Exam 1

Question 1

Equation for daily return

Answer 1

daily return = (today’s price / yesterday’s price) - 1

Question 2

Equation for cumulative return

Answer 2

cumulative return = (today’s price / first day’s price) - 1

Question 3

Kurtosis

Answer 3

Describes tails of a distribution.

Fat tails = positive kurtosis
Skinny tails = negative kurtosis

Question 4

Equation for present value

Answer 4

PV = FV/(1 + IR)^i
and
PV = FV/DR where FV can be dividend, DR is discount rate

Question 5

Sharpe Ratio equation

Answer 5

SR = mean(daily returns - daily risk factor) / std(daily returns)

Question 6

Book value equation

Answer 6

Book value = total assets (ignoring intangibles) minus liabilities.

Intangibles include brand power, patents
Liabilities include loans

Question 7

Symbols for different types of funds

Answer 7

ETF = 3 or 4 letters
Mutual funds = 5 letters
Hedge fund = long name like Berkshire Hathaway

Question 8

Market cap

Answer 8

share price * number of shares outstanding

Question 9

Intrinsic value

Answer 9

PV = FV/DR

Example: if dividend is 1 dollar, and there are 1 million shares outstanding, and the DR is 5%, it’s 1 million / .05. Intrinsic value would be 20,000,000.

Question 10

CAPM

Answer 10

Equation:
r-sub-i(t) = B-sub-i * r-sub-m(t) + alpha-sub-i(t)

• Tells us that a significant portion of a stock’s return is due to the market
• Alpha is random and expected value is 0
• Beta is relationship of stock to market. Make money by picking stocks with high B in up markets and low B in down markets.
• CAPM and EMH combined say you can’t beat market
• APT = multiple B for different sectors

Question 11

RMSE equation

Answer 11

square_root_of((sigma(yTest - yPredict)^2) / N)

Question 12

Roughly how big are training and testing chunks in general

Answer 12

60% training, 40% testing

Question 13

Lin Reg vs KNN vs DT (using correlation), what is fastest to slowest training time?

Answer 13

KNN, LR, DT

Question 14

Lin Reg vs KNN vs DT (using correlation), what is fastest to slowest query time?

Answer 14

LR, DT, KNN

Question 15

Lin Reg vs KNN vs DT (using correlation), what is best for space needed to save model?

Answer 15

LR

Question 16

Lin Reg vs KNN vs DT (using correlation), what is fastest for adding new data?

Answer 16

KNN

Question 17

What are the benefits of ensemble learners?

Answer 17

lower error

less overfitting

Question 18

Boosting and bagging

Answer 18

Same learner is used, just with different parts of its data

Question 19

What is more likely to overfit as m increases? Boosting (ADA) or bagging?

Answer 19

ADA Boost

Question 20

Technically, boosting and bagging are _____

Answer 20

wrappers for existing methods

Question 21

Fundamental analysis

Answer 21

estimate a company’s value. buy stock when below company’s value. looks at earnings, dividends, cash flow, book value, etc.

Question 22

Technical analysis

Answer 22

Don’t care about value of company. Look only for patterns or trends in a stock’s data. Looks only at price and volume.

Question 23

Momentum equation

Answer 23

momentum[t] = (price[t] / price[t-n]) - 1

Where:
n = number of days, like 5 or 10
result is usually between -.5 and .5 (50% loss, 50% gain)

Question 24

Simple moving average

Answer 24

SMA[t] = (price[t] / price[t -n : t].mean()) -1

Question 25

Bollinger Bands

Answer 25

BB[t] = (price[t] - sma[t]) / (2 * std[t])

This is to see where price is relative to Bands

Question 26

Normalization

Answer 26

Puts different metrics on same scale from -1 to 1, with mean of 0.

To norm something:
normed = (values - mean) / values.std()

Question 27

what does bid mean?

Answer 27

buy

Question 28

what does ask mean?

Answer 28

sell

Question 29

market order book must always give stock to seller/buyer that is _______

Answer 29

the best price for them (highest if seller, lowest if buyer)

Question 30

what does it mean if there are more asks than bids in the order book?

Answer 30

more selling pressure = stock price likely to go down