“The obvious objective of video games is to entertain people by surprising them with new experiences.” -Shigeru Miyamoto, creator of Mario I am almost certain that anybody who is reading this article has an idea about one of the most popular phenomenons in the world: Video games. The addicting nature, universal appeal and admirable technical work that lies behind the concept makes it one of the most intriguing pastime activities in the world, reaching more and more people over the years. In fact, with a growth rate of around 5% annually, the video game industry is one of the largest and fastest growing industries in the world right now. There is no denying that the industry as a whole has achieved a rather mainstream appeal when compared to the interest in the early 90’ and 00’s. Mainly driven by the motivation to create versatile games that appeals to numerous people from different ages, game developers have found many ways to profit off of and expand their craft. With concepts like mobile gaming and consoles like the Wii (which aims for a family appeal), video games are taking their places in people’s lives in different magnitudes. However, a few weeks ago, I came to a sudden realization about how popularity can turn something that is fun and creative into dull and repetitive very quickly and cruelly for a fan of the industry. This past September, 2K Games released an addition to their NBA 2K line with NBA 2K18, the latest addition to their realistic basketball simulation game series that has been annually released for over a decade. With improved graphics and new features, fans of the game were excited for the upcoming release date, however, the game has not turned out the way the fans have imagined. As of 18th of October 2017, on the online game retail store Steam, NBA 2K18 stands at a surprising 27% approval rate by the community reviewers of the game. For a better understanding as to how bad this situation is, the next worst rated sports simulation game is WWE 2K18 (coincidentally from the same publisher), by the same publisher, which stands at a 63% approval rate. The 27% approval rate also makes NBA 2K18 the lowest rated game on the platform for the year 2017 so far. Steams community reviews are crucial for video game developers, as any potential buyer can see the ratings every time they come across the game on Steam, and all reviews are open to access on the platform, and users can vote on how helpful these reviews were for them. So, why would this happen to a franchise that has sold more than 68 million copies of their games in the last 18 years? Well, the thousands of reviews I’ve read before starting this article showed a very significant problem: Microtransactions, and their prices. Microtransaction is a business model where consumers can purchase virtual goods for small amounts of money. The concept is usually online driven, and it has taken a significant part in the video game industry since the early 2010’s, as it can help developers to multiply their revenue by five with a smart addition of a microtransaction service in their games. A very common example of a game that basically runs on microtransactions is Candy Crush Saga, which took the world by storm around the year 2014.  For the developers of Candy Crush, it can be said that microtransactions hold an immense importance for the well being of their company, as their approach to microtransactions helps them generate close to $45.000.000 per month. The microtransaction system of the game is very simple. The game gives the user five “hearts” (tries) to pass the level that they are on, and if the user loses all of his/her “hearts”, the game sets a timer to when the next heart will spawn for the user. The game offers a microtransaction service where the user can purchase these hearts, along with other extra bonus items that can help the user in-game, and bypass the timer to continue where they have left off. However for a player that does not want to spend money on a casual game that they play to kill time, the microtransactions do not hinder the gaming experience much, as the game can easily played with the waiting time (which gives the user one heart per 30 minutes). Plus, it does help that the game is originally free, and microtransactions do not add to an amount that was already paid. So while the game was criticised for its addicting nature, it did not punish the user for not purchasing in game content, which usually negatively affects the gaming experience. For the situation of NBA 2K18, the entire game is based on the concept of having a “MyPlayer”, a self created in game character that the user can use to embark on an NBA career story mode, where your MyPlayers signs for a basketball team, or MyPark, where your player can compete in street basketball tournaments for in game prestige. The players attributes like shooting or dribbling the ball can be upgraded through 2K’s own in game Virtual Currency (VC), which can be obtained either in large sums through microtransactions or smallers sums through playing matches in game. The reason as to why this has been considered an immense problem by the community was caused by the unplayable nature of the game, without having to spend money on the already overpriced VC of 2K Games. Complaints among the reviews argued that the starting rating of the MyPlayer was significantly lower than the ratings of in game NBA players, who you have to come up against for the MyCareer mode, or argued that players who have the budget to “splash the cash” on VC had an upper advantage on people who were not willing or able to pay for better ratings. The reason why these problems are crucial for the gaming experience is caused by how expensive VC is to buy and how long it takes to accumulate enough VC for in game upgrades. My own experience with the game can provide a solid understanding regarding how hard it is to obtain currency. As of today, I have accumulated 51 hours of play time, trying my best to upgrade my character as much as possible. I managed to upgrade my character from a 60 rated player to a 69 rated player (without spending money on VC). To show how terrible this is, the lowest rated player in the game on any NBA team is rated the same rating, which is 69. Most users believed that the game has lost all of its fun, since it is nearly impossible to be successful with a “MyPlayer” without either playing the game for over 300+ hours or spending another 70 euros on a game that already retails at between 55 and 60 euros. Millions were reported to have refunded their games, and the company is currently facing serious accusations of power hungry and greedy motives. I was disappointed as an avid fan of the franchise as well, and, as I was doing my research prior to writing this article, I came to the sudden realization about the legitimacy of something I’ve learned in my first ever Business Administration class two months ago: “It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own interest.” -Adam Smith

Given the fact that you paid for the device on which you’re most likely reading this article, the answer to this question should be pretty straightforward. However, when you purchase a technological gadget you acquire ownership only of the physical device, not the indispensable software that it runs with. This has become a modern dilemma because as any other owner, manufacturers want and need to protect their property. They do this by implementing license agreements on their customers and keeping diagnostic manuals to themselves. These measures serve to protect their systems, but they also prevent both individuals from “tinkering” with their devices and independent repair business from performing their jobs, circumstances that intend to control a product even after its purchase which according to Gay Gordon-Byrne, the most important advocate in the movement, goes against many aspects of ownership. Over the last few decades, freelance tech repair became a service industry that creates millions of jobs worldwide. Before, most technicians that worked in this field were able to get a full grasp of the workings of a machine with the repair manuals that they came with. However, in the recent years, this indispensable data has been kept from the public by using copyrights as an excuse. This means that repairmen now have to dig for this information, by doing appliance tear ups that allow them to get to know the parts of the machines, and basically create their own guidebooks which are then shared online with the intent of increasing collective knowledge. But even as efficient as this process has become, the range of problems they can solve is still lowered the more secretive manufacturers become about their designs. Take the latest visions of the iPhone as an example; ever since Apple implemented the fingerprint reader to their home button, independent technicians haven’t been able to repair it. This is because, in order to get an iPhone to assimilate a new one they’d need to calibrate it with a machine only available on official Apple stores -not even on their authorized service providers- and so mysterious that there are not even images of it online. Additionally, the fact that the iPhone X integrates the home button and touch ID into the screen means that now independent technicians won’t be able to fix even a broken screen which can be easily considered the most common type of repair. This situation enables a monopoly in which the manufacturer is the only capable entity of fixing a broken appliance. In fact, according to iFixit, this change puts at risk more than 15000 companies who employ their iPhone parts sale program. Moreover, by eliminating the possibility of rushing to the most accessible or cheap aid they are also creating an environmental issue which has caused an increase in the share of new products purchased to replace a defective one from 3.8% to 8.3% in five years. Besides this, forcing users to wait for official assistance also makes the repair process much slower, an aspect that becomes even more serious if we take into account that this dilemma goes beyond smartphones as a result of the fact that in our modern societies more than one industry is going through digital transformations. An example of this is John Deere Tractors, a company that now forces farmers to wait for a representative of the company to get assistance whenever they break down, a process that is not convenient or profitable for them at all since they need to harvest and sell their products within certain dates. Nevertheless, there are some arguments in favor of manufacturers, for starters, and probably the most significant one is that granting users or independents repair businesses access to this information also means that hackers would get a deeper knowledge of their devices, which would make systems more vulnerable to cyber attacks, something that would be convenient for neither of the parties involved. Furthermore, multiple companies have also suggested that retaining repair manuals also prevent individuals from harming themselves while trying to repair an appliance without the necessary skills to do so. It is safe to say that both parties have viable arguments justifying their actions or demands, It wouldn’t be convenient for manufacturers nor users if hackers got their hands on some manual that would allow them to breach the system that protects your personal information. Nevertheless, keeping this indispensable data to themselves is a disruption of ownership because ultimately, if you buy something, then you should be able to decide what to do with it when it breaks down, instead of the current situation in which you have some kind of shared custody with the manufacturer.

