principal component and constantly re-balanced portfoliooti/portfolio/lectures/bcrp.pdf0 hb, x 1i...

Principal component and constantly re-balancedportfolio

Gyorgy Ottucsak1

Laszlo Gyorfi

1Department of Computer Science and Information TheoryBudapest University of Technology and Economics

Budapest, Hungary

September 20, 2007

e-mail: oti@szit.bme.huwww.szit.bme.hu/ otiwww.szit.bme.hu/ oti/portfolio

Ottucsak, Gyorfi Principal component and constantly re-balanced portfolio

Investment in the stock market: Growth rate

The model:

d assets

S(j)n price of asset j at the end of trading period (day) n initial

price S(j)0 = 1,

S(j)n = enW

(j)n ≈ enW (j)

j = 1, . . . , d

average growth rate

W(j)n =

asymptotic average growth rate

W (j) = limn→∞

The model:

d assets

price S(j)0 = 1,

S(j)n = enW

(j)n ≈ enW (j)

j = 1, . . . , d

average growth rate

W(j)n =

W (j) = limn→∞

The model:

d assets

price S(j)0 = 1,

S(j)n = enW

≈ enW (j)

j = 1, . . . , d

average growth rate

W(j)n =

W (j) = limn→∞

The model:

d assets

price S(j)0 = 1,

S(j)n = enW

(j)n ≈ enW (j)

j = 1, . . . , d

average growth rate

W(j)n =

W (j) = limn→∞

Static portfolio selection: single period investment

The aim is to achieve maxj W (j).

Static portfolio selection:

Fix a portfolio vector b = (b(1), . . . b(d)).

S0b(j) denotes the proportion of the investor’s capital invested

in asset j . Assumptions:

no short-sales b(j) ≥ 0

self-financing∑

j b(j) = 1

After n day

Sn = S0

b(j)S(j)n

Use the following simple bound

S0 maxj

b(j)S(j)n ≤ Sn ≤ dS0 max

jb(j)S

The aim is to achieve maxj W (j).Static portfolio selection:

self-financing∑

j b(j) = 1

After n day

Sn = S0

b(j)S(j)n

S0 maxj

jb(j)S

self-financing∑

j b(j) = 1

After n day

Sn = S0

b(j)S(j)n

S0 maxj

jb(j)S

self-financing∑

j b(j) = 1

After n day

Sn = S0

b(j)S(j)n

S0 maxj

jb(j)S

assume that b(j) > 0

nlnmax

(j)S(j)n

)≤ 1

nlnSn ≤

(dS0 max

jb(j)S

nlnS0b

(j) +1

)≤ 1

≤ maxj

nln(dS0b

(j)) +1

limn→∞

nlnSn = lim

n→∞max

(j)n = max

jW (j)

Conclusion: any static portfolio achieves the maximal growth ratemaxj W (j). We can do much better!

nlnmax

(j)S(j)n

)≤ 1

nlnSn ≤

(dS0 max

jb(j)S

nlnS0b

(j) +1

)≤ 1

≤ maxj

nln(dS0b

(j)) +1

limn→∞

nlnSn = lim

n→∞max

(j)n = max

jW (j)

nlnmax

(j)S(j)n

)≤ 1

nlnSn ≤

(dS0 max

jb(j)S

nlnS0b

(j) +1

)≤ 1

≤ maxj

nln(dS0b

(j)) +1

limn→∞

nlnSn = lim

n→∞max

(j)n = max

jW (j)

Conclusion: any static portfolio achieves the maximal growth ratemaxj W (j).

We can do much better!

nlnmax

(j)S(j)n

)≤ 1

nlnSn ≤

(dS0 max

jb(j)S

nlnS0b

(j) +1

)≤ 1

≤ maxj

nln(dS0b

(j)) +1

limn→∞

nlnSn = lim

n→∞max

(j)n = max

jW (j)

Dynamic portfolio selection: multi-period investment

The model:

Let xi = (x(1)i , . . . x

(d)i ) the return vector on trading period i ,

x(j)i =

S(j)i−1

is the price relatives of two consecutive days.

x(j)i is the factor by which capital invested in stock j grows during

the market period iOne of the simplest dynamic portfolio strategy is theConstantly Re-balanced Portfolio (CRP):

Fix a portfolio vector b = (b(1), . . . b(d)), where b(j) gives theproportion of the investor’s capital invested in stock j .This b is the portfolio vector for each trading day.

