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  • JAKE XIA: This is the second time we are having this class.

  • We had it last year in a smaller version.

  • That was for six units of a credit,

  • and we had it once a week.

  • And mostly practitioners from the industry,

  • from Morgan Stanley, talking about examples

  • how math is applied in modern finance.

  • And so we got some good response last year.

  • So, with the support of the math department,

  • we decided to expand this class to be 12 units of credit

  • and have twice a week.

  • So, we have every Tuesday and Thursday afternoon

  • from 2:30 to 4:00, as you know, in this classroom.

  • So last year, Dr. Vasily Strela and I-- by the way,

  • I'm Jake Xia and that's Dr. Vasily,

  • and we were the main instructors last year.

  • Now we doubled it up to four main instructors.

  • That's Dr. Peter Kempthorne and Dr. Choongbum Lee.

  • The reason we doubled up the main instructors

  • is we have newly added math lectures, mostly focusing

  • from linear algebra, probability to statistics,

  • and some stochastic calculus to give you the foundation

  • to understand the math will be used

  • in those examples in the lecture taught by the practitioners

  • from the industry.

  • And the purpose of this course is really

  • to give you a sampling menu to see how mathematics is applied

  • in modern finance and help you to decide if this is a field

  • that you would be--

  • RECORDED VOICE: Thank you, for using WebEx.

  • Please visit our website at www.webex.com.

  • JAKE XIA: OK, you heard that.

  • And so hopefully, this will give you enough information

  • to decide this is a field you would like to pursue

  • in your future career.

  • In fact, last year when we finished the class,

  • we had a few students coming to work in the industry.

  • Some work at Morgan Stanley, some work at elsewhere.

  • So that's really the goal.

  • And at the same time, obviously, you

  • will further solidify your math knowledge

  • and learn new content.

  • And we put the prerequisite about the math part a bit

  • later.

  • So I will use today's first lecture's time

  • to give you an introduction, really,

  • to prepare you some basic background knowledge

  • about the financial markets.

  • Some terminologies will be used, which

  • you may not have heard before.

  • So before I get into the introduction,

  • I always like to know who are actually in the classroom,

  • so let me ask you a few questions.

  • You just need to raise your hands

  • so I know roughly what kind of background and where you are.

  • So how many undergraduate students are here?

  • So I would say 80% percent.

  • How many graduate students are here, just to verify?

  • Yep, that's about right, 20%.

  • And how many students are in finance and business major?

  • Just one.

  • And how many of you are a math major?

  • Most of you.

  • How many of you are engineering majors?

  • A few.

  • How many of you actually are from other universities?

  • Great, because last year we had quite a few,

  • so I want to specifically tell you

  • that you're very welcome to attend the classes here.

  • So it's open door.

  • And last year I remember we had a couple of students

  • from Harvard.

  • That's where I actually work right now.

  • I forgot to mention that, but I'm

  • affiliated with both the math department and the Sloan school

  • here.

  • So anyway, thanks for that.

  • We will be doing a bit more polling along the way,

  • mainly to get feedback of how you feel about the class.

  • Last year we had it online, so if you

  • feel the class is going too fast,

  • or the math part is going too slow,

  • or the finance part is a bit confusing,

  • the easiest way is really just to send us

  • emails, which you will find from the class website.

  • So anyway, today--

  • VASILY STRELA: And all of us got MIT emails.

  • JAKE XIA: Yes.

  • We all have MIT emails, which are listed on the website.

  • VASILY STRELA: [INAUDIBLE].

  • JAKE XIA: And obviously, we have offices here.

  • You can easily stop by Peter and Choongbum's offices.

  • And Vasily and I probably will be less often on campus,

  • but we'll be here quite often and definitely love to be more.

  • So anyway, I will start today's lecture with a story,

  • and a quiz at the end.

  • Don't worry, it's not a real quiz.

  • Just going to ask you some questions

  • you can raise your hand and give your answer.

  • But let me start with my story.

  • This is actually my personal story.

  • I want to tell you why I tell the story later.

  • But the story actually was in the mid '90s.

  • I just left Salomon Brothers -- that was my first financial

  • industry job -- to go to Morgan Stanley in New York to join

  • the options trading desk.

  • So the first day, I sat down, I opened the trading book,

  • I found something was missing.

  • So, I turned around, I asked my desk quant.

  • I said, where is the vega report?

  • So, let me show you.

  • So that's the story.

  • So I'm obviously not going to tell you the story of Pi

  • or "Life of Pi."

  • That's not a financial story.

  • The rest of the story, alpha, beta, delta, gamma,

  • theta, which you will learn from Peter and Choongbum

  • and Vasily's classes.

  • So I'm going to talk about vega.

  • So by the way, before I tell you the story,

  • what's unique about vega on this list?

  • AUDIENCE: It's not a Greek letter.

  • JAKE XIA: It's not a Greek letter.

  • That's right.

  • So I turned around and asked my desk quant, I said,

  • where's the vega report?

  • But how many of you actually know what a vega is?

  • OK, lot of people know.

  • So anyway, I'm not going to-- just

  • for the people who haven't heard about it before, it's

  • a measurement about a book or portfolio

  • or position's sensitivity to volatility.

