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1. The time value of money. It underlies virtually everything in business, is non-intuitive, and completely reorients your perspective on a wide range of issues. (Nearly as important is the inverse: the money value of time, also known as "Opportunity Cost").The time value of money is the value of money figuring in a given amount of interest earned over a given amount of time. The time value of money is the central concept in finance theory.For example, $100 of today's money invested for one year and earning 5% interest will be worth $105 after one year. Therefore, $100 paid now or $105 paid exactly one year from now both have the same value to the recipient who assumes 5% interest; using time value of money terminology, $100 invested for one year at 5% interest has a future value of $105. This notion dates at least to Martín de Azpilcueta (1491–1586) of the School of Salamanca.The method also allows the valuation of a likely stream of income in the future, in such a way that the annual incomes are discounted and then added together, thus providing a lump-sum "present value" of the entire income stream.All of the standard calculations for time value of money derive from the most basic algebraic expression for the present value of a future sum, "discounted" to the present by an amount equal to the time value of money. For example, a sum of FV to be received in one year is discounted (at the rate of interest r) to give a sum of PV at present: PV = FV − r·PV = FV/(1+r). http://en.wikipedia.org/wiki/Time_value_of_money2. Operations Research. The fact that there is likely to be a mathematically derivable answer to virtually any business question you can ask, gives you a different way of considering questions that you would previously have answered based on 'gut feel'.Operations research is a discipline that deals with the application of advanced analytical methods to help make better decisions. Employing techniques from mathematical sciences such as mathematical modeling, statistical analysis, and mathematical optimization, operations research arrives at optimal or near-optimal solutions to complex decision-making problems.Because of its emphasis on human-technology interaction and because of its focus on practical applications, operations research overlaps with other disciplines, notably industrial engineering and operations management, and draws on psychology and organization science. Operations Research is often concerned with determining the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost) of some real-world objective.http://en.wikipedia.org/wiki/Operations_research3. Negotiation basics. Once you understand the three critical components of your negotiating position (the overall market, everyone's alternatives, and your true bottom line) negotiating anything becomes a piece of cake.Negotiation is a dialogue between two or more people or parties, intended to reach an understanding, resolve point of difference, and to produce an agreement upon courses of action. Negotiation is a process where each party involved tries to gain an advantage for themselves by the end of the process. Negotiation is intended to aim at compromise.The study of the subject is called negotiation theory. Professional negotiators are often specialized, such as union negotiators, leverage buyout negotiators, peace negotiators, hostage negotiators, or may work under other titles, such as diplomats, legislators or brokers.http://en.wikipedia.org/wiki/Negotiation4. Financial statements. While these are purely technical rather than theoretical, understanding them is critical for anyone engaged in any facet of business operations.For a business enterprise, all the relevant financial information, presented in a structured manner and in a form easy to understand, are called the financial statements. They typically include four basic financial statements, accompanied by a management discussion and analysis:Statement of Financial Position: also referred to as a balance sheet, reports on a company's assets, liabilities, and ownership equity at a given point in time.Statement of Comprehensive Income: also referred to as Profit and Loss statement (or a "P&L"), reports on a company's income, expenses, and profits over a period of time. A Profit & Loss statement provides information on the operation of the enterprise. These include sale and the various expenses incurred during the processing state.Statement of Changes in Equity: explains the changes of the company's equity throughout the reporting periodStatement of Cash Flows: reports on a company's cash flow activities, particularly its operating, investing and financing activities.http://en.wikipedia.org/wiki/Financial_statements5. Integrity and Ethics. Last on this list relative to its specific relationship to business school, but first on any list of critical business concepts, it is imperative to understand how *pragmatically* important integrity is in the real world of business.Business ethics is a form of applied ethics that examines ethical principles and moral or ethical problems that arise in a business environment. It applies to all aspects of business conduct and is relevant to the conduct of individuals and entire organizations.The range and quantity of business ethical issues reflects the interaction of profit-maximizing behavior with non-economic concerns. Interest in business ethics accelerated dramatically during the 1980s and 1990s, both within major corporations and within academia. Today, most major corporations promote their commitment to non-economic values under headings such as ethics codes and social responsibility charters.http://en.wikipedia.org/wiki/Business_ethics