Close your eyes. Imagine the American town. One can think of big shiny malls with parking lots larger than football fields or perhaps well-kept high streets where friendly neighbors are doing their daily shopping. Now open your eyes. This movie-like version of Americana has never been accurate in describing life in the United States, yet nowadays it is even less than accurate. A big transformation in American towns and in the lifestyles of its residents is reshaping the cliché into something new. Notably, Americans are changing their shopping habits in a commercial surge whose leader is no else but the internet giant Amazon. In recent years, more and more shoppers buy their favorite clothes, appliances, groceries and digital devices via e-commerce websites that offer them the best deals on a daily basis. Tired of walking through endless aisles, comparing prices relentlessly, knowing that you are definitely being ripped off? Have no worries. The shopping experience online is customized, tailored to your own preferences, easily at your reach. If you’re a regular Amazon shopper (and you know if you are one), the company probably knows more about your purchasing inclinations than your own mom. Definitely more than Macy’s, Sears, and any other retail giant who struggles to stay above water and attract shoppers. Over the past few years, we have been witnessing the ongoing closure of numerous chain stores and companies. You might have heard the Toys R Us filed for bankruptcy a while ago, joining other famous companies such as RadioShack, Rue21, Aerosoles and The Limited. Giant retailers are closing stores all across America due to declining sales: J. C. Penny is closing dozens of stores this year to accommodate for changing consumer habits, alongside Abercrombie & Fitch, Macy’s, Gap and Kmart. And all of this happened only in the past year! In this mystery of the disappearing retailers, Amazon is considered a prime suspect. During the spectacular rise of e-commerce, the online giant has become a term by its own rights, another way of saying online shopping. First, the numbers. In 2015, Amazon sold $55.6 billion worth of goods in the United States alone, operating 75 million square feet of distribution space and more than 110,000 employees. Approximately 50% percent of dollars spent online go directly into Amazon’s virtual hands. This spectacular boom is changing towns all across America: giant malls are stranded, parking lots in shopping centers are empty, and employees are losing their jobs or forced to work part-time. It is fair to add that economists and business experts have been warning that the US is “over-retailed” for quite some time. Simply put, there are too many retail stores, and not enough demand to satisfy their existence. According to this article, in 2015 there were approximately 25 square feet of retail space per capita in the United States, compared to 2.5 square feet per capita in Europe. Department stores and other commercial giants occupy millions of square feet across the US, in malls, shopping centers, and on high streets. In this new internet-based reality, real-estate becomes a disadvantage – a prime example is Sears, whose real estate holdings are worth more than the value of the company itself. The internet-era customers do not want to be one a million customers roaming through gigantic halls of mass merchandise, and they expect personalized marketing that appeals to their uniqueness. Thus, gigantic stores and malls may seem redundant. Back to Amazon. The company’s massive infrastructure of warehouses (more than 50,000!) and online presence has given the company an indisputable advantage almost in every retail sector. Their web services provide the computing backbone for leading companies and government agencies and their distribution systems dominate package delivery in America. The decline in sales of traditional retailers implies lower tax revenues and immediate fiscal consequences. And do not forget human capital – employees suddenly feel unneeded when their jobs are being done by fancy drones. Workers in “traditional” retail stores and local businesses find it harder to earn commission wages and struggle to maintain their working statuses as companies cut down costs and shut down dozens of locations. It was estimated that in 2015, Amazon’s net job impact in the US was the loss of around 148,000 jobs. Needless to say, numbers like these might be misleading within a narrow context of the economy. And perhaps we’re being a bit too bleak. Amazon’s emergence has had some beneficial effects on American towns. For example, Tracy, California. Located in the traditionally agricultural area of Central Valley, California, citizens of Tracy have started to witness more and more Amazon warehouses clustering in the eastern and western sides of town. Tracy citizens are now facing a larger number of job opportunities emerging from the e-commerce sector, and the widespread boom attracts new business to the area. A job in an e-commerce warehouse usually pays more than a regular retail job (about $12 an hour), and arguably requires more technical skills. E-commerce companies have been moving their distribution and shipping centers to strategic locations in the outskirts of large cities, such as central New Jersey, changing employment and commerce in both agricultural and industrialized areas. Is it for the best, though? Amazon is frequently voted as one of the worst retail-sector companies to work for. The working conditions in the warehouses gained a somewhat notorious reputation with complaints about low wages, negligence, and overall unpleasant treatment. Numerous organizations such as this have been calling for contractionary measures to cut back Amazon’s impact on local communities, issuing reports on Amazon’s aggressive entrance into so many sectors of the economy. It seems that some people are fighting back in the battle against this giant digital windmill, however, the revolution has already begun. They might be too late.