  • So, what is volatility?

  • Which again, you will learn more in rigorous terms

  • how it's defined in mathematics.

  • But the meaning of it is really a measurement or indication

  • of how volatile, or what's the standard deviation of a price

  • can change over time.

  • That's all you need to know right now.

  • I'm not going to ask you questions later.

  • So my desk quant look at me, said--

  • this is supposed to be options trading

  • desk, so he look at me puzzled.

  • So instead of answering my question,

  • he handed over me a training manual

  • for new employees and new analysts.

  • So I opened the training manual and looked it through.

  • I actually found my answer.

  • So actually, at Morgan Stanley this is not called vega,

  • it's called kappa.

  • So now, I remember to call it kappa.

  • Kappa is actually a Greek letter.

  • So further, I look on the same page

  • there was actually a footnote, which I copied down.

  • So the footnote about why it's called kappa at Morgan Stanley.

  • Kappa is also called vega by some uneducated traders

  • at the Salomon Brothers.

  • That's where I came from.

  • I just joined.

  • They have mistaken vega as a Greek letter

  • after gambling at Vegas.

  • So anyway, so that was my first day.

  • So obviously, I learned how to call kappa

  • very quickly, because I came from Salomon Brothers.

  • And I called it kappa in the last 17 years,

  • but you will hear people calling it vega.

  • Obviously, I have probably more people calling it the vega.

  • But anyway, so that's my first day at Morgan Stanley.

  • But why did I tell you the story?

  • What point I try to make?

  • So this story is actually-- when you think about it,

  • mathematical or quantitative finance is a rather new field.

  • A lot of these terms were newly introduced.

  • And the pricing model of options, as you know,

  • was introduced in the Black-Scholes in the '70s,

  • or some of the ground work may be done a bit earlier.

  • But it's not like finance was a quantitative profession

  • to start with.

  • So what we witness in the last 30 years

  • was really a transformation of the trading profession coming

  • from mostly under-educated traders.

  • Some of them typically joined the firms in the mail room

  • and became trader later on.

  • That's typical career path.

  • And to nowadays, if you walk on the trading floor,

  • you talk to the traders, most of them have advanced degrees

  • and quite a few of them have very high training

  • in mathematics and computer science.

  • So what has changed over the last 20 or 30 years?

  • I myself, personally, was probably one of the data

  • point experiencing this change.

  • And I certainly didn't expect I would

  • be doing this when I was at MIT, but I

  • did that in the last 20 years.

  • So the point I'm trying to tell you

  • is, before you dive into any details of mathematics

  • or any concept in finance in this class, just bear in mind,

  • this is a field developed in the last mostly 30 years, or even

  • shorter.

  • And what you really need to ask questions

  • is-- it's not really is it right or wrong in mathematics,

  • is it right or wrong in physics?

  • So, how the concepts are established

  • and defined and verified.

  • Because this is a field-- the transformation

  • about the participants, products, models, methodology,

  • everything are changing very rapidly.

  • Even nowadays, they're still changing.

  • So with that, I will give you some background

  • on how the financial markets actually started,

  • and that's really the history part of this industry.

  • So, when we talk about markets, we know in early days

  • people need to exchange goods.

  • You have something I don't have, I

  • have something you don't have, so there's exchanges.

  • Then it becomes centralized.

  • There are stock exchanges, futures exchanges all

  • over the world where these products will

  • be listed as securities on these exchanges.

  • That's one way of trading, which is centralized.

  • Obviously, in the last 10, 15 years,

  • now we have ECNs, electronic platforms.

  • Trade over-- you know, even larger volume of those trades.

  • So, financial products is really just one form of trading.

  • There are many other ways of trading aside from exchanges.

  • One of them, which is called OTC,

  • is over-the-counter, meaning two counterparties agree

  • to do a trade without really subject to the exchange rules,

  • or the underlying trading agreement does not

  • have to be a securitized product, or standardized,

  • or whatever ways you define it.

  • And the different regions have different exchanges

  • and markets, as well.

  • And they typically specialize in local products, local company

  • stocks, local bonds, and local currencies.

  • So, there are many different forms.

  • So again, what's in common?

  • That's the question you need to ask.

  • Also, you don't know the specifics.

  • And the currencies, money itself, are also traded.

  • And that's where different currencies

  • issued by different countries.

  • So, when we talk about trading stocks--

  • there are also people trade baskets of stocks,

  • trade groups of stocks together, and that's

  • stock index or indices.

  • So, there are different products.

  • How the stock get listed on the stock exchange?

  • It goes through IPO-- Initial Public Offering process.

  • So, when a company changes from private to public,

  • it goes through this IPO process.

  • It's called primary market, primary listing.

  • And once the stock is listed on the exchange

  • and it becomes traded in the market,

  • we call it secondary trading.

  • So, that's after the primary market.

  • And equity or stock is one form of trading

  • or one form of financial products.

  • What are other forms?

  • Loans.

  • Actually, debt products are more generic than equity products.

  • When you started thinking about it,

  • what is really finance is about?

  • It's really about someone has money, someone doesn't.

  • Someone has money to lend out, someone needs to borrow money.