The model:

Let xi = (x(1)i , . . . x

x(j)i =

S(j)i−1

the market period i

One of the simplest dynamic portfolio strategy is theConstantly Re-balanced Portfolio (CRP):

The model:

Let xi = (x(1)i , . . . x

x(j)i =

S(j)i−1

Fix a portfolio vector b = (b(1), . . . b(d)), where b(j) gives theproportion of the investor’s capital invested in stock j .

This b is the portfolio vector for each trading day.

The model:

Let xi = (x(1)i , . . . x

x(j)i =

S(j)i−1

Dynamic portfolio selection: multi-period investment:2

Repeatedly investment:

for the first trading period S0 denotes the initial capital

S1 = S0

d∑j=1

b(j)x(j)1 = S0 〈b , x1〉

for the second trading period, S1 new initial capital

S2 = S1 · 〈b , x2〉 = S0 · 〈b , x1〉 · 〈b , x2〉 .

for the nth trading period:

Sn = Sn−1 〈b , xn〉 = S0

n∏i=1

〈b , xi 〉

= S0enWn(b)

with the average growth rate

Wn(b) =1

n∑i=1

ln 〈b , xi 〉 .

S1 = S0

d∑j=1

b(j)x(j)1 = S0 〈b , x1〉

S2 = S1 · 〈b , x2〉 = S0 · 〈b , x1〉 · 〈b , x2〉 .

Sn = Sn−1 〈b , xn〉 = S0

n∏i=1

〈b , xi 〉

= S0enWn(b)

Wn(b) =1

n∑i=1

ln 〈b , xi 〉 .

S1 = S0

d∑j=1

b(j)x(j)1 = S0 〈b , x1〉

S2 = S1 · 〈b , x2〉 = S0 · 〈b , x1〉 · 〈b , x2〉 .

Sn = Sn−1 〈b , xn〉 = S0

n∏i=1

〈b , xi 〉

= S0enWn(b)

Wn(b) =1

n∑i=1

ln 〈b , xi 〉 .

S1 = S0

d∑j=1

b(j)x(j)1 = S0 〈b , x1〉

S2 = S1 · 〈b , x2〉 = S0 · 〈b , x1〉 · 〈b , x2〉 .

Sn = Sn−1 〈b , xn〉 = S0

n∏i=1

〈b , xi 〉 = S0enWn(b)

Wn(b) =1

n∑i=1

ln 〈b , xi 〉 .

log-optimum portfolio

The CRP is the optimal portfolio for special market process, whereX1,X2, . . . is independent and identically distributed (i.i.d.)

Log-optimum portfolio b∗

E{ln 〈b∗ , X1〉} = maxb

E{ln 〈b , X1〉}

Best Constantly Re-balanced Portfolio (BCRP)Properties:

needed full-knowledge on the distribution

in experiments: not a causal strategy. We can calculate itonly in hindsight.

E{ln 〈b , X1〉}

Best Constantly Re-balanced Portfolio (BCRP)

Properties:

E{ln 〈b , X1〉}

Optimality

If S∗n = Sn(b∗) denotes the capital after trading period n achievedby a log-optimum portfolio strategy b∗, then for any portfoliostrategy b with capital Sn = Sn(b) and for any i.i.d. process{Xn}∞−∞,

limn→∞

nlnSn ≤ lim

n→∞

nlnS∗n almost surely

limn→∞

nlnS∗n = W ∗ almost surely,

whereW ∗ = E{ln 〈b∗ , X1〉}

is the maximal growth rate of any portfolio.

Optimality

If S∗n = Sn(b∗) denotes the capital after trading period n achievedby a log-optimum portfolio strategy b∗, then for any portfoliostrategy b with capital Sn = Sn(b) and for any i.i.d. process{Xn}∞−∞,

limn→∞

nlnSn ≤ lim

n→∞

nlnS∗n almost surely

limn→∞

nlnS∗n = W ∗ almost surely,

whereW ∗ = E{ln 〈b∗ , X1〉}

is the maximal growth rate of any portfolio.

Semi-log-optimal portfolio

log-optimal:arg max

bE{ln 〈b , X1〉}

It is a non-linear (convex) optimization problem with linearconstraints. Calculation: not cheap.