On Friday the 8th of September, more than 65 students gathered in front of the Academy Building of the University of Groningen. They placed tents, rolled out sleeping bags, and laid on air mattresses. Why? To show that they will just sleep in front of the university if there are no rooms for international students. The reason behind this protest is that the University of Groningen has accepted a lot of international students, even though there are no rooms for them in the city. These students are now staying with friends, in tents, and in hostels. It is clear that international students face a serious housing problem, but what can be done to tackle this issue? The problem begins with the university. Universities want to attract as many international students as possible, and they do a lot of marketing for great studies. So a lot of students apply, many get accepted, but when they come to the Netherlands, they find out that there are no rooms for them. It is already very hard for Dutch students to find a room due to the insane housing market at the moment. Unfortunately, international students face even more difficulties in finding a room. In other countries, most universities have a campus, but in the Netherlands, it is your own responsibility. Due to this, some students simply do not have any experience in finding a room by themselves, or how to even start. What makes it even more difficult is that these students are not in the Netherlands when they start looking for a room. They cannot come to viewings or meet potential roommates; instead, all contact is digital. Furthermore, some room owners do not want to rent rooms to international students because they will only be here for a short period of time. What remains is the private market, which is still a problem for most international students. Rooms in this market are often extremely expensive, since these rooms are not only targeted to students, but also to young professionals. In addition, when a tenant leaves a student house, most of these houses are looking for Dutch-speaking people or non-internationals. This can be seen very clearly on Facebook, where adverts for rooms often begin with: “Only Dutch-speaking. I’m sorry, no internationals.” It is clear that this market is not ideal for international students to find a room on. As already mentioned, these international students do not have a room when they come to the Netherlands, so where do they stay at? Most of them stay on the couch at a friend’s house, others in hostels, tents, or even AirBnB’s. The University of Groningen provided hundreds of temporary locations in response to the protest. In these locations, students sleep in big halls together with many other students. Of course this can only be a temporary solution, since no student can study productively when they have no room of their own and they need to share everything with a hundred other students. Luckily, universities have acknowledged the problem and are looking for solutions. Many old buildings are quickly being transformed into student rooms. Think of the old ACTA building in Amsterdam. Furthermore, more cities have built a Student Hotel. Amsterdam, Rotterdam, Eindhoven, The Hague, Maastricht, and Groningen now all have a Student Hotel. Another improvement is that universities make websites on which it is easier for international students to find a room. There are many websites now which all require a registration fee in order for you to become a member. Unfortunately, many of these websites are scams which just collect the registrations fees and only show fake rooms. An example of a reliable website provided by a university is ‘At Home in Groningen’. All possible rooms in Groningen can be found on this site. It is just too bad that this site was of no use to this year’s international students, as it only became available when the semester had already begun. There is also a waiting list in Amsterdam, where you can register for a room. The downsides of this waiting list are that the registration fee is 555 euros, and the odds of them finding a room for you at short notice are still very small. The last solution most cities are working on is building new places for students to stay at. This is a very good thing, but it is a long-term solution. Until then, universities must think about whether they have the capacity and facilities to provide a good student experience before accepting many new international students. This includes a proper room, for a proper price. The situation in Amsterdam is even a bit worse than in all other cities. In Amsterdam, the housing market in general is a huge problem. Dutch students spend months looking for a room. The prices are way too high. Rooms of 600-700 euros per month are the standard, and the Student Hotel in Amsterdam also asks higher prices than in other cities: 800 euros per month in Amsterdam. Compare that to Eindhoven, where the Student Hotel ‘only’ costs 650 euros per month. The Amsterdam Student Organisation ASVA receives many complaints about the prices and lack of rooms in Amsterdam. They also received complaints from international students about their situation. The ASVA immediately demanded that the University of Amsterdam would take action. This helped; in July, there were still 500 international students on a waiting list without a room, whereas now, in October, there are only 34. This is a good improvement, but we are not there yet. There are still people on that list, and probably many more students in the city looking for a room. To conclude, more rooms need to become available for international students. More need to be built, not only for the international students but for all students, because it is terrible that there are students without a room, with some even sleeping in tents. Until this problem is resolved, universities should only accept the number of students they can actually take care of. We will see next year whether the situation is any better.

Whether or not graduating school is worth the cost is a subject that’s rife with controversy. Depending on whom you talk to, getting a master’s degree or another advanced degree is either a great decision or a huge waste of time. The choice to attend graduate school is easy for future doctors and lawyers: they must have a professional degree to get started. But for someone who has chosen a path, for instance, in the area of economics and business, doing a postgraduate study is not a predetermined next step. A master’s degree is a qualification to be proud of, whatever your plans are. But for most students it’s also a big investment of time, effort and, of course, money, especially if you are an international non-EU student. How much does it cost? Pursuing a graduate degree can be a tempting opportunity; after all, you will likely improve your marketability when job hunting, increase your salary and expand your professional network. I often hear from my European friends: “Come on! It is only one extra year of studying, but it will make you stand out from the crowd once applying for a job.” And as much as I want to agree with them, I have recently been leaning more towards a “no” answer.  First of all, it WILL NOT make me much more exceptional among the other job applicants, over 70% of whom will also have a master’s diploma in their hand. According to the Digest of Education Statistics, the number of master’s degrees awarded has more than doubled in 2015. What could actually help you to be noticed by your prospective employer is a remarkable CV, with a couple of impressive internships, a few extra-curricular activities (such as team sports, student association, etc.) as a proof of your strong teamwork, time management, and communication skills as well as the ability to show your transferable skills. Is a master’s degree on the list? It would be definitely a plus, but will not necessarily help you to emerge among the rest of the applicants. Furthermore, yes, it is only one more year of studying, throughout which I could focus on a specific area in my fields of interest, gain more knowledge and experience, etc. However, it will also cost me an amount somewhere between 15,000-20,000€, not taking into account living expenses and rent. Definitely an investment that requires a second thought and a detailed cost-benefit analysis. When deciding whether or not a graduate program is right for you, you may want to take a look at the return on investment (ROI) of your degree as part of your decision. So, the bottom line is: could a Master’s degree that will cost me on average 17,000€ (on top of the 27,000€ that I have already spent on my Bachelor’s degree) give me any benefits as a job seeker and eventually pay off? Let’s have a look at the numbers (remark: the numbers are based on the assumption that I want to pursue my master’s in the Netherlands). According to the official website of the UvA, a full-time master’s programme in Economics will cost me 14,950€ per year (a master’s in Finance will cost a non-EU student a bit more – 16,850€). Next, as uva.nl suggest, the living expenses in Amsterdam, including rent, will lie somewhere between 900€ and 1400€ per month. This number includes insurance, accommodation, general living expenses, books and public transport. This sums to up to: Around 28,000€, and almost twice as much if you chose to do an MBA! Quite a tidy sum, isn’t it? However, the actual ROI of your master’s degree truly depends on the destination and professional scope of your future job. If you were to stay in the Netherlands, then your postgraduate school will pay off in just over two years. Sounds like a positive NPV investment to me. Moreover, if you are an EU or a Dutch student, your master’s programme expenses would be even less, and therefore you will get a return on such an investment even in a shorter time period. There are a number of factors you’ll have to consider for your decision of pursuing a graduate degree: professional goals, personal interests, and timing in life all play important roles. However, it’s equally important to weigh the financial implications of your intended program. Following your passion is important in life, but perhaps not at the cost of your financial security. On the other hand, you may have much to gain by completing your graduate degree. Reasons to choose a master’s programme Of course, there are plenty of intangible rewards that come with an advanced study that are not summed up to a number. Those are things I have already mentioned, like expanding your knowledge, networking, and social