@tranvan19996: #xuhuong

Trần Vân Boutiquee

Open In TikTok:

Region: VN

Thursday 08 October 2026 12:47:30 GMT

44470

595

123

42

Music

Download

No Watermark .mp4 (4.41MB) No Watermark(HD) .mp4 (3.06MB) Watermark .mp4 (4.78MB) Music .mp3

Comments

A SÓC🐯 :

Cả sét Bn v shop

2026-10-12 14:22:19

1

Dung tẹt 😍 :

cả sét bao nhiêu

2026-10-16 06:37:24

0

RCOM H Niên :

xin giá quần

2026-10-11 07:05:02

3

huong_lop6789 :

Giá sét a

2026-10-10 15:45:22

2

Conmeoluời’s2 :

E xin giá áo bò ah

2026-10-08 23:26:21

0

Mítttttttt :

Mình xin giá cả set

2026-10-09 10:47:47

0

🏳️‍🌈레 티 하오🥰 🖤(katy)🌈 :

áo mua sao ạ

2026-10-10 18:56:42

0

Phương Thảo :

giá quần

2026-10-10 04:48:55

0

Trang sức nhẫn nam :

E xin giá cả sét nhe

2026-10-16 15:38:24

0

M.M :

Set nay có màu đen ko shop

2026-10-18 08:39:15

0

Mai Mèo :

Xin giá ao

2026-10-09 08:01:39

0

Cảnh shuffle dance :

Xin giá sét

2026-10-10 09:12:30

0

Nguyễn Hà :

xin giá cả set ạ

2026-10-10 00:22:56

0

Đỗ Thương 1986 :

Mua kiểu j shop

2026-10-09 12:25:50

0

rung@23195 :

Xin cả bộ

2026-10-09 22:13:53

0

🇻🇳Kim Oanh🇹🇼 :

minh xin gia voi ạ

2026-10-10 02:25:31

0

Lưu Ly :

xin giá cả bộ

2026-10-10 01:05:02

0

Mèo con :

giá đi

2026-10-08 14:56:39

0

Thu Hương :

Set này bn ạ

2026-10-09 01:32:45

0

Nguyệt🦉 :

Set bò bn a

2026-10-09 01:47:16

0

Phương chinh :

xin gia aos khiacs

2026-10-09 08:32:01

0

Minh Thu :

quần bnh ạ

2026-10-09 17:05:36

0

Anh pham :

Cả sét hay lẻ ạ

2026-10-10 06:35:51

0

Trang Lê 0208 :

Xin giá nguyên sét

2026-10-10 06:33:52

0

🇻🇳Tuệ Trí 1988 🇻🇳 :

sét bn ạ

2026-10-08 21:47:51

0

To see more videos from user @tranvan19996, please go to the Tikwm homepage.

Other Videos

Condeçalina capítulo 1 #trending #novelas #frutas #fyp #viraltiktok

Condeçalina capítulo 1 #trending #novelas #frutas #fyp #viraltiktok

#sealteam #linkinpark #whatlvedone #military #seriesnetflix #belica #Amigos #parati #viralvideo #fuerzasespeciales #marinademexico #marinadeeeuu #usa #honor #fyp #parati #soldier #guerra #dañopsicologico #estres #conmoción #conmocioncerebral #dañosdelaguerra #reflexion #estadosunidos #estadodeanimo #soledad #luchando #videomotivacional #soldadoanonimo #peliculas #películas #linkinpark #tessastevejablonsky #stevejablonsky #soundtracks #viral #estadosparawhatsapp

#sealteam #linkinpark #whatlvedone #military #seriesnetflix #belica #Amigos #parati #viralvideo #fuerzasespeciales #marinademexico #marinadeeeuu #usa #honor #fyp #parati #soldier #guerra #dañopsicologico #estres #conmoción #conmocioncerebral #dañosdelaguerra #reflexion #estadosunidos #estadodeanimo #soledad #luchando #videomotivacional #soldadoanonimo #peliculas #películas #linkinpark #tessastevejablonsky #stevejablonsky #soundtracks #viral #estadosparawhatsapp