The Department of Homeland Security's (DHS) fall agenda proposes major changes to the H-1B visa program and seeks to redefine both ‘specialty occupation' and the 'employee-employer relationship,’ which currently requires a theoretical and practical application of a body of specialized knowledge and together with at least a bachelor's degree or its equivalent.The issue with a revision to ‘specialty occupation,’ specifically the singular degree requirement is that often the coursework of a related degree provides the specialized knowledge needed to perform job duties.Currently, if an employer determines that an applicant with a mathematics degree is most qualified for a computer programmer position, a technical evaluation must then be submitted with solid documentation demonstrating how the applicant’s coursework is directly connected to the job description, adding time and money to an already complex process.The employer-employee relationship is already intensely scrutinized by USCIS and is evaluated by weighing multiple factors such as who directly supervises the H-1B worker and whether supervision is performed on or off-site. These days, even the use of proprietary information and whether the end work product is directly linked to the H-1B employer's (petitioner's) business is closely examined.Additionally, the DHS is seeking to add requirements for wage levels. USCIS has argued that the job duties paying level 1 wages are not complex enough to qualify as specialized but too complex to be considered entry-level because they involve the exercise of judgment and thus require oversight from a wage level 3 supervisor. Quite the predicament.Also in the works is an update of the Adjustment of Status Procedures for “more efficient processing” by eliminating the concurrent filing of visa petitions and Form I-485 for all applicants seeking an immigrant visa in a preference category and changing the dates when applicants can file Form I-485.Another item on the DHS agenda is to establish a period of authorized stay for F-1 and other non-immigrants. Currently, F-1 students are admitted for a “duration of status” and allowed to remain in the country for the duration of their full course of study, including any period designated for practical training. DHS proposes to replace the “duration of status” system with “date certain” admissions as it apparently causes confusion and contributes to student visa overstay.Lastly, the DHS wants to rescind the February 2015 rule extending eligibility for employment authorization to certain H-4 dependent spouses of H-1B visa workers seeking employment based lawful permanent resident status (green card). The effect will be felt by companies as their labor turnover costs are expected to increase.Restricting H-1B visa issuance and increasing the difficulty for students and other high-skill foreign-nationals has a number of negative effects on our economy.Statistically, every H-1B visa creates almost two jobs for Americans on average. In smaller companies, it can create almost 7 jobs per each H-1B. Reducing H-1Bs reduces jobs for Americans at almost double a rate. Making these high skilled visas tougher to obtain for American companies forces jobs and businesses to leave America.Foreign students, who contribute enormously to higher-education institutions and more often than not enter in-demand STEM fields, have already begun to lose interest in attending US Universities.Indian students have decreased admissions by 28% while Chinese students have decreased by 24%. This translates to a loss of approximately $2 Billion in tuition to US universities annually.Other countries, like Canada and India, are already enjoying increased migration of American companies and foreign nationals to bolster their economy, especially in the tech industry.The US has lead the world in technology by importing people with the highest skills to our inviting and innovative environment. Visa regulations are chilling this environment and could cause us to lose our leadership position and adversely impact our economy.

This is a bit of a tricky question. I’ll share my story given I feel I am an example of a ‘beginner with no fucking experience’ into a full-blown career into quantitative finance, from roles to being a quant as well as being an i-banker.When I started trading, I was young and stupid, I had not much background in Maths, let alone a background in anything. I wasn’t yet 18. I was obsessed about how the financial markets worked. This as my dad received expensive investment letters in the mail (they don’t do that anymore) from the bank packed with stock recommendations. I saw my dad earn more money with stocks than I did working a crap supermarket job 5 days a week after school. All he did was follow advice given by the bank.. Could it be that easy?Given I was below 18, my dad opened another trading account under his name, I could put money there and started trading. I felt I had an advantage, because my dad received these letters from his private banker about stock recommendations. I noticed quickly, that these recommendations were full of shit unfortunately...The reasons which were given why a firm would go up or down just didn’t make sense to me. You don’t have to be 40 years old and 22 years of experience in trading to smell that something is off. 95 out of a 100 times it is just common sense. It could be as simple as, a firm get’s 10% of it’s revenue out of country X, while 90% is country Y. And the entire recommendation bit in the letter would be about how that particular 10% in country X could grow to 15%.. ‘Buy buy buy!’I was still in high school, but my grades suffered. Trading was much more fun that reading how my bones were called. I fucked up a serious amount of modules at high school. I never studied, did all logic thinking. So only did well in mathematics and economics.I enjoyed reading about firms I was investing in. I realized that I didn’t understand certain terminology in annual reports. I didn’t understand why goodwill could change so significantly over a year and so forth. So I had a marker and shit-loads of annual reports I went through many firms I had an interest in. Reading - marking - reading - marking - reading, it even became more important on the toilet…Whatever I didn’t understand I would write down. Looking it up on books, everywhere. I was trying to be like a sponge. Soaking up information…Back at high school, I was about to graduate but I needed a thesis for graduation. I did it on, how convenient, trading. Technical indicators, fundamental indicators. I was back-testing all sorts of indicators in Excel. It was fun. However, I was a duly noted idiot who still had to learn the world, I still didn’t grasp the bigger picture. I graduated with a flunky degree, I fucked up many modules except for Maths, Economics and History (I had a genuine interest in history so I read the entire