opportunities as well as satisfying your own passions. For some positions, a master’s degree is an essential entry requirement while for many others it is a highly useful one. Having a relevant postgraduate degree under your belt could give you a crucial competitive edge in a crowded job market. The reasons obviously vary from one individual to another, depending on the/your personal situation and goals. Some undergraduates might consider building a teaching career or dedicate their whole career to research. Others aim for a placement in a particular field, e.g. investment banking or business/financial analysist, and choose a master’s programme accordingly. However, there are some common beliefs. For instance, some of my friends have decided to continue with their master’s in order to put off the job search. Others choose to pursue a postgrad degree to keep themselves skillful and marketable for career advancement. Perhaps one of the biggest reasons why recent undergraduates make a choice towards a master’s is still to boost their salaries. So, is there actually a big wage gap between undergraduates and postgraduates in Europe? The good news is that studies across Europe and the US show that a master’s degree does actually provide an incentive for higher pay in the future. The table below from The Holland Alumni Network gives an overview of the average annual salary ranges for different degree types, and it is quite useful as a benchmark for future salary negotiations. As we can see, the lower boundaries do not change significantly, but as you gain experience in an industry (which can be pursued along with your main studies), you as a professional move towards the higher end. And here the difference becomes more noticeable. By means of simple mathematical manipulation we can see that a MSc graduate can earn nearly 27% more than a bachelor graduate, and a job seeker with an MBA degree (in his/her pocket) can boost his/her salary level up to 58% above that of a worker with an undergraduate degree. Quite a significant difference and an extra point to add to an argument for taking upon a master’s. Do you really need a master’s degree? The message I want to leave you with, and the belief that I am trying to stick to these days is: Don’t feel pressured into pursuing a master’s degree, don’t be afraid to take a gap year, or take upon an internship opportunity, or choose to do voluntary work instead of a year of advanced study. Do what feels the right thing to you. Everyone is different and has their own story, their own experiences. If, let’s say, you have decided that you are passionate about finance, and know that it is what makes you happy – then you are the lucky one among us. You found your path and you follow it. However, if you are not sure what you want to do and where you see yourself even in the near future – then take your time. After all, a master’s degree is undoubtedly valuable. But is it that for YOU? [1] Differs at universities across the Netherlands [2] The travel costs vary depending on a student’s home country. The plane ticket back home usually cost between 180-400€, taking an average equals 290€. Assuming that the student travels home twice during the academic year (during winter and summer break), travelling costs equal on average 580€.

Imagine that you are walking into an electronics store in order to shop for your appliances, more often than not, you would actually see that a flashy, modern, (and usually) expensive refrigerator being presented right at the entrance of the store with all of the functions that you need. As you have looked at the model for quite a few seconds, you might as well be tempted to look at the price tag and think whether it is possible to buy it. In most cases, the selling price would be unaffordable for many customers, so you’d rather walk into the store to look at other fridges. Unless you have a clear agenda, you will probably overspend, which throws your budget off balance. By virtually nothing, the store has successfully maneuvered to you to spend more than you had expected to. So, how did they do it? (The answer will be unveiled in the later part of this article) What is happening psychologically in your mind is coined as the “anchoring” effect. It was formally discovered by Tversky and Kahneman, whose scientific contributions are invaluable to the development of applied behavioral economics. To put it simply, as they asserted, our brain can be divided into two systems. One system is automatic and instinctive which requires little effort to control and can be trained through repetitive actions. The second system requires much more mental exertion which we usually encounter when dealing with highly complicated and complex tasks. The “anchoring” effect can influence both of the systems by either activating an adjustment mechanism towards the anchor (through the complex system) or facilitating a non-existent world where it tries to makes some associative links with the anchor (through the effortless system). The general idea is that, through these two systems, an “anchor” would influence people’s judgment, although it is totally irrelevant to the discussion at hand. To test the validity of their findings, they performed an experiment on university students. Now, try this experiment by yourself (and if you are calling your friends, that’s even better). So, take two small pieces of paper and write two numbers “10” and “65” on each, fold them, and ask everyone to pick up one of the two pieces, then return it to you so you could proceed to the other members (they used the wheel of fortune, but they manipulated the wheel so that the outcomes could only be either “10” or “65”). Then, the participants were asked to answer the following two questions: “Is the percentage of African nations among UN members larger or smaller than the number you just wrote (chose)?” What is your best guess of the percentage of African nations in the UN?” Most participants would be able to answer the first one correctly, but their estimations for the second question would be hugely varied. In particular, they found that the answers to the estimates of 10 and 65 were 25% and 45%, respectively. That yields a difference of 20 percentage point, although question 2 in the first case is phrased indifferently with the one in the second case. If you are not aware of the information in advance, it also comes naturally to you that you have very little confidence in your answer. In fact, as of current, all African countries are now UN members, so the correct answer to this question would actually be exactly 100%! In the experiment they conducted later on, they inquired the participants whether they knew the age that Mahatma Gandhi passed away (at the age of 78), with two anchors being “9” and “140”. They also found that there was statistically a significant difference between the responses of the two groups with different anchors. In this instance, although you do not deliberately steer your thought towards these numbers, the “anchors” create many implied suggestions from these numbers. If you are given number “140”, then it is likely that an image of an ancient person would come to your mind subconsciously – as such the effortless mechanism part of the brain has created an imaginary world in your brain that is more or less linked to this number. As such, your prediction has been influenced by seemingly an irrelevant factor. The theoretical concept of “anchoring” is also widely applied in many economic scenarios, especially during the process of negotiations. Suppose that you are accepted for a job position in a company and are offered an initial salary. The negotiation process is usually carried out by the interviewer herself, so she gains the position of the first-mover. This is extremely advantageous from the perspective of the company by “anchoring” the discussion in their favor. The resulting effects of it can be explained by both system: by offering the proposed salary as the anchor, your brain tries to analyze the reasoning behind the offer and seeks an appropriate counter-offer, while the effortless system attempts to construct associative networks from the offered salary so as to make this initial salary as reasonably as possible, even though you do not consciously control that. The similar line of reasoning can be applied to auctions, where if there is no reserve price, the auction price of an item should be largely influenced by the first bidder. So, in the context of the electronics store, how does the anchoring effect come into the picture? Well, the fact that the existence of the flashy refrigerator at the entrance has indeed influenced your pricing mechanism, as soon as you think about it. Therefore, it is more likely that when people enter the store, they would look at models that possess at least some of the special qualities of the featured product at the entrance, which typically belong to the high-end spectrum. When there are unknown qualities of the product, or virtually it is incomprehensible for one individual to quantify such a trait of the product and with (moderate) time pressure, it is more likely that we are drawn our attention to the positive qualities. As a result, we would tend to overestimate the true value of the product in many cases. Therefore, artificially the expected benefits of using the product increase and hence your evaluation of a fair cost-benefit analysis of an item is positively-skewed, but only temporarily. In fact, Galinsky (2011) finds that when there is some uncertainty, high “perceived” anchors usually attract people to look at the upsides of the matter, while low “perceived” anchors draw people to direct their attention to the flaws. It is actually very difficult to go against these behavioral patterns since it is inherently the nature of human body. However, having realized this, we could minimize the impact of anchoring, by actually not focusing on it. If you are considering buying some new appliances for your apartment and unsure whether to buy the item, put that anchor on something you would have spent otherwise. By that way, it is much easier for you to control your expenditure. In negotiations, however, you should prepare the desired amount beforehand and focus your attention on that to see whether the offered anchor is acceptable; the important thing is that if you are thinking of a counter-offer, make sure that it makes sense. Moreover, repeated interactions would decrease the significance of an anchor as both parties are well aware of their counterparts.