  • So, that's loan.

  • Loan is really a private agreement

  • between two counterparties or multiple counterparties.

  • When you securitize them, they become bonds.

  • And when you look at bonds, every government

  • will issue large sovereign debt.

  • So, US government has large outstanding US Treasury debt--

  • bonds, notes, bills.

  • And corporates have issued a lot of debt product, as well.

  • They borrow money when they need to build a new factory

  • or expand.

  • Universities borrow money.

  • When MIT needs to build a new building, some of the money

  • will come from the endowment support,

  • some will come from some other form of research budget,

  • or some will come from debt financing.

  • Just borrow from the public-- local governments,

  • states, counties, even.

  • So, they have various forms.

  • So, that's debt product.

  • Commodities, actually, you know.

  • Metal, energy, agriculture products

  • are traded, mostly in the futures

  • format and some in physical format,

  • meaning you take deliveries.

  • When you actually buying and sell,

  • you build a warehouse to take them.

  • You ship a tank to store above the ocean.

  • And the real estate, you're buying and sell houses.

  • 2008 financial crisis, if you read about it,

  • this has a lot to do with the real estate

  • market, the mortgages, and asset-backed securities.

  • So, I'm not trying to give you all the definition,

  • dumping the information on you.

  • But I like you at least hearing it once today,

  • and then you have more interest, you can read on the side.

  • So asset-backed securities is when you have an asset,

  • you basically issue a debt with the asset backing it.

  • And how do you rate the asset's risk level

  • and what's the income stream, cash flow?

  • And before 2008 financial crisis,

  • as you heard, large amount of CMBS-- basically,

  • it's a commercial real estate backed

  • securities, mortgage securities, and the residential, as well.

  • And further of all of these, you heard probably a lot

  • about the derivative products.

  • So, that started with swaps, options.

  • And the structure of the products, it

  • become more tailor-made for either investors or borrowers

  • to structure the products in a way to suit their needs.

  • And some of the complexity of those structured products

  • become quite high, and the mathematics

  • involved in pricing them and the risk management

  • become rather challenging.

  • So coming back to the players in the market,

  • one large type of player is really bank.

  • Essentially, after 1933 Glass-Steagall legislation,

  • there were two main types of banks.

  • One is called commercial bank, the other is investment bank.

  • Commercial bank is supposedly, you're

  • taking deposits and lend out the money,

  • and doing more commercial services.

  • Investment bank supposed to focus on the capital markets,

  • raising capital, trading, and asset management.

  • But obviously, after 1999, the Glass-Steagall was repealed.

  • There's no longer that.

  • Some people blame that, and probably

  • for a very good reason, for the cause of 2008 financial crisis.

  • But I want to tell you how currently investment banks are

  • organized.

  • Vasily just mentioned he works in the fixed income.

  • So banks typically organized by institutional business

  • and asset management.

  • So, within the institutional client business,

  • it has typically three main parts.

  • Fixed income, which trade the debt

  • and the derivative products.

  • Equity, trade stocks and the derivative products.

  • And IBD, stands for Investment Banking Division,

  • which really covers corporate finance,

  • raising capital, listing a stock, IPO, and merger

  • and acquisition, and advisory.

  • So that's how banks are organized.

  • Outside banks, other players, basically, the asset managers,

  • are obviously a very big force in the financial markets.

  • So the question a lot of people ask

  • is, is this a zero sum game?

  • I'm sure you've heard this many times.

  • So, in the financial markets, some people win,

  • some people lose.

  • A lot of times, it depends on the specific products you

  • trade, the market you're in.

  • It is, lot of times, pretty net zero.

  • But why do we need financial markets?

  • This comes back to what I described before.

  • Because something existed-- actually,

  • there's a need for it.

  • It's really the need to bridge between the lenders

  • and the borrowers.

  • That's really coming down to the essential relationship.

  • So, investors who have money need

  • to have better yield or better return, better interest.

  • In the current environment, when you have a savings account,

  • you don't really earn much at all.

  • And so you would have to take more risk to generate more

  • return, or you have longer horizon

  • CDs, other type of products, or trade the stocks.

  • So, when somebody has money, when you trade stocks,

  • you're essentially-- you're buying a stock,

  • you give the money somewhere.

  • Supposedly, it will go to the company.

  • Company use the money to generate a better return.

  • And for the borrowers, whoever needs money,

  • they need to have access to the capital.

  • So obviously, different borrowers have different risks.

  • Some people borrow money, never return.

  • So, never generate any returns, or never even

  • return the principal.

  • And so the trade between lenders and the borrowers,

  • is again, essentially the main driver

  • of the financial markets.

  • So, a few more words about the market participants.

  • So, banks and so-called dealers play the role of market making.

  • What is market making?

  • So, when you or some end user go to the market,

  • wants to buy or sell, typically, if there's no market,

  • you don't really find the match.

  • And some of the products you want to buy or sell

  • may not necessarily be liquid.

  • So, the dealers step in the middle, make you a price.

  • Say, OK, you want to buy or sell.

  • I can tell you-- this stock, I make you price.

  • $0.99, and that's my bid. $0.95, that's my offer.

  • So, that's the price I'm willing to buy or sell.