➡️ Part 28 of Quantitative Finance From Scratch: the low volatility anomaly. Here, risk means volatility, how much a stock's price swings. The CAPM (part 15) says expected return rises with beta, and wild stocks tend to have higher betas. Malcolm Baker, Brendan Bradley and Jeffrey Wurgler (Financial Analysts Journal, 2011) sorted all listed US stocks every month into five groups by the volatility of their monthly returns over the previous two to five years. From January 1968 to December 2008, $1 in the calmest fifth grew to $59.55, while $1 in the wildest fifth shrank to $0.58 (nominal, weighted by company size, rebalanced monthly, before trading costs; the authors note that trading costs were even higher for the wild stocks). The wildest fifth had the highest beta in the study, 1.71. Reason one is volatility drag (part 9): growth is roughly the average return minus half the variance. In our test run (assumed numbers: five groups with the same average return of 11 % a year and the volatilities of the study's groups, 13.1 % to 32 %, 41 years of monthly returns, 10,000 simulated histories each), a typical dollar ends at $63.29 in the calmest group and at $11.46 in the wildest. Luck still matters in a single history: with equal averages, the wildest group finished ahead of the calmest in about 22 % of the histories. In the study, the first four groups had almost the same average return above cash (about 4 to 5 % a year), so the drag explains most of their gap. The wildest group averaged 1.7 % a year less than cash, which the drag cannot explain. Reason two, as researchers suggest: many investors buy wild stocks like lottery tickets, hoping for a big win, so they pay too much and later earn less. The authors also point to fund managers who are judged against an index and rarely borrow. Low volatility funds use this idea, but the study covers one period of history, wild stocks soared in the technology bubble around 2000, and nobody knows whether the effect will hold in the future. Education only, not financial advice. #quantfinance #lowvolatility #factorinvesting #python #finance

➡️ Part 28 of Quantitative Finance From Scratch: the low volatility anomaly. Here, risk means volatility, how much a stock's price swings. The CAPM (part 15) says expected return rises with beta, and wild stocks tend to have higher betas. Malcolm Baker, Brendan Bradley and Jeffrey Wurgler (Financial Analysts Journal, 2011) sorted all listed US stocks every month into five groups by the volatility of their monthly returns over the previous two to five years. From January 1968 to December 2008, $1 in the calmest fifth grew to $59.55, while $1 in the wildest fifth shrank to $0.58 (nominal, weighted by company size, rebalanced monthly, before trading costs; the authors note that trading costs were even higher for the wild stocks). The wildest fifth had the highest beta in the study, 1.71. Reason one is volatility drag (part 9): growth is roughly the average return minus half the variance. In our test run (assumed numbers: five groups with the same average return of 11 % a year and the volatilities of the study's groups, 13.1 % to 32 %, 41 years of monthly returns, 10,000 simulated histories each), a typical dollar ends at $63.29 in the calmest group and at $11.46 in the wildest. Luck still matters in a single history: with equal averages, the wildest group finished ahead of the calmest in about 22 % of the histories. In the study, the first four groups had almost the same average return above cash (about 4 to 5 % a year), so the drag explains most of their gap. The wildest group averaged 1.7 % a year less than cash, which the drag cannot explain. Reason two, as researchers suggest: many investors buy wild stocks like lottery tickets, hoping for a big win, so they pay too much and later earn less. The authors also point to fund managers who are judged against an index and rarely borrow. Low volatility funds use this idea, but the study covers one period of history, wild stocks soared in the technology bubble around 2000, and nobody knows whether the effect will hold in the future. Education only, not financial advice. #quantfinance #lowvolatility #factorinvesting #python #finance

Respondendo a @🐼Miih🐼🦋 #tiktoklive #vitelo #roblox

Respondendo a @🐼Miih🐼🦋 #tiktoklive #vitelo #roblox

About

Robot
API

Legal

Privacy Policy