Have you already watched the new Designated Survivor-episodes? The new season of Orange is the New Black? All six seasons of House of Cards, and probably even Stranger Things? Over 103.95 million people have subscribed to Netflix, the popular streaming and video-on-demand (VOD) service which accounts for 125 million hours of streamed movies and series per day. With a business model that focuses on binge-watching, it has become impossible to imagine a relaxed night on the couch without your favourite series on the screen. The original series that Netflix produce are widely known and  well respected. This year, the Californian company received 93 nominations for the Emmy Awards: a reflection of the leading role that the company  is playing within the television industry. Although it is often said that in Hollywood ‘nobody knows anything’, Netflix is producing one success after another. These successes are the results of an interesting combination of data, creativity, a well-defined business model and a little bit of luck. Whathas defined Netflix’s success and what will be the consequences? It was in 2013 that Netflix brought their streaming services to any device of their customers’ choice. Shortly after, they expanded the number of originally created content, led by Chief Content Officer Ted Sarandos. As Sarandos revealed in several interviews, these two developments are closely connected. As an increasing number of people have become able to stream films and series, the company namely has created their competitive advantage over other television producers: data. Every episode that is watched, paused, repeated or ended is being tracked, so that Netflix can adjust new content to the customers’ preferences. Since many creative industries are characterized by unstable and unpredictable demand, including film-making , television producers do not receive direct signals from their audience. The online traces that Netflix uses to analyse the market partly explain the success of the originally created series, of which there are so many examples. Sarandos and his department thus spend a lot of time analysing their user data in order to determine the audience for new series. House of Cards is a very good example of this practice. At Netflix, they noticed that the demand for political television series was relatively large, that a significant amount of people who watched this kind of series also liked films starring Kevin Spacey, and that many out of this particular group also watched dramas directed by David Fincher. Even before the release of the first season of House of Cards, Sarandos had already bought the second season of the political drama series, starring Kevin Spacey, and directed by David Fincher. Netflix is definitely making use of the tools they have at hand, which leads to a good guess of what their target audience might want to watch. But as usual in the creative industries: it is still a guess. Some series do not become as great successes as the examples that are mentioned earlier due to the ever-changing preferences of the customers. The algorithm used to predict the customers’ taste is determined by past experiences, which can of course change over time. This uncertainty has led to unsuccessful productions in the past few years, but did not result in severe losses. The business model, that is built upon subscriptions, provides the company with the possibility to experiment with content. As long as they keep making a few series that appeal to their audience, they can incur the costs of a failing one. An example is The Get Down. Of this series that displays the origins of the hip hop scene in the Bronx, New York, and which should have been extremely appealing to the target audience, was made only one season. Although many subscribers might have been disappointed by this drama series, they did not immediately withdraw their monthly subscription. Why not? Probably because (1) the cost of the subscription is relatively low (although last Thursday the company announced to increase the monthly price from $9.99 to $10.99), (2) Netflix is popular among many people, and (3) people are binge-watching a lot. The price of the online streaming service is relatively low compared to regular television, and the quality of the content is relatively good compared to the free piracy-driven alternative. That is most likely the explanation to why people are willing to pay for the service.. But what further keeps the customers away from withdrawing is that Netflix is such a widespread phenomenon. Due to the so-called network effect, people want to keep their subscriptions in order to talk about the same series as others in their direct environment. Before the rise of the internet, people used to watch the same television shows. They could share their thoughts and opinions about the television shows and series, because there was hardly another option than watching the same shows and series. Nowadays, it is pretty convenient to talk about the same successful series as everyone else. The successes of House of Cards, Narcos, Orange is the New Black, Breaking Bed, and Pretty Little Liars has thus created a network of subscribers that will continue watching series on Netflix and are rather loyal. However, probably the best step of Netflix has been the initial idea behind the brand: watching video on demand. Being able to watch an entire season at once, also known as binge-watching or bingeing, has contributed to the big success that companies, such as Netflix, but also Amazon and HBO, have become. The series and films have almost become an addiction to its audience, which leads to a very stable customer base. Pleasing this customer base by creating series tailored to their preferences thus is logical step in expanding the brand loyalty. The increasing popularity of Netflix, however, goes at the cost of linear television. With the possibility to watch your favourite series, about which all of your friends are talking, at any moment on any device, linear television has become less and less popular. Especially the fact that linear television does not adhere to the schedule of the customer is disadvantageous to television producers. The audience ratings have dropped since the rise of more on-demand services, which can certainly be characterized as a threat for the established broadcasting companies. As John de Mol (the biggest television producer in the Netherlands, and producer of The Voice and Big Brother) said when he visited Room for Discussion this September: the other broadcasting companies are not the biggest threat, but the internet, YouTube, Netflix, and other on-demand services are. Linear television is at stake due to the rising popularity of Netflix, but will it eventually disappear entirely? It might, because let’s be honest: do you still watch linear television? #CulturalIndustries #Netflix

Time and again, we see different news headlines talking about why aiding development in underdeveloped countries is paramount to betterment of the people. This begs the question, how exactly do we aid these countries? Do we spoon-feed them according to our ideologies? Or is it just monetary aid that they seek, with the government’s operational capacities competent enough to resolve it’s states issues? Also, who is going to provide this money? Will they impose strict restrictions on how to use this aid, thus facilitating a hidden propaganda? Or will it be just foreign aid from charity and philanthropic organizations? Last but not the least, who is benefitting from this, and what is the target? Is it just increasing production of material goods? Or do we also account for less tangible things such as happiness, quality of life and freedoms to pursue means to an end? These questions, and many more, are the backbone of international development. As students of economics, we understand how the world works from (arguably) the most important lens: the monetary one. However, the buck stops there for us. What exactly happens afterwards? Where are these huge macroeconomic policies implemented? How has it affected us over the years? For eg: we talk about international trade and capital flows. But what escapes us is the fact that countries made a conscious decision to be open to the global trade frame work. What was trade like before, in a closed economy? More often than not, we consider economics to be an exact science. We have a set of theories which we base on assumptions, and use statistical tools to verify the degree of correctness and pat ourselves on the back. However, we must realize that it is as subjective as any other social science. What we interpret as important for a country’s economy (for eg: growth rate of Gross Domestic Product [GDP]) might not be true for a student of development, who might strongly believe that local representation in development programs (local contextualization) is key to reaching a high Human Development Index (HDI). There are different viewpoints coming from different disciplines, however, we can not afford to stick to one. Instead we must strive to be as holistic as possible, in our understanding of the world. Development studies and economics have a very profound relationship. Very simply put, economics shows us theories of how money is created, and how it is used. For eg: demand and supply determine