  • But what the result of the trade-- the dealer

  • actually takes the other side of your trade.

  • So, they take principal risk, in this case.

  • So, that's the difference between dealers

  • and the brokers.

  • So, brokers don't really take principal risks.

  • If you want to buy something or sell something,

  • if I'm a broker, I don't make you a price.

  • I go to the market makers.

  • I actually put two people together,

  • matchmaking, make that trade happen.

  • So, I earn the commission.

  • So, that's a broker's role.

  • So obviously, there are individual investors,

  • retail investors, same meaning.

  • Mutual funds, who actually manage public investors' money,

  • typically in the long-only format.

  • Long means you buy something.

  • So, you don't really short sell a particular security.

  • Insurance companies has large asset.

  • They need to generate a return, generate cash flow

  • to meet their liability needs.

  • So, they need to invest.

  • And the pension funds, same thing.

  • As inflation goes higher, they need

  • to pay out more to the retirees, so where do you get the return?

  • Sovereign wealth fund, similarly, endowment

  • funds-- they all have this same situation,

  • have capital and needs to deploy and to make better return.

  • So this other type of players, hedge funds.

  • So, how many of you have heard hedge funds?

  • OK, good.

  • Almost everyone.

  • And Peter mentioned that he used to work at a hedge fund.

  • And so, there are different types

  • of strategies, which I will dive into a bit more,

  • but hedge fund play the role in the market-- they basically

  • find opportunities to profit from inefficient market

  • positioning or pricing, so they have different strategies.

  • And the private equity is different type of funds.

  • They basically look to invest in companies

  • and either take them private or invest in a private equity form

  • to hopefully improve the company's profitability,

  • and then catch up.

  • And governments obviously have a huge impact on the market.

  • So, we know in the financial crisis, government intervened.

  • And not only that, at the normal market condition,

  • government always have a very large impact on the market,

  • because they are the policymakers.

  • They decide the interest rate and interest rate curve.

  • And the different policies they push out, obviously,

  • will generate different outlook for the future markets,

  • therefore, profitability.

  • Then the corporate hedges and the liabilities.

  • When corporates borrow money, they create some risk,

  • so they need to be sensitive to the market, it changes.

  • So, to summarize the types of trading.

  • The first type is really just hedging.

  • That means you're not proactively

  • adding risk to what you have.

  • You already have some exposure.

  • Just give you an example.

  • Let's say you borrow money, you bought a house,

  • so you have mortgage.

  • So, let's say it's a floating rate mortgage payments.

  • And you're worried about interest rates going higher,

  • so you can lock that rate in into the fixed rate format.

  • Or you can find ways to hedge your exposure.

  • Or your corporate has a large income coming from Europe.

  • So, you have euros coming in, but you're not sure

  • if euro would trade stronger to the US dollar in the future,

  • or trade weaker.

  • If you think it will be stronger, you just leave it.

  • But if you think it will trade weaker,

  • so you may want to hedge it, meaning

  • you want to sell euro and buy US dollars.

  • And so that's the hedging type.

  • The second type, as I mentioned, is a market maker.

  • So, market maker also takes principal risk,

  • but the main source of profit is really to earn the bid offer.

  • I gave you the example $0.90 bid, $0.95 offer.

  • So, that's what the market maker is trying to profit from.

  • But obviously, they have residual risks

  • sitting on the book.

  • Not every trade is matched.

  • So, how to optimize those group of trades,

  • that's what market maker is doing.

  • Most of the bank's dealers are market makers.

  • In the new regulation, obviously, proprietary trading

  • is banned, right?

  • And so the third type is really the proprietary trader,

  • the risk taker.

  • So, these are the hedge funds or some portfolio managers.

  • They need to focus on generating return and control the risk.

  • So, that's where the beta and alpha, the concept comes in.

  • So, if you're a portfolio manager, some people say,

  • don't worry.

  • Don't go pick any stocks.

  • Just buy S&P 500 index fund.

  • Very cheap.

  • You can pay very little cost to do it.

  • That's true.

  • But if you want to beat the S&P 500

  • index-- let's assume we call S&P 500 index fund is asset b.

  • So, the return of that, R(b).

  • That's a return of that index.

  • Now, you have a portfolio a.

  • Your time series of return of your asset a,

  • obviously, you can do linear regression.

  • A lot of you are math major here,

  • and you can find a correlation between those two time series.

  • So, how the two returns are related in a simplified form.

  • So you can say, this actually-- somehow it came out.

  • It's supposed to be alpha and beta,

  • but it turned out to be the letters.

  • So, in a short description, beta is really-- just

  • think as correlated move with the other asset.

  • Alpha is really the difference in the return.

  • It's a format.

  • You want to beat S&P 500, so you want to basically

  • have certain tracking of this index,

  • but you want to return more on top of that.

  • So let me just go in bit of details

  • of how each type of trade actually occurs.

  • So, when we talk about hedging, I

  • mentioned the currency example.

  • Let me give you another example.

  • There are a lot of people issue bonds, or issue debt.

  • So this example I'm going to give you is,

  • let's think about Australian corporate.