Idea: use the Taylor expansion:

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

Only the two biggest principal components, others are drop.semi-log-optimal:

arg maxb

E{h(〈b , X1〉)} = arg maxb

{〈b , m〉 − 〈b , Cb〉}

Cheaper: Quadratic Programming (QP)Connection to the Markowitz theory.Gy. Ottucsak and I. Vajda, ”An Asymptotic Analysis of the

Mean-Variance portfolio selection”, Statistics&Decisions, 25, pp. 63-88,

2007. http://www.szit.bme.hu/ oti/portfolio/articles/marko.pdf

log-optimal:arg max

bE{ln 〈b , X1〉}

It is a non-linear (convex) optimization problem with linearconstraints. Calculation: not cheap.Idea: use the Taylor expansion:

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

Only the two biggest principal components, others are drop.

semi-log-optimal:

arg maxb

{〈b , m〉 − 〈b , Cb〉}

log-optimal:arg max

bE{ln 〈b , X1〉}

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

arg maxb

E{h(〈b , X1〉)}

= arg maxb

{〈b , m〉 − 〈b , Cb〉}

log-optimal:arg max

bE{ln 〈b , X1〉}

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

arg maxb

{〈b , m〉 − 〈b , Cb〉}

log-optimal:arg max

bE{ln 〈b , X1〉}

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

arg maxb

{〈b , m〉 − 〈b , Cb〉}

Cheaper: Quadratic Programming (QP)

Connection to the Markowitz theory.Gy. Ottucsak and I. Vajda, ”An Asymptotic Analysis of the

log-optimal:arg max

bE{ln 〈b , X1〉}

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

arg maxb

{〈b , m〉 − 〈b , Cb〉}

Cheaper: Quadratic Programming (QP)Connection to the Markowitz theory.

Gy. Ottucsak and I. Vajda, ”An Asymptotic Analysis of the

log-optimal:arg max

bE{ln 〈b , X1〉}

ln z ≈ h(z) = z − 1 − 1

2(z − 1)2

arg maxb

{〈b , m〉 − 〈b , Cb〉}

2007. http://www.szit.bme.hu/ oti/portfolio/articles/marko.pdfOttucsak, Gyorfi Principal component and constantly re-balanced portfolio

Principal component

We may write

E{〈b , X1〉 − 1} − 1

2E{(〈b , X1〉 − 1)2}

= 2E{〈b , X1〉} −1

2E{〈b , X1〉2} −

= −1

2E{(〈b , X1〉 − 2)2} +

arg maxb

2E{(〈b , X1〉 − 2)2} +

arg minb

E{(〈b , X1〉 − 2)2},

that is, we are looking for the portfolio which minimize theexpected squared error.

Principal component

We may write

E{〈b , X1〉 − 1} − 1

2E{(〈b , X1〉 − 1)2}

= 2E{〈b , X1〉} −1

2E{〈b , X1〉2} −

= −1

2E{(〈b , X1〉 − 2)2} +

arg maxb

2E{(〈b , X1〉 − 2)2} +

arg minb

E{(〈b , X1〉 − 2)2},

Principal component

We may write

E{〈b , X1〉 − 1} − 1

2E{(〈b , X1〉 − 1)2}

= 2E{〈b , X1〉} −1

2E{〈b , X1〉2} −

= −1

2E{(〈b , X1〉 − 2)2} +

arg maxb

2E{(〈b , X1〉 − 2)2} +

arg minb

E{(〈b , X1〉 − 2)2},

Principal component

We may write

E{〈b , X1〉 − 1} − 1

2E{(〈b , X1〉 − 1)2}

= 2E{〈b , X1〉} −1

2E{〈b , X1〉2} −

= −1

2E{(〈b , X1〉 − 2)2} +

arg maxb

2E{(〈b , X1〉 − 2)2} +

arg minb

E{(〈b , X1〉 − 2)2},

Conditions of the model:

Assume that

the assets are arbitrarily divisible,

the assets are available in unbounded quantities at the currentprice at any given trading period,

there are no transaction costs,(go to Session 1 today at 17.30)

the behavior of the market is not affected by the actions ofthe investor using the strategy under investigation.