price. And based on price, a rational consumer would allocate his budget to his basket of goods. And based on this allocation, different products have different profit margins, which in turn are related to their production costs. Again, these production costs can be attributed to availability of raw materials, labor force, etc.This is just a small example of how different concepts in economics are related. Ofcourse, on a macro-economic scale, there is whole new strata of concepts, which can not be explained so simply. On the other hand, development studies exposes us to the different actors in global development. And arguably the most primary tool for development is money. Development studies for example talks about how organizations such as the Bretton-Woods institutions, namely the World Bank and International Monetary Fund, distribute wealth to countries in need of it. However, what it also does is take a critical stand, and look at these institutions. It discusses why these institutions were created, and how they push a neo-propaganda. It also talks about different theories of development, and relates it to historical events. To put things in a clearer context, I will be talking about one such theory: Rostow’s stages of development. Walt Whitman Rostow is one of the biggest names in the academic field of development studies. He was a strong supporter of capitalism because of the efficiency it brought to the economic system, and likewise, he was very averse to socialism. His biggest contribution to this field was his book: “The Stages of Economic Growth: A Non-Communist Manifesto”. If you still had doubts about whether development studies was related to economics, the name of one of the most influential books in development should clear that cloud of uncertainty for you. According to Rostow, economic growth occurs in five stages: Traditional Societies Pre conditions for take off Take off Drive to maturity Age of high mass consumption Traditional societies: Some of the defining features of a traditional society are: High primary sector (almost 100%) share of GDP No social and political organizations such as a government or a civilized society Very primal and limited technology Pre conditions for take off: As a state or a society moves towards growth-oriented development, it needs to begin with the pre conditions for the same. This is marked by: Export of cash crops to facilitate economic growth General feeling of national identity Demand for raw material, either internal production or external imports Take off: Urbanization increases, industrialization proceeds, technological break through occurs. “Secondary” (goods-producing) sector expands and ratio of secondary vs. primary sectors in the economy shifts quickly towards secondary. Textiles and apparel are usually the first “take-off” industry, as happened in Great Britain’s classic “Industrial Revolution” An Example of the Take-off phase is the Agriculture (Green) Revolution in the 1960s. Drive to maturity: Diversification of the industrial base; multiple industries expand and new ones take root quickly Manufacturing shifts from investment-driven (capital goods) towards consumer durables and domestic consumption Rapid development of transportation infrastructure. Large-scale investment in social infrastructure (schools, universities, hospitals, etc.) Age of Mass Consumption: the industrial base dominates the economy; the primary sector is of greatly diminished weight in economy and society widespread and normative consumption of high-value consumer goods (e.g. automobiles) consumers typically (if not universally), have disposable income, beyond all basic needs, for additional goods Urban society (a movement away from rural country sides to the cities) As is clearly evident, the very core of Rostow’s development theory is economical, because it is expressed in the different sectors of the economy, export, import, impact of technology and other concepts integral to economics. And although the theory itself (like most of other theories in social sciences) is subjective, it throws light on some of the biggest changes in the world, which revolutionized the global economy. That is why we as economics students must delve into the field of international development studies, if we wish to gain a more holistic view of the world, and be able to understand events from more than just one perspective.

One of the first things they teach you in an introductory statistics class is that you shouldn’t take the average of ordinal data. It doesn’t make sense, because unlike 4 °C and 5 °C, the difference between four stars and five stars is neither quantifiable, nor the same for all people. For me, Twelve Angry Men is five out of five stars. It’s a great example of the use of dialogue and acting being the sole focus of the story. However, for someone else, it may be tough to watch… the movie is just twelve men in a room, being angry at times. The thing is, if they give the movie only one star, that doesn’t mean that they like it one fifth of how much I like it, it just means that they didn’t enjoy it. It gets even trickier though, let’s say that me and my friend both give Fight Club four stars. Do we enjoy the movie the same? Likely not! So when a movie’s average is 8.8/10 on IMDb, that means… not that much except that people scored their appreciation of it 8.8, on average. It doesn’t mean you’ll like the movie, and it doesn’t mean that you’ll hate it. It may tell you something about the quality of the movie; I noticed that I tend to enjoy movies with ratings higher than 8 for sure, but I also dislike quite a few of them too… Let’s get even more confused though, shall we? Ratings on IMDb are not distributed the same as Rotten Tomatoes, Metacritic, or Fandango. People who rate movies on Fandango seem to enjoy movies more than the ones rating on IMDb, and Rotten Tomatoes seems to have a nearly-uniform distribution. So not only are these ratings more or less meaningless, they aren’t even distributed the same! Considering all these drawbacks of ordinal ratings, we care an awful lot about them – to the extent that 36 percent of all movie-goers in the United States check these ratings when considering watching a movie. Essentially, you will never really know what that number really means, but it’s stuck with you as a form of prejudice for-or-against the movie. With all the attention given to these ratings, you would expect movies rated on the lower end of the scale (say, less than 30%) to perform badly, right..? Out of the most recent ones, the box office tells a different story. Box office is the place where you get your tickets to be admitted into an event, and in the film industry it’s most often the amount of money a movie makes when it’s being screened. It’s a good metric to determine whether or not a movie achieved success at its release, because it roughly demonstrates the amount of people who cared enough to see it as it was being screened. In our weekly Rostra meetings, we propose ideas for articles. When I pitched the idea for this one, we had a small debate on why Emoji Movie made so much money. Alma pointed out that it’s a kid’s movie, so you’ll always have kids and their parents coming to the movie to essentially double or triple the box office figures. That makes sense, but it’s not really sufficient to explain cases like the new The Mummy, Baywatch, and Pirates of the Caribbean. All of them have ratings on the lower end of the scale, but range from $60,000,000 to $170,000,000 in box office revenues. To put that into scale, the cult-classic and critically acclaimed Fight Club only pulled in about $100,000,000. Movies have been making significantly more money over the years, true. Even though there are outliers like Avatar (2009, $2,787,965,087 worldwide gross, $3,190,584,377.92 adjusted for inflation) and Titanic (1997, $2,186,772,302 worldwide gross, $3,345,134,883.58 adjusted), there is a strong trend in worldwide gross and box office figures. So comparing Fight Club to Baywatch may not be the best choice. However, it’s obvious when a bad movie does better than a good movie. The discussion on this topic itself actually started from the Emoji Movie, by the way. The movie currently has a 10% rating on Rotten Tomatoes (which was actually 0% when it was initially released), but it made a staggering $25,000,000 in its opening weekend. No matter how much people care about the ratings, bad movies are clearly still in demand. No matter how meaningless movie ratings are, in whichever form or website, their power is nothing to sneeze at. Rotten Tomatoes may not always be able to break a movie, but it’s also not able to save good ones either. War for the Planet of the Apes, with an incredible 93% on Rotten Tomatoes, made less than the new Pirates movie. If rating were anything to regress on, we seemingly can’t multiply 29% by 3, and get nearly $500,000,000 that War for the Planet of the Apes ‘deserves.’ In the end, the worrisome thing to me is not whether good movies will prevail or not—I’m sure they will—but whether bad movies will prevail or not. With the amount of money chick-flicks and bad remakes are making, the return-on-investment is too good for Hollywood to pass up on them. Although, who am I to call a movie bad? My one-star may be your five-star after all.