  • Because interest rate in Australia

  • is higher than in Japan, so typically,

  • people like to borrow money in Japan, because you

  • pay smaller interest.

  • And they invest it in Australia.

  • You earn higher interest rate.

  • So let me ask you a question.

  • Who can tell me, why don't people

  • just do that all day long, just borrow from Japan

  • and invest it in Australia?

  • Then that interest rate, I'm giving you

  • example of a difference is about 3.5% for the roughly 10 year

  • swap rates.

  • Yeah, go ahead.

  • AUDIENCE: [INAUDIBLE].

  • JAKE XIA: Right.

  • Because you invest in the Australia

  • Ozzie, Australian dollar.

  • The Australian dollar may become weaker to the yen.

  • You may lose all your profit, or even more.

  • And further, if everybody plays the same game, then

  • when you try to exit, you have the adverse impact

  • of your trade.

  • So, let's say you think that's the right time to do it,

  • but then at one time, you wake up,

  • you said, huh, I think too many people are doing this.

  • I want to hedge myself.

  • So, what do you do?

  • AUDIENCE: [INAUDIBLE]?

  • JAKE XIA: Yep.

  • So, you try to lock in, right?

  • So basically, you sell the Australian dollars,

  • buy the Japanese yen.

  • Or on the interest rate terms, you

  • say you'll basically pay the Australian dollar in the swap

  • leg, and receive yen.

  • This involves foreign exchange trade, interest rate swap,

  • and the cross-currency swap.

  • So, your answer about currency forward is roughly right,

  • but obviously involves a bit more in actual execution.

  • So that's just to give you example.

  • Even if you are not a finance guy,

  • you work in a corporate, you just do you import, export,

  • or building a factory, you have to know, actually,

  • what the exposure is.

  • So, risk management, nowadays, becomes pretty widespread

  • responsibility.

  • It's not just the corporate treasury's responsibility.

  • So, that's on the hedging side.

  • Obviously, if you are Intel, for example,

  • you sell a lot of chips overseas.

  • And your income-- actually, Intel does

  • have lot of overseas income sitting outside the States.

  • So, the exposure to them is if the exchange rate fluctuates,

  • dollar becomes a lot stronger, they actually lose money.

  • So, they need to think about how to hedge the revenue produced

  • overseas.

  • And obviously, for import-exporters,

  • that's even more apparent.

  • And if you're entering in a merger deal,

  • and one company is buying another,

  • you need to hedge your potential currency

  • exposure and your interest rate exposure.

  • And whatever is on the assets, or the liability,

  • or the balance sheet, you need to hedge your exposure.

  • So we talked about hedging activity.

  • Let's talk about market making.

  • So if it's a simple transparent product,

  • everybody pretty much knows where the price is.

  • So, if you buy Apple stock, I think a lot of people

  • know pretty much where it is.

  • You may even have it on your cellphone,

  • know where that stock is.

  • But if it's not transparent, so what do you do?

  • So, if instead of asking you where Apple is,

  • probably you're going to tell me $495 today.

  • AUDIENCE: I don't really know.

  • JAKE XIA: OK.

  • But if I asked you instead, what is the call option

  • on Apple stock in two month's time?

  • I'll give you a strike, let's say, 500.

  • So you're probably less transparent.

  • So that market maker comes in to provide that liquidity,

  • and then takes the risk.

  • They manage the book by balancing those Greeks, which

  • I mentioned earlier.

  • Delta, which describes the [INAUDIBLE] relationship

  • of this whole book to the underlying stock, or underlying

  • whatever currency.

  • That's called delta.

  • Gamma is really the change of the portfolio.

  • Take the derivative to the delta,

  • or to the underlying spot.

  • So, that's second-order derivative.

  • Delta is the first order.

  • So gamma, now you have curvature or convexity coming in.

  • And theta is really-- nothing changes in the market.

  • Nothing changes in your position.

  • How your trading book is carrying or bleeding away

  • money.

  • And we talk about the volatility exposure was vega.

  • And on top of that, what are the tail risks?

  • What are the events can actually get you into big trouble?

  • So people use value at risk.

  • So you will hear this "VaR" concept

  • in some of the lectures, which is also, obviously,

  • a very important concept.

  • I think Peter will-- or Choongbum will-- probably

  • Peter will teach.

  • Then capital.

  • How much capital are you using?

  • It becomes a very important issue nowadays.

  • And balance sheet.

  • Again, you have asset, you have liability.

  • How do you leverage?

  • How much leverage you have?

  • Before the crisis, for example, lot of the banks leverage up

  • 40 times, meaning when you have $1, you had $40 exposure.

  • So when the market moves little, you get wiped out.

  • That's really what amplified in the 2008 financial crisis.

  • And how do you measure the asset in balance sheet

  • when you have derivatives rather than a straightforward

  • notional?

  • So lot of quantitative type of people

  • like to focus a bit more on the risk taking side,

  • because people heard stories about successful cases

  • of some hedge funds using high math.

  • They generated very impressive returns

  • and they seem to have an edge.

  • So now, people focus on trading strategies.

  • So that falls into the category of proprietary trading or risk

  • taking.

  • So that you can just simply doing directional trading

  • strategies.

  • Just go long or short the stock.

