The GBM doesn't model big shocks to stock prices, due to quarterly announcements, news, public policy change etc.
All numbers (including target) will be positive integers. Elements in a combination (a1, a2, … , ak) must be in
non-descending order. (ie, a1 ≤ a2 ≤ … ≤ ak). The solution set must not contain duplicate combinations.
For example, given candidate set 2,3,6,7 and target 7, A solution set is:
[2, 2, 3]
Facts to internalize, so when you see them in the middle of a puzzle, you could recognize them.
1/5 + 1/52 + 1/53 … = 1/4
Fact C: (I call this a geometric-arithmetic series)
1/5 + 2/52 + 3/53 … = 5/ 4*4
Fact C2: (similar)
1/5 + 4/52 + 9/53 + 16/54… is also tractable
Book value of EQ is still 600-480 = $120k, but current EQ would be 1.2m – 480k = 720k.
(Book value of) Leverage in “literature” is defined as
(book value of) ASset / EQuity (book value)
The denominator is much lower as book value than the current value. For a listed company, Current value of total equity is total market cap == current share price * total shares issued so far. In contrast, Book value is the initial capital of the founder + actual dollars raised through the IPO, ignoring the increase in value of each share. Why is this book value less useful? We need to understand the term “shareholder equity”. This term logically means the “value” of the shares held by the shareholders (say a private club of …. 500 teachers). Like the value of your house, this “value” increases over time.
Given some observed data Y, you first pick some explanatory variables X_1, X_2 etc
If you pick a linear model to explain the observed Y, then OLS is the best, linear, unbiased and efficient (BLUE) solution using a computer. It will give you all the parameters of your linear model – the b_0, b_1, b_2 etc.
If you feel the relationship isn’t linear, you still can use OLS. As an alternative to a linear model, you could use AR(1) models to explain Y using the X1 X2 etc. You use AR models when you believe there’s strong serial correlation or autocorrelation.
I believe AR models use additional parameters beside the b1, b2 etc. The computation is more efficient than OLS.
When a bank issues a financial statement, the meaning of AS (asset) and LI (liability) tend to confuse me.
Suppose JPMC bank has client IBM…
Liability – Deposits (incl. CD) at the bank
Liability – overnight borrowing. This interest rate could surge like in 2008.
Liability – Commercial papers issued by the bank
Liability – Bonds issued by the bank
Asset – securities owned by the bank (treasury department?), including stocks, govt bonds and corp bonds etc. Securities could devalue like bad loan!
Asset – Loans to corporations like IBM — on the balance sheet treated like a govt bond!
Asset – Loans/mtg to retail — on the balance sheet treated like a govt bond!
Asset – spare cash
AS = LI + share holders’ equity
If the bank issues 600M shares in an IPO, the $600mil collected is considered share holders’ equity capital or simply “capital” or simply “equity”.
Chronologically, the balance sheet starts with the initial share holders’ equity. Then Deposits come in and sitting there as spare cash. Similarly the bank can issue bonds.
Then the bank could use the spare cash to buy securities — without change on the LI side.
The bank can also use the spare cash to give loans — without change on the LI side.
Each type of transaction above affects the balance sheet only in a “realized” sense i.e. book values —
Big warning – all the AS numbers and LI numbers and equity values are book values.
* Latest share price doesn’t enter the equation. Those 600M shares will always be recorded as worth $600M on the balance sheet.
* market value (m2m) of the loans lent out doesn’t matter
* market value (m2m) of the securities owned by the bank doesn’t matter.
Fair Value accounting tries to change that. Mark-to-market is a big effort in GS and many investment banks.
Professionals “always” trade pairs like
– Relative value pairs
– Option “strategies”
My problem with pair trading is the commission or bid/ask spread.
__Tan Bin (+65)6530 1386 OC Centre #17__
* A lot of Jargon (need a bit of intuition)
** how each instrument is priced
** using what mkt data
* the math theory below the surface
* probability puzzles, math algorithms.
* c++, matlab
I'm trying to understand the relation between the finance perspective and the accounting perspective.
Financial data often fall into 2 categories – accounting data and financial data. For example, I believe live market data (“financial data”) is largely irrelevant to accountants when updating the accounting records.
Accounting calc is strictly by the rule, a bit mechanical; finance calc is flexible and subject to interpretation.
When we hear of accounting, we think of the external (and internal) accounting firms, and financial reporting (yes accountant's job). I feel financial reporting has legal implications. Investors and regulators demand accurate calc. Therefore accounting rules are like laws. Breaking these rules is like breaking the law, falsifying and cheating tax authority and regulators, retail investors, institutional investors.
In my mind, “Finance” as a profession and discipline is about … valuation of corporate and other securities ultimately for transactions. For eg, an investment is a transaction — buying a security.
Finance is at a somewhat higher level than accounting?
See Lesson 55 about details on deltaW and dW
See Lesson 19 about N@T
See Lesson 33 for a backgrounder on the canonical Wiener variable W
The Hull definition of the canonical Wiener process (Lesson 33) —
deltaW = epsilon * sqrt(deltaT) // in discrete time
dW = epsilon * sqrt(dT) // in continuous time
The “=” has a different meaning than in algebra.
Discrete time is simpler to understand. Recall deltaW is a stepsize of a random variable. The “=” doesn’t mean a step size value of 0.012 is equal to the product of an epsilon value and sqrt(deltaT).
The “=” means equivalent-to.
Here epsilon represents … (hold your breath)… a noisegen, in fact the canonical Gaussian noisegen.
I’d say both deltaW and epsilon are N@T. These are not regular variables.