Economics as usually taught is not a positive science at all. To be fair, no science is. If we claim that scientists should stick to the facts, this implies they should give up the goal of explaining or predicting real world events. This is true even in physics. Why has the pen I just dropped fallen to the ground? Because it is subject to the law of gravity (duh!). But why do we accept this explanation? It is because most of us have never left earth and therefore have never experienced an exception to the law of gravity and thus find it safe to assume that the law will hold for all objects dropped. Thus, our acceptance of scientific laws is ultimately an act of faith and this faith can be corroborated more or less by experience, but the law can never be accepted as such on the basis of experience alone. Economists’ assumptions, however, are very different. The workhorse model of modern macro-economics for instance assumes ’a large number of [profit maximizing] identical firms, […that] hire workers and rent capital in competitive factor markets, […] sell their output in a competitive output market[, and] are owned by the [rational] households, so any profits they earn accrue to the [large number of identical] households’ (Romer 2012: 49-50). In sharp contrast to assumptions in physics, none of these assumptions are corroborated by experience. Firms are not identical, but instead try to stand out from the competition. Neither are households and in the light of much behavioral economics and psychology alike, the assumption of rationality does not appear to be very tenable either. If you have ever spent time in the real world, you also know that competitive markets are few and far between and if we all shared in companies’ profits, the developments Pikkety describes could never occur. Romer might as well write: ‘Ignoring reality completely, we may assume…’ Now, if these were just simplifying assumptions to be relaxed later (comparable to assuming friction away when discussing gravity), this lack of realism would just be a temporary matter and more advanced models would regain their positive footing. But as I stated before, we have known ever since Sonnenschein (1972), Mantel (1974) and Debreu (1974), that some of these assumptions cannot be relaxed at all without sacrificing the solvability and/or stability of the  model. So we are indefinitely stuck with absurd assumptions that have no basis in reality and can never be relaxed. Reasoning from those absurd assumptions however, we can ‘prove’ that markets tend to optimal allocative efficiency, a unique equilibrium that optimizes societal welfare. If you accept this conclusion, it seems to follow that society is best of if the assumed conditions can be realized in reality. But once you have reached that conclusion, you are engaging in wishful thinking: a normative agenda that outlines not what reality really is like, but what it should be like to reach optimal outcomes. This in itself already has a normative ring to it, but once you realize that reality cannot even in principle conform to all of the assumptions, it follows that belief in the policies such models prescribe, requires that the absurdities of the model are somehow immaterial to the applicability of their conclusions. So, even though the existence of differences in preferences, for instance, theoretically invalidates the existence of stable equilibria, we are somehow required to convince ourselves that this theoretical impossibility has no real bearing on reality. Now, that is a big leap of faith. All in all, much of neoclassical economic modelling hides a normative agenda, whose acceptability and applicability hinges on our acceptance of the impossible as either possible or negligible. None of the above is exactly rocket science, I think. Why then do so many economists cling to the belief that economics is a positive science that studies ‘man as he really is’ (Hirschman 1977; cf. Mandeville 1724 or, more recently, Levitt and Dubner 2009)? Their conviction probably stems from historical inertia. When Adam Smith wrote The Wealth of Nations, he was up against the clergy and the widely held belief that avarice (which we now call profit maximization) was a mortal sin that should be held in check and fought tooth and nail, if we were to prevent chaos. This moral battle was notoriously difficult to win, especially for those living in poverty (which in Smith’s day was true for the vast majority). Pushing back against the clergy, Smith conjectured that if we were all guided by self-interest and avarice and interacted through markets, the market would not only constrain our vices sufficiently to prevent society from spiraling into chaos, but would actually ensure superior outcomes. This was a very liberating argument. No longer did people need to constrain their impulses, live in poverty and hope for salvation in the after-life. Instead, they could do what was best for them without fearing God’s wrath, as they could redress their attention to their self-interest as an act of biblical love for one’s neighbor. From this perspective it can be understood why economics at the time could be hailed as the study of ‘man as he really is’. Economics had after all replaced lofty moral ideals with the idea that giving in to one’s impulses is actually beneficial to all and doing so surely comes more naturally to most people than being a saint. The early successes of the new world order Smith envisioned seemed to prove him right: wherever capitalism took hold, economic growth jumped up (Heilbroner 2012; cf. Fouquet and Broadberry 2015). This strengthened the belief in Smith’s conjecture and later on prompted  attempts to prove it mathematically. From the outset, then, neoclassical economic modelling was a teleological endeavor in which the assumptions were at least as much premised on the conclusion as the other way round. The conclusion just had to be that communities of self-interested individuals interacting in markets were bound to achieve superior outcomes and the investigations and modelling took the form of establishing the assumptions that would generate this conclusion. Thus, realism of assumptions or establishing an objectively true theory was never the primary goal. But if you mutilate your assumptions to the point of absurdity in order to prove the (potential) superiority of a particular society, you are engaging in ideology rather than science.