  • That's very simple.

  • Those so-called the gut traders, gut feeling.

  • Go with your gut.

  • You don't even think.

  • You say, I'm eating curry today, so I go long.

  • I'm eating rice tomorrow, so I go short.

  • So, this arbitrage.

  • Arbitrage is really to find the relationships between prices,

  • and try to profit from those relationship mispricing.

  • This is actually very interesting.

  • Not many people focus on arbitrage,

  • because lot of people are gut traders.

  • You essentially just watch your own market.

  • You don't really care what's going on.

  • If you trade gold in the States, the gold price

  • happen in Asia and in Europe matters, right,

  • because you're trading the same thing.

  • If they are not priced the same way,

  • you can profit from the difference.

  • And that's just a simple example.

  • But a spot price versus forward price, that's

  • a deterministic relationship.

  • It's a mathematical relationship.

  • If that relationship breaks down, you can also profit.

  • So there are many examples mathematical relationship

  • which gives you the arbitrage opportunity.

  • The other type is called a value trader, or relative

  • value strategies.

  • Think there's a deterministic, temporary mathematical

  • relationship.

  • You look at the longer term in horizon,

  • trying to determine what is really the underlying

  • value of a particular instrument,

  • then trade on the relative value.

  • Obviously, there are successful value investors out there.

  • And the systematic trader builds computer models.

  • One example is trend following, so just follow the price trend.

  • That used to be an effective strategy for some time,

  • but when lot of people doing the same thing, that

  • becomes much less effective.

  • Or momentum, same thing.

  • Stat arb, finding statistical relationship

  • among large number of stocks, then

  • trade at the higher frequency.

  • And fundamental analysis, you're really

  • trying to understand what's going on in the world.

  • What is the trade balance?

  • What is the earning potential of a company?

  • What's the trade balance of a country?

  • What is a policy change?

  • What does it mean when Federal Reserve

  • announce they're going to taper the quantitative easing?

  • Why the stock market is sold off in the last couple months,

  • especially why stocks in India, Brazil, Indonesia,

  • sold out more.

  • Why is that?

  • So it goes through those fundamental analysis.

  • And there are special situations.

  • Some companies are going through particular difficulties,

  • assets are priced very cheaply.

  • So, there are firms out there -- you probably heard Bain Capital

  • and many others -- where they focus on these private equity

  • and special situation opportunities.

  • So what have all of these to do with mathematics?

  • Where does math come in?

  • How do you use math?

  • So, I want to give you some aspects of that.

  • So from my personal experience, I joined the market,

  • really start to working on pricing models.

  • So, that's the first area.

  • So, math is very effective, because when

  • you, your bank, your corporate, you

  • want to buy some financial instruments,

  • you have to know where is the price.

  • It's easy to observe a stock in the market,

  • but when it comes to more complex products,

  • they just take one step forward on the complexity,

  • which is the option.

  • You have to know how to price an option.

  • So, that's where the math comes in.

  • You actually have to be able to solve differential equations

  • to get a model price, then you obviously

  • adjust to your assumptions to fit into the market.

  • So, pricing model, which Vasily and many of his colleagues

  • can tell you more-- which is very much

  • a very interesting and challenging area.

  • How do you price all these instruments?

  • And when I say pricing, it's not in the narrow definition

  • of just coming up with the price.

  • When you build a pricing model, you also

  • generate the risk parameters of these instruments,

  • and how do you risk manage them.

  • So, that comes to the second part.

  • So math is very useful in risk management,

  • which I will give you some -- not quiz --

  • questions after this slide.

  • You can see that risk management itself is very challenging.

  • It's not a purely mathematical question,

  • but yet, math plays a very important role

  • to quantify how much exposure you have.

  • Then, the third is trading strategies.

  • Again, I think a lot of people with math background,

  • or in general, people are looking

  • for the so-called holy grail trading strategies.

  • It's almost like perpetual motion machines people

  • looking for 100 years ago.

  • You just turn it on.

  • It makes money by itself.

  • You go to sleep, you go on vacation, you come back,

  • you'll have more in your bank account.

  • Obviously, that's not going to happen.

  • The robotrader, a robotic trader, is a dream.

  • It has its place or its use, but it's a fast evolving market.

  • You have to constantly either upgrade your research

  • and adjust your strategies.

  • There's no such thing you can build and leave it alone,

  • it runs for itself forever.

  • But I just want to mention that because maybe

  • towards the end of the term you will feel, hmm,

  • I came up with this brilliant trading strategy.

  • I think it's going to make money forever.

  • Please let me know first.

  • AUDIENCE: And me second.

  • PROFESSOR: So, I want to leave some time to Vasily.

  • Actually, he can give you some examples

  • of projects of last year's students

  • who actually came to this class and did some real application

  • at Morgan Stanley.

  • But before I hand it over to Vasily,

  • let me ask you some questions.

  • I just want to-- not really to quiz you, just give you

  • the sense how math and intuition and judgment

  • can come into the same place.

  • So, let me first give you an example I call risk aversion.

  • So, you are facing two choices, choice A and a choice B. Choice

  • A being you have 80 chance to lose $500.

  • You have 20% chance to win $500.