Assume that

NYSE data sets

At www.szit.bme.hu/~oti/portfolio there are two benchmarkdata sets from NYSE:

The first data set consists of daily data of 36 stocks withlength 22 years.

The second data set contains 23 stocks and has length 44years.

Both sets are corrected with the dividends.

Our experiment is on the second data set.

NYSE data sets

At www.szit.bme.hu/~oti/portfolio there are two benchmarkdata sets from NYSE:

The first data set consists of daily data of 36 stocks withlength 22 years.

The second data set contains 23 stocks and has length 44years.

Both sets are corrected with the dividends.Our experiment is on the second data set.

Experimental results on CRP

Stock’s name AAY BCRPlog-NLP weights semi-log-QP weights

COMME 18% 0.3028 0.2962HP 15% 0.0100 0.0317

KINAR 4% 0.2175 0.2130MORRIS 20% 0.4696 0.4590

AAY 24% 24%running time (sec) 9002 3

Table: Comparison of the two algorithms for CRPs.

The other 19 assets have 0 weight

KINAR had the smallest AAY

COMME 18% 0.3028 0.2962HP 15% 0.0100 0.0317

KINAR 4% 0.2175 0.2130MORRIS 20% 0.4696 0.4590

COMME 18% 0.3028 0.2962HP 15% 0.0100 0.0317

KINAR 4% 0.2175 0.2130MORRIS 20% 0.4696 0.4590

Dynamic portfolio selection: Causal-CRP

BCRP is not a causal strategy. A simple causal version could be,that we use the CRP that was optimal up to n − 1 for the next(nth) day.

Stock’s name AAY BCRP CCRP Staticlog-NLP w. semi-log-QP w.

COMME 18% 0.3028 0.2962HP 15% 0.0100 0.0317

KINAR 4% 0.2175 0.2130MORRIS 20% 0.4696 0.4590

AAY 24% 24% 14% 16%running t. (sec) 9002 3 111 3

we can even do much better!!

Dynamic portfolio selection: Causal-CRP

BCRP is not a causal strategy. A simple causal version could be,that we use the CRP that was optimal up to n − 1 for the next(nth) day.

Stock’s name AAY BCRP CCRP Staticlog-NLP w. semi-log-QP w.

COMME 18% 0.3028 0.2962HP 15% 0.0100 0.0317

KINAR 4% 0.2175 0.2130MORRIS 20% 0.4696 0.4590

AAY 24% 24% 14% 16%running t. (sec) 9002 3 111 3

we can even do much better!!

Dynamic portfolio selection: general case

xi = (x(1)i , . . . x

(d)i ) the return vector on day i

b = b1 is the portfolio vector for the first dayinitial capital S0

S1 = S0 · 〈b1 , x1〉

for the second day, S1 new initial capital, the portfolio vectorb2 = b(x1)

S2 = S0 · 〈b1 , x1〉 · 〈b(x1) , x2〉 .

nth day a portfolio strategy bn = b(x1, . . . , xn−1) = b(xn−11 )

Sn = S0

n∏i=1

⟨b(xi−1

1 ) , xi

S0enWn(B)

Wn(B) =1

n∑i=1

ln⟨b(xi−1

1 ) , xi

xi = (x(1)i , . . . x

S1 = S0 · 〈b1 , x1〉for the second day, S1 new initial capital, the portfolio vectorb2 = b(x1)

S2 = S0 · 〈b1 , x1〉 · 〈b(x1) , x2〉 .

Sn = S0

n∏i=1

⟨b(xi−1

1 ) , xi

S0enWn(B)

Wn(B) =1

n∑i=1

ln⟨b(xi−1

1 ) , xi

xi = (x(1)i , . . . x

S2 = S0 · 〈b1 , x1〉 · 〈b(x1) , x2〉 .

Sn = S0

n∏i=1

⟨b(xi−1

1 ) , xi

S0enWn(B)

Wn(B) =1

n∑i=1

ln⟨b(xi−1

1 ) , xi

xi = (x(1)i , . . . x

S2 = S0 · 〈b1 , x1〉 · 〈b(x1) , x2〉 .