An upcoming feature in financial markets is algorithmic trading. Even though there are only a few big players in this market, the impact is huge. During 2009-2010, around 60% to 70% of U.S. trading was attributed to high-frequency-trading and algorithmic trading. This is an astonishing amount if you remember that there are only a few big players in this market. Let’s take a look into the world of algorithmic trading and high-frequency-trading. Let us first define both algorithmic trading and high-frequency-trading. Both are trading strategies, mostly used by hedge funds and specialized trading firms, but also by investment banks. To give a definition of both strategies, first, note that high-frequency-trading is a subset of algorithmic trading and, in turn, high-frequency-trading includes ultra high-frequency-trading. Algorithms essentially work as middlemen between buyers and sellers, with high-frequency-trading and ultra high-frequency-trading being a way for traders to capitalize on infinitesimal price discrepancies that might exist only for a miniscule period of time. In the end, algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. Computer-assisted rule-based algorithmic trading uses computer programs that make automated trading decisions to place orders. Algorithmic trading splits large-sized orders, places these split orders at different times and even manages trade orders after their submission. Large sized orders are placed by big institutional investors like pension funds or insurance companies. Due to the scale of the order, the price of the projected security is influenced (driving it up with a buy order and vice versa). This causes less yield on the investment made. Algorithmic trading aims to reduce that price impact by splitting large orders into many small-sized orders, thereby offering traders some price advantage. The algorithms control the schedule of sending orders to the market by reading real-time high-speed data feeds, detect trading signals, identify appropriate price levels and then place trade orders once they identify a suitable opportunity. Other kinds of algorithms can also detect arbitrage opportunities and can place trades based on trend following, news events and even speculation. This type of algorithms is mostly used by hedge funds with that specific investment strategy. As mentioned above, high-frequency-trading is an extension of algorithmic trading and manages small-sized trade orders to be sent to the market at high speeds. To define speed, think about milliseconds or microseconds. A high-frequency-trading system can process orders in less than four hundred microseconds (0.0004 seconds). That is a thousand times faster than the blink of your eyes.  HFT algorithms typically involve two-sided order placements (buy-low and sell-high) in an attempt to benefit from bid-ask spreads. HFT algorithms also try to “sense” any pending large-size orders by sending multiple small-sized orders and analyzing the patterns and the time taken in the execution of the trade. If they sense an opportunity, HFT algorithms then try to capitalize on large pending orders by adjusting prices to fill them and make profits. Ultra HFT is a further specialized stream of HFT. By paying an additional exchange fee, trading firms get access to see pending orders a split-second before the rest of the market does. There are a couple of algorithmic trading strategies. Any strategy for algorithmic trading requires an identified opportunity which is profitable in terms of improved earnings or cost reduction. The following are the trading strategies used mainly in algorithmic trading. If you have had some hedge fund classes, most of them will be familiar to you. Trend Following Strategies: The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because they do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis. An example is the 50 and 200 day moving average, which is a popular trend following strategy. Arbitrage Opportunities: Buying a dual listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks versus futures instruments as price differentials do exists from time to time. Implementing an algorithm to identify such price differentials and placing the orders allows profitable opportunities in an efficient manner. Index Fund Rebalancing: Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20-80 basis points profits depending upon the number of stocks in the index fund, just prior to index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and best prices. Mathematical Model Based Strategies: Further, there are also a lot of proven mathematical models, like the delta-neutral trading strategy, which allows trading on combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero. In this way, you will hedge the delta risk but still pocket the profits. Trading Range (Mean Reversion): The mean reversion strategy is based on the idea that the high and low prices of an asset are a temporary phenomenon that revert to their mean value periodically. Identifying and defining a price range and implementing an algorithm based on that allows trades to be placed automatically when the price of asset breaks in and out of its defined range. Volume Weighted Average Price (VWAP): The Volume Weighted Average Price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock specific historical volume profiles. The aim is to execute the order close to the Volume Weighted Average Price (VWAP), thereby benefiting on average price. Time Weighted Average Price (TWAP): The time weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and an end time. The aim is to execute the order close to the average price between the start and end times, thereby minimizing market impact. Percentage of Volume (POV): Until the trade order is fully filled, this algorithm continues sending partial orders, according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels. Implementation Shortfall: The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely. Now that we have seen what algorithmic trading is and which strategies exist, we can look at technical requirements for algorithmic trading. Algorithmic trading relies heavily on these (technical requirements). First of all, you need computer programming knowledge to program the required trading strategy, hired programmers or pre-made trading software. Of course, network connectivity and access to trading platforms for placing the orders is necessary. Also, access to market data is a must because it will be monitored by the algorithm for trading opportunities to place orders. Historical data can be useful for back-testing, depending upon the complexity of rules implemented in the algorithm. Risks in algorithmic trading might include another market participant’s algorithm being faster in executing the buy and sell order. This results in an open position. To illustrate this risk, suppose you place a buy and sell order. Prices fluctuate in milli- and even microseconds. What happens if your buy trade gets executed, but the sell trade does not as the sell prices change by the time your order hits the market? You will end up sitting with an open position, making your arbitrage strategy worthless. Also, system failures, network connectivity errors, time-lags between trade orders and execution, and most importantly, imperfect algorithms could be a risk. Bottom line, it is very exciting that these developments take place and they might make financial markets more efficient. On the other hand, algorithmic trading highly depends on the technical conditions of systems and errors that might be out of control of human actions. Nevertheless, I think that people who pursue a career in financial markets should learn the basics of programming. Not because they can build systems on their own, but rather because of the ability to analyze the algorithms and to know what the engineers are doing so they can have a valuable discussion.

I think many of us while looking any sports game on TV thought: why do they get so much money anyway? Of course, it’s all the sponsors and investors, and being a professional athlete is indeed hard, so they deserve the money, but some of the sums sound ridiculously high. In this article, I want to discuss where the money in the sports industry comes from. The first thing to notice is that not all athletes seem to be “overpaid”. There is a huge gap in salaries between players from the same team/same sport. It does depend on how experienced and good the athlete is. A good example of this can be a comparison of annual salaries of football players from the same team. In Real Madrid F. C. Christiano Ronaldo (forward) earned 32 million pounds in 2016, whereas Gareth Bale (forward) earned 15 million pounds that same year. The difference may come from experience and from how good they play during the season. As well as this, some teams earn more than the others in a certain sport because of the rank they have. The higher you are in the league the more annual pay you get. One of the more interesting things that drives the salaries for athletes up are injuries. Not all of us constantly think how injuries affect athletes both during and after their career. The insurance companies will not necessarily accept athletes as clients, as it is not profitable for them, or will offer the coverage which does not help with paying the full cost of the needed surgery, for example. Next reason is the short career of an athlete. Of course, some athletes, like David Beckham, for example, make their name a “brand” before finishing their career and can enjoy high salaries from commercials, tv-shows and nearly anything connected to the fashion business. However, only a small percentage of athletes can succeed in such things. Most of them do stop their career at a much younger age than the official retirement of 60-65 years. With an understanding of why athletes deserve such high salaries, comes another question. How does the sports industry make so much money, so they can pay such high salaries? One way the industry as a whole makes so much money are the fans themselves. Buying official merchandise, tickets and overpriced food and drinks at the stadiums – that drives the profits. If there was no demand – prices for tickets and wages of athletes will have to go down. However, people watch sports not only because it is a fun way to relax and entertain yourself, but also because many people actually get inspired by watching sports and might as well do greater things themselves. Both inspiration and pleasure are not the things that you can evaluate in terms of money. Another reason for the profitability of the sports industry is the sponsors or investors. Large multinational companies sponsor the games, design and provide uniforms and make contracts with the most famous players for advertising campaigns. Sports are watched all around the world and have millions of fans out there, so that is a nice marketing field for the companies to promote their product. Some of the largest contracts include Adidas cooperation with Leonel Messi. It pushed Adidas to the top of the sports apparel market. The company also sponsored two teams (Germany and Argentina) during the World Cup 2014 and had a right to design the tournament ball as well. Another example of such cooperation is Reebok working with Conor McGregor (a UFC fighter). The company sponsors some of his fights and the athlete also receives 20-30% of all the sales made of his own merchandise for Reebok. The company became one of the main apparel suppliers to UFC. The sports industry already makes a high revenue every year, but this number is expected to grow even further. The amount of money paid by TV channels to buy the rights to translate the games becomes higher and higher every year. Sponsorship money is expected to grow by 4.5% and the media revenues are expected to go from 14.6 billion in 2016 to 20.6 billion in 2019. Obviously, the profitability of the industry increases and that is why the salaries of professional athletes are going to grow as well. This does make a lot of sense, but some people still think that sportsmen are overpaid and do not deserve such high salaries. What is your opinion on this matter?