the daily basis keeps track of five major pollutants. Similarly to the Canadian index, this one provides values in the 1 to 10 range. In China, the quality of air is reflected by a measure called Air Pollution Index (API), same in Hong Kong. The Chinese API is evaluated by the country's Ministry of Environmental Protection and its values are between 0 and 300+. In Hong Kong, the API index is provided by the HK's Environmental Protection Department. It is calculated on the basis of 5 pollutants and has a scale of 0 to 500, similarly to the U.S. AQI range of values. In Malaysia, the abbreviation "API" is used, too, but it stands for "Air Pollutant Index", and its values range from 0 to 300+. Finally, in Russia a general air quality measure is called Atmosphere Pollution Index (ИЗА, Индекс Загрязнения Атмосферы). It is a complex measure, as well, reflecting cumulative impact of several contaminative substances, and it ranges from 0 to 14+.

The indicators listed above have much in common: all of them are based on the values of one to five criteria pollutants. Once the desired sensitivity (scale) is established, it is feasible to come up with a synthetic criteria that would be common for all countries.

By now, unfortunately, no such measure is known.

Greetings from Hong Kong!

Dreams come true, and I'm writing this post from a computer belonging to the Department of Statistics and Actuarial Science of the University of Hong Kong. I've been here for a week as realizing a short-term stay, and I'll be here for a week more. My stay  is intended to perform a knowledge exchange, and there is a lot to learn, I must admit. Being here is fruitful for me, because, apart from myself being introduced to the whole new modus vivendi and modus operandi, my air pollution research has got a shift, and has been pushed in a completely new direction. I am very excited about it, but don't want to dig into this before I have done some investigation in this regard and made come preliminary conclusions.

May I just say that my environmental research seemingly acquires economic wheels, so I could be probably driven (back) to a money-based space.

I have been kindly invited here by Prof. Philip Yu, PhD, who, apart from being a great scientist (please follow link for the list of his selected publications), is an amazing person. I am very grateful to him for having invited me over.

So, tomorrow I am giving a talk here, and I am very-very much anticipating it. I will have to speak for 1 hour in front of a seemingly large audience. I hope I will be received in a friendly manner, as it has been until now. Moreover, my PhD defense date is to be announced later this week, and I believe that the X day will happen this September. Therefore, the tomorrow's seminar is a sort of a dress rehearsal to me, although I am strongly inclined to think that it will go way more smoothly, since my thesis covers less things than my tomorrow's talk. 

(I've just e-mailed this photo of my seminar's announcement to my Mum: hope that she'll like it.)

I will probably post the slides later on: may be, just a short summary of them since it is a long talk, and there is a lot of graphical material. 

Cell phone security problem

It's been over a month since I've lost my HTC Sensation smartphone.  I wasn't  too overwhelmed with grief, however, because it was actually just a piece of high-tech plastic. Still, I had to spend some time changing my passwords for all the applications such as e-bank, data storage facilities etc. etc. And yes, I also miss my Ibiza bikini pics that I have not backed-up and that somebody else can see now. That's the most annoying part.

All of these could not have happened if I were cautious and had used a security key to protect my phone's content. I have never ever done this, because I am too lazy to memorize the key, and the need to enter it every time I want to access your phone really irritates me.

However, it is better than changing passwords and sharing the photos of my not-that-killer abs with some random dudes.

When I got my new ringing gadget, that is iPhone 4S (no link needed :-)), the first thing I did was to introduce that security key. The iPhone device has the only option, that is the 4-digits key, while other modern smartphones, such as Samsung Galaxy Note, offer the possibility to choose between the key and a geometric pattern.

I've noticed that the majority of my friends choses patterns, and I wondered, why? Are the geometric keys visually more appealing than numbers? When it comes to safety, pin-codes are definitely safer. The number of combinations one can compose of 4 digits is equal to:

A pattern can be composed of up to nine positions, but each point must be adjacent to the previous one and non-repeating. I've asked some of my friends, at random, how many digits do they employ in the security pattern. The result is: from 2 to 4. People do not seem to tend to bother to invent 9-positions keys.

These pattern keys turn out to not only be a measure of safety of one's information but also to breed a nice combinatorics problem. To my mind, the latter is of more value :-).

Recently, as spending a day at a beautiful Costa Brava's beach and having that kind of view in front of myself:

I had nothing to think of. So, this problem came to my mind, and I decided to estimate the odds of breaking a pattern-key. For a precise comparison, as a pin-code has 4 digits in it, I have calculated the number of all possible combinations of 4 adjacent points that could form a sequrity pattern. For 4 points, it was pretty complicated to do this analytically, so I've opted for a straightforward calculation. Which led me to this kind of tree:

(image from my Moleskine, perdon my handwriting).

From here, the number of combinations can be deduced for 2,3 and 4 adjacent points.

The result is: 488 combinations for adjacent only points with no repetitions. For comparison, if not only adjacent points could be used, there would be 9*8*7*6 = 3024 combinations.

Resume: 4-digits number keys are safer than 4-points sequrity patterns.

To compare the complete safety of the number key VS the pattern key, all the combinations of 2,3,...,9 points must be sumed up and the resulting number should be opposed to 10000. To do so, I'd probably straightforwardly compute the number of combinations from 5 to 9 by simulations. A program is needed for that, of course. The sum of all the combinations might be close to 10000 or even superior.

However, common sense tells me that people won't probably bother as inventing keys of more than five positions. All in all, I don't regret that iPhone does not provide the opportunity to use pattern keys.
   