  • That's pretty clear, right?

  • That's choice A. Or choice B, you basically

  • just lock in you have 100% chance to lose $280.

  • Let me ask you, for whoever likes to choose choice A,

  • please raise your hand.

  • One, two, three, four.

  • About six out of say, let's call it 50.

  • So, can I ask you why you think choice A makes sense?

  • AUDIENCE: So, I know it's a lower expected value,

  • but I enjoy gambling and I would rather take the chance of--

  • JAKE XIA: Right, because you don't want to lock in that $280

  • loss, right?

  • That, or you still have 20% chance to win.

  • For the ones raised their hand for choice A,

  • are there any other reasons?

  • Same reason.

  • AUDIENCE: [INAUDIBLE]

  • JAKE XIA: I assume the rest of you

  • would choose choice B, unless you-- Neither?

  • How many of you choose choice B?

  • Choice B. And are there anybody think neither is right?

  • You have to choose.

  • No, you have to choose.

  • So, either choice A or choice B.

  • So, let me just talk a little bit about this.

  • Again, I'm not trying to tell you which one is right,

  • but I just share my thoughts how we look at these.

  • Why it called risk aversion?

  • So, this is very common human behavior.

  • When you go to the market, you buy a stock.

  • When the stock goes up, makes bit of money,

  • the natural tendency -- for especially someone is new

  • to the market -- is to let's take profit.

  • Let's sell.

  • Oh, I made $1000.

  • I made $500.

  • Let's go have a nice meal or whatever.

  • Buy an iPad.

  • But when the stock loses money, what's the natural tendency?

  • AUDIENCE: [INAUDIBLE]

  • JAKE XIA: That's--

  • AUDIENCE: [INAUDIBLE]

  • JAKE XIA: I think natural tendency, lot of people

  • will keep it.

  • I think if you have the discipline to get out,

  • that's great.

  • Trading is really all about how do you risk manage,

  • have the discipline, and how to manage your losses.

  • The natural tendency of a lot of people

  • is, well, I think there's a 20% chance to come back,

  • and I'm going to make $500 more.

  • Why do I want to lock in to stop myself out at 280?

  • So even though the expected value-- I think lot of people

  • said, you lose expected value, which is $300 in choice A,

  • but you would still not to choose choice B,

  • because you don't want to lock in the $280 loss.

  • Again, I'm not trying to inject the idea to you of which one

  • is right or wrong, but think about it.

  • So, that's really the common behavior, which mathematically

  • may not make sense, but lot of people still would like to do.

  • And also, really, when you think about it,

  • depends on your situation.

  • And let's say, you think the market--

  • I'm giving you the stock example again.

  • If you're not purely following the discipline of stop loss,

  • but you just think the fundamental picture

  • has changed.

  • You really don't think the stock should go up anymore.

  • Obviously, at whatever level you should get out, regardless

  • how much loss you lock in.

  • But if you think the fundamental story is still very sound,

  • you should think about as if you don't have a position, what

  • you want to do next.

  • But anyway, mathematically, I just

  • want to see-- I guess this is MIT,

  • so many people think mathematically

  • where you would actually choose choice B, because that's

  • low expectation, which makes sense.

  • But I think if you ask a larger audience,

  • I think a lot of people don't really want to choose choice B,

  • because they don't want to lock in the loss.

  • Now, let me change the question a little bit.

  • So, choice A becomes instead of the 80% chance to lose,

  • now you have 80% chance to win $500 and 20% chance

  • to lose $500.

  • Choice B, you have 100% chance to win $280.

  • Who would choose choice A?

  • Again, minority of this audience.

  • Let's say less than 10%.

  • Who would choose choice B?

  • The rest of you.

  • All right.

  • Can someone choose choice A give me an argument why would you?

  • AUDIENCE: [INAUDIBLE]

  • JAKE XIA: Yep.

  • Anyone want to give me a reason for choice B?

  • AUDIENCE: Higher Sharpe.

  • JAKE XIA: Higher Sharpe?

  • Mm-hm.

  • Yup.

  • Well, let me just leave it here.

  • Again, I think we can talk a bit more along in the class.

  • I mean, the last day of the class,

  • hopefully we'll have much deeper discussion on this.

  • It's not unique.

  • The answer, I think it can go you either way, as you said.

  • If your bank account balance is-- let's

  • say you are a freshman student.

  • Your bank account is $800.

  • Your choice will be very different from someone has

  • $100,000 in his bank account.

  • And also, your risk tolerance, how much you can tolerate.

  • I'm not going to give you say, this is right or wrong.

  • But with that, let me move on and give you some homework.

  • So, before I give you the homework,

  • I want to make a few more comments.

  • Do people always learn from their experiences?

  • In science, we collect evidence, we build models.

  • We first understand the physics.

  • We build mathematical models, then we verify in physics,

  • doing experiments.

  • But is that the same investigation process

  • in finance?

  • Market cycles are typically very long,

  • but people tend to have short memories.

  • So, how do people really learn from their experiences?

  • A very interesting question.

  • And very natural tendency is to extrapolate

  • historical experience.

  • What happened in 2008?

  • People still remember.

  • What happened in 1970s?