book for 1 semester in 2 weeks).Finally I found myself studying in London, doing an mixed econometric/economics/finance degree. I had 2 finance modules, 2 economics modules, 2 econometric modules every year, and 2 modules I could pick myself. I came into a class full of people who followed the; “I want to become an investment banker and work for Goldman Sachs” dream. It was delusional and not very motivating. Because of all my pre-studies I’ve done as a hobby, I absolutely nailed every module. I became bored incredibly quickly. We had Bloomberg terminals at university and that caused me to spend every free single minute on such a computer (this includes Sundays). I wrote down every ticker, what it did, what it could do. All the ins’ and out’s. The funny tickers, how to track this or that. I wrote it all down. Before the end of the first semester as a first year BSc, I was asked to be the assistant of a Finance professor for his finance MBA and MSc classes. Finally some fun.. fuck.. all those guys were very similar minded: “I want to become an investment banker and work for Goldman Sachs”Many of the students I worked with, were all crazy about trading. They gave me all sorts of advice. They told me about all sorts of candle sticks patterns, MACD, RSI, Aroon indicator, yada-yada…So a period of testing started. I had friends of mine telling me how ‘awesome’ these indicators were, I had data of the stock market, and the variables which ‘apparently’ predicted the stock market. It wasn’t difficult to start putting back-testing models together. I quickly realized that all this technical analysis is complete horse shit and I found dozens’ of ways to debunk every technical indicator thrown at me. Again, I became disillusioned.My maths’ teacher was a guy who studied Maths at Cambridge in the 70′s. He was a old-school ‘you can’t fuck with me’ kind of type. He was the only one who kept challenging me - and therefore I picked him as my thesis supervisor - how convenient - a topic about automatic trading with stocks. This time, in contrast to my high school time, I had much more developed models, I had much more data, I had a professor kicking my nuts, I didn’t just have technical variables, I had dozens of macro variables, from precipitation data to gold price, unemployment to house prices to price of oranges. Plus, I actually automated my models into live automatic trading models through an API. I did well during my studies. Not because I studied, but because I enjoyed. I didn’t study to get an A, I couldn’t give a fuck about grades. I enjoyed understanding the topic, hence when asked during an examination if I understood in the form of questions, it wasn’t difficult to get boat loads of A’s every year. I was the only one of my class who graduated with a first.I started off my working career in Standard and Poors, looking at credit ratings and models (I know.. don’t start..). I was curious why certain models of Moody’s, Fitch or S&P would be so different. What drives this difference? Once again I was quickly disillusioned. A friend of mine at S&P told me to study Quantitative Finance as a masters. And so I did - in the continuous search for answers which started before I finished high school.Going back to study, I quickly realized how backwards thinking the world of academia is in comparison to what is ‘practical applications of mathematics in the financial industry’. I saw what professors had to do to make their articles ‘publishable’. It wasn’t as interesting as I thought it would be, you could qualify it as having to be ‘politically correct’. During studies we had all this crap about Markowitz, random models, Euler equations, Brownian motions. All this shit about risk adjusted performance metrics, the Sharpe ratio was the epitome of stupidity. It was so out of date. I’ve had a few interviews with proprietary trading boutiques and hedge funds, and they weren’t able to convince me. I went for a quant position at a big bank instead. I started working before I graduated, yet needed a thesis. Again, I realized the conflict between the world of academia and the world of finance about how to practically apply mathematics in real world scenarios. It was like water and fire. It fucking sucked horse manure given my thesis supervisors didn’t see the light on the same topic. One point, at that bank, I once had lunch with an older gentleman who did equity research. He left me annual reports of firms he investigated and marked every metric he felt smelled off and dropped it at my desk. Once again I felt the young boy, and I enjoyed that much more than trying to do something ‘academically justifiable’.I’ve worked as a Quant for a while, getting great satisfaction until that ended. The majority of other QF students all went for investment banking jobs. After getting fucked up eyes having to deciphered endless lines of codes I felt it was time for a change. The switch quant to investment banking was extremely easy. I realized the search for ‘what drives this or that - quantitative analysis’ was still essential in the i-banking industry, so with a quantitative background I enjoyed looking at similar processes as I’ve done ever since my last years at high school. After that I was job-hopping across the world, ending up everywhere, in all sorts of functions, all quantitative analysis related. All about ‘why does this happen, what is the driver?’ - ‘does this make sense’ and so forth.Moral of the storyI am an example of someone who had bat-shit knowledge about maths as a young kid, yet ending up doing a quantitative career in finance. But questions like; “how do I break into quantitative analysis” are questions I’ve never asked myself when I as young. I don’t really know anyone who did either.. You just do - and progress from there.My entire path was always about, why the fuck does this happen? And why the fuck does that happen?My best learning examples have been peers who were more clever than me (my math’s professors at university for example) or trading buddies and quants at work. Books and studies, events and conferences in general were absolutely fucking useless and tend to be extremely politically correct. Especially conferences.So on the question; “can you teach yourself?” Yes!I still code, I only do MATLAB nowadays and still work in the financial industry.