Hong Kong's air pollution hits a two-year record and, I so want to know more

How fun is that? Lately, I've been tremendously interested in Asian air pollution research, and I have been convinced that Hong Kong is a place that hosts a top notch findings and methods in this field. As I am going to China this September, I am very much willing to get to know people that carry out air quality research there and see how they apply their methodology.

Two days ago, I have contacted CAN to inquire whether I could join them as an unpaid intern for some two or three weeks. No reply has followed yet, however. And today I have found out that Hong Kong is being suffering from the worst air pollution in two years. CAN's website provides an interactive air pollution map, which now looks as follows:

As you can see, there are many unhappy faces on this map.

On August, 1, CAN has published a press release stating that air pollution in Hong Kong exceeds the guideline values provided by WHO and avoiding that people go outdoors.

I've used the twitter search to find out the exact numbers regarding yesterday's and today's concentrations, but so far there are no such reports yet. Also, I have not succeeded to find daily listings of contaminants concentrations at the monitoring stations.

I wonder, which spatial prediction models are of common use in Hong Kong to evaluate the region's air pollution? I can count some 25 monitoring stations on this map, an the CAN's July press release reports that the study has been has been based on the information from 14 stations. The number of the monitoring stations is within the average, so geostatistical methods should be probably successfully applied. This amount of stations for the area of 1.104 sq. km is comparable to the placed in Barcelona's metropolitan region: some 49 stations for the area of 3.218 sq. km.

Land-use regression models should be probably used, too. A recent study by people from the Hong Kong Polytechnic University reports on the analysis of PM2.5 concentrations in Hong Kong. The analysis is based upon the data obtained by a spectroradiometer (satellite data) and further integrated into a GIS database with an account to meteorology. A linear regression model has been used to fit the concentrations.

In a nutshell, as a statistician, I understand that yesterday's and today's values are outliers, and I would like to see how they correspond to the fitted distributions for pollutants, as well as how they concord with the predicted pollution surfaces.

I wish someone could share this knowledge with me.

And, the last but not the least, I am tremendously sorry for the residents of Hong Kong that are suffering from high contamination right now. I very much respect they bravery to create an alert and draw public attention to the problem. For instance, in Moscow, which is my home pueblo, no such alerts happen. Although, Moscow is a very, very polluted city, however, there is no much fuzz about the pollution problem there, and it should be voiced and handled ASAP.

Get a job, doctor!

Hi there!

It's been a long time since the last update.

The reason for my absence in this blog was the following: lately, I've been not sure that research is my thing. Also, I've been trying to put my dissertation together, doing the impossible to get it through all the commissions and reviews. That is to say nothing of the publications that take a lot of effort, after which you doubt: will anyone read it? Ever?

And in the end, when I am on the edge of getting my PhD, my doctorado europeo, what is next? Obscurity...

To get inspired, I've been watching this amazing movie which is all true. Thank you, Jorge Cham! You've opened our eyes as showing us how even postgrads from top U.S. universities get desperate about their present and future. So what about us, common people from small young (Southern) European universities? Shall we probably make a commitment to gardening till it's not too late? Oh no, we have to learn how to plant stuff first. Another degree for that?

Weeks away from getting crowned as an Honorable Author of A PhD Thesis, I find myself standing in the middle of the job market. However, instead of being a proud buyer, I try to get myself sold. And it must be a good deal. We, the doctors, have no moral right to be a bargain: we have been busting our everything in grad schools for the sake of the best future possible, as for us, so for the humanity.

There are two ways to go for a freshly squeezed PhD: academia and non-academia. After some skimming over the internets, I've got to an unpromising conclusion: both ways are slippery and rocky.

If you go academia, what will it look like? Most probably, it will be a postdoc and/or a junior teaching position at some academic institution. A postdoctorate contract will last for some three years, after which you need to get employed by yet another academic institution on a permanent basis. What you will do is teach and research. The ratio between these two activities will depend on the place you are at, but the labour will definitely not be very well paid. So, it will be pretty much like grad school all over again. The top of the pyramide is a professor's position. It is almost impossible to get there.

If you go to the industry, just like more than a half of PhD programs graduates, get ready to get low. Once you have adjusted your perception of reality to the world of business, and once you get in, you will surely find yourself surrounded by people younger that yourself that have no doctor degree. Nonetheless, they earn a good buck and most probably are your boss. Will this be tough for you?

Ask yourself, why did you go to grad school? To make a difference? Please, define making a difference then. On the edge of graduation, I still have not figured out what it means. However, I don't think that grad school is a waste of time. I really believe that we, guys, are like samurais who get very trained through all these years of our PhDs. If you are lucky and brilliant, you come up with a discovery. If you are not so, what you get is the skills and the knowledge of how to cope with an impossible stuff. You've been on your own for three years at least: just you and your research. Now, when you've delivered your thesis, despite your pain and frustration, you can do anything.

In the present moment, I'm facing an almost impossible task: to get a decent job fast. Due to family reasons, I would probably be looking for employment in Lyon, France. There is a lot of research going on there (no, I don't want to be a manicurist). However, I don't speak French yet. Neither do I have a work permit for France. What are my chances?

Thinking of this, I try to be optimistic. I'm in Morocco now, sun is high, and my hotel is very good, too.  I am writing this post on my hotel room balcony, and this is my view:

Nice one, isn't it?

So, now I am going to go to eat some amazing fresh fish and have a swim in this pool. And learn some French! :-)

Au revoir!