Sn = S0

n∏i=1

⟨b(xi−1

1 ) , xi

⟩= S0e

nWn(B)

Wn(B) =1

n∑i=1

ln⟨b(xi−1

1 ) , xi

X1,X2, . . . drawn from the vector valued stationary and ergodicprocesslog-optimum portfolio B∗ = {b∗(·)}

E{ln⟨b∗(Xn−1

1 ) , Xn

⟩| Xn−1

1 } = maxb(·)

E{ln⟨b(Xn−1

1 ) , Xn

⟩| Xn−1

Xn−11 = X1, . . . ,Xn−1

Optimality

Algoet and Cover (1988): If S∗n = Sn(B∗) denotes the capital afterday n achieved by a log-optimum portfolio strategy B∗, then forany portfolio strategy B with capital Sn = Sn(B) and for anyprocess {Xn}∞−∞,

lim supn→∞

nlnSn −

nlnS∗n

)≤ 0 almost surely

for stationary ergodic process {Xn}∞−∞.

Kernel-based portfolio selection

fix integers k, ` = 1, 2, . . .elementary portfolioschoose the radius rk,` > 0 such that for any fixed k,

lim`→∞

rk,` = 0.

for n > k + 1, define the expert b(k,`) by

b(k,`)(xn−11 ) = arg max

∑{k<i<n:‖xi−1

i−k−xn−1n−k‖≤rk,`}

ln 〈b , xi 〉 ,

if the sum is non-void, and b0 = (1/d , . . . , 1/d) otherwise.

Kernel-based portfolio selection

fix integers k, ` = 1, 2, . . .elementary portfolioschoose the radius rk,` > 0 such that for any fixed k,

lim`→∞

rk,` = 0.

for n > k + 1, define the expert b(k,`) by

b(k,`)(xn−11 ) = arg max

∑{k<i<n:‖xi−1

i−k−xn−1n−k‖≤rk,`}

ln 〈b , xi 〉 ,

if the sum is non-void, and b0 = (1/d , . . . , 1/d) otherwise.

Combining elementary portfolios

let {qk,`} be a probability distribution on the set of all pairs (k, `)such that for all k, `, qk,` > 0.

The strategy B is the combination of the elementary portfolio

strategies B(k,`) = {b(k,`)n } such that the investor’s capital becomes

Sn(B) =∑k,`

qk,`Sn(B(k,`)).

Combining elementary portfolios

let {qk,`} be a probability distribution on the set of all pairs (k, `)such that for all k, `, qk,` > 0.The strategy B is the combination of the elementary portfolio

strategies B(k,`) = {b(k,`)n } such that the investor’s capital becomes

Sn(B) =∑k,`

qk,`Sn(B(k,`)).

Experiments on average annual yields (AAY)

Kernel based log-optimal portfolio selection withk = 1, . . . , 5 and ` = 1, . . . , 10

r2k,` = 0.0001 · d · k · `,

AAY of kernel based semi-log-optimal portfolio is 128%double the capitalMORRIS had the best AAY, 20%the BCRP had average AAY 24%

r2k,` = 0.0001 · d · k · `,

AAY of kernel based semi-log-optimal portfolio is 128%

double the capitalMORRIS had the best AAY, 20%the BCRP had average AAY 24%

r2k,` = 0.0001 · d · k · `,

AAY of kernel based semi-log-optimal portfolio is 128%double the capital

MORRIS had the best AAY, 20%the BCRP had average AAY 24%

r2k,` = 0.0001 · d · k · `,

AAY of kernel based semi-log-optimal portfolio is 128%double the capitalMORRIS had the best AAY, 20%

the BCRP had average AAY 24%

r2k,` = 0.0001 · d · k · `,

AAY of kernel based semi-log-optimal portfolio is 128%double the capitalMORRIS had the best AAY, 20%the BCRP had average AAY 24%

The average annual yields of the individual experts.

k 1 2 3 4 5`

1 20% 19% 16% 16% 16%

2 118% 77% 62% 24% 58%

3 71% 41% 26% 58% 21%

4 103% 94% 63% 97% 34%

5 134% 102% 100% 102% 67%

6 140% 125% 105% 108% 87%

7 148% 123% 107% 99% 96%

8 132% 112% 102% 85% 81%

9 127% 103% 98% 74% 72%

10 123% 92% 81% 65% 69%

principal component and constantly re-balanced portfoliooti/portfolio/lectures/bcrp.pdf0 hb, x 1i...

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