  • Maybe some people still remember.

  • What happened 100 years ago?

  • So, people tend to extrapolate, drawing conclusions

  • from very recent experience.

  • And deterministic relationship versus statistical relationship

  • is very interesting, as well.

  • When you try to trade on those, how do you really build models?

  • Is the market really efficient?

  • What part is efficient?

  • How do you really apply those theories

  • in your day-to-day risk management or trading

  • activities?

  • And sometimes, people tend to oversimplify.

  • Just say, oh, I can model this.

  • This is one important parameter.

  • I just take that.

  • So I just give you all the warnings

  • that the-- again, very young, new field

  • and largely, often, this is art, than science.

  • So keep that in mind, even though we're talking

  • about mathematics in finance.

  • Math is very powerful and useful in finance.

  • So learn the math, learn the finance first,

  • but keep those questions along the way

  • when you are learning during this class.

  • So suggested homework, optional.

  • I mentioned a lot of terminologies today.

  • Go to the course website, read what we have put up

  • for the financial glossary.

  • So if you still have things you don't understand,

  • compile your own list of financial concepts, which

  • you can search on the web or even ask us.

  • But I encourage you to do that.

  • It will prepare you well.

  • So, that's really-- and read other materials

  • on the course work.

  • So we got maybe-- how about this?

  • We still got about 15 minutes or 12 minutes left,

  • so I'll pass it to Vasily, then maybe we

  • can leave five minutes for some questions.

  • VASILY STRELA: Yeah.

  • JAKE XIA: Yeah, OK.

  • VASILY STRELA: [INAUDIBLE] mentioned

  • that, Apple trades, that now it's $494.4 Yeah, just a couple

  • of [INAUDIBLE].

  • Well, first of all, no offense to people who were [INAUDIBLE],

  • but I just wanted to give an example of [INAUDIBLE].

  • AUDIENCE: [INAUDIBLE].

  • VASILY STRELA: --because he was working in our group,

  • and it just will give you a little bit of an idea what

  • we will be talking about and what actually we

  • do in the daily life, or what an intern or somebody who

  • comes to work in this industry could do.

  • And one project is [INAUDIBLE] worked

  • was on estimating the noisy derivative.

  • Derivative is called delta.

  • Delta is usually the first derivative to a function.

  • And as we will see in the class, quite often, to obtain a price,

  • you do it through Monte Carlo, meaning running a lot of paths

  • and then averaging along them.

  • So, it's a statistical method.

  • So obviously, there is a noise to your answer every time.

  • So, if you want to differentiate this functions

  • and get a derivative, then this derivative will be quite noisy.

  • And so, instead of getting the true derivative,

  • you might obtain something quite different from true derivative

  • just because there is a confidence

  • interval around any point.

  • And obviously, there is a trade off here, as well,

  • because you can run more paths, throw more computational power,

  • which will reduce your confidence interval.

  • You will know better where you are, more precise.

  • Or the other solution could be, if you

  • know that your function is not too concave and reasonably

  • flat, you might do the numerical differentiation

  • on wider interval.

  • Basically, reducing the significance of the error,

  • and you will hope to arrive to a better approximation.

  • So obviously, there is somewhere balance, and the question was,

  • is there an optimal shift size to get the derivative?

  • And that's what-- uh oh, the slide got corrupted.

  • So, there was quite a bit of mathematics

  • involved and minimization and optimization.

  • There was an answer.

  • And that's actually what we finally arrived at.

  • And that's some toy example, but still, it

  • shows you that if you use constant size

  • and not optimal size, that would be your numerical derivative

  • of this blue function.

  • While if you use an optimal shift

  • size, which [INAUDIBLE] computed,

  • it would be much smoother and much better.

  • So, that's one of example, and that's what he did.

  • And we actually are implementing it in our systems and plan

  • to use it in practice.

  • Another project was actually quite different.

  • And it was about electronic trading

  • and basically how to better predict prices of currencies

  • and exchange rate.

  • And funny enough, it was on ruble/US dollar,

  • because it was actually aimed for our Moscow office.

  • And basically, what we had, we had the noisy observation

  • of broker data and it was coming out

  • at different non-uniform times.

  • Basically, at random times.

  • So, we decided to use Kalman filter

  • and to study how it can predict.

  • And that's one of the nice graphs [INAUDIBLE]

  • produced, which again, we will use this strategy

  • and the Kalman filters which he constructed

  • in our e-trading platform in Moscow.

  • So, that's just a couple of examples,

  • which I wanted to give you as a preview of what we

  • will be talking in the class.

  • Just to remind, the website is fully functional.

  • We put syllabus there, a short list of literature.

  • We will be posting a lot of materials there.

  • Probably most lectures will be published there.

  • Jake's slides are there already.

  • So, any questions?

  • JAKE XIA: Please hand back the sign up sheets.

  • We like to get your emails so we can put you

  • on the website for further announcements,

  • but you can also add yourselves. [INAUDIBLE].

  • But it's probably easier if you put your email

  • on the sign up sheet, so we can [INAUDIBLE].

  • VASILY STRELA: Yeah, but please visit

  • and sign up here, because there will

  • be announcements to the class.

  • Thank you very much.

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