Statistika & Sains Data di Dunia Pasar Modal

Program Studi Statistika dan Sains Data

Departemen Statistika - IPB University

3 JUNI 2020

SEMINAR ONLINE STATISTIKA DAN SAINS DATA

Farid AbdurrahmanAssistant Vice President - Investment Banking

PT Mirae Asset Sekuritas Indonesia

Profil

Farid Abdurrahman

• 2000-2005 : Departemen Statistika – IPB University

• 2005-2012 : Debt Research PT Danareksa Sekuritas

• 2012-2013 : Portfolio Manager/Analyst PT Indo Premier Investment Management

• 2013-2016 : Fixed Income Research PT BCA Sekuritas

• 2016-sekarang : Investment Banking PT Mirae Asset Sekuritas Indonesia

Disclaimer: Materi presentasi ini merupakan pandangan pribadi, tidak mewakili institusi apapun.

2

Pelaku Pasar Modal

INSTITUSI

• Pelaku Utama Pasar Modal: Perusahaan Efek, Manajemen Investasi, Emiten, Investor

• Regulator: Pemerintah RI, Kementerian Keuangan, OJK, Bursa Efek, Kustodian Sentral, KPEI.

• Pendukung: Konsultan Hukum, Notaris, Wali Amanat, Badan Administrasi Efek

Perusahaan Efek:

1. Broker, Dealer, Sales (Equity Capital Market/Debt Capital Market) – WPPE, Market

2. Settlement/Penyelesaian Transaksi Efek – WPPE, Market

3. Trader (ECM/DCM) – WPPE, Market, Ekonomi, Finance, Statistika & Sains Data

4. Investment Banker – WPEE, Corporate Finance

5. Research Analyst:

• Equity Research:

• Fundamental Analyst: Accounting, Finance

• Technical Analyst: Market, Statistika & Sains Data

• Fixed Income/Debt Research:

• Credit Analyst: Accounting, Finance, Statistika & Sains Data

• Debt Market Analyst: Ekonomi, Statistika & SainsData

• Economist/Econometrician: Ekonomi, Statistika & SainsData

6. Quant Trading, Algo Trading: Market, Ekonomi, Finance, Statistika & Sains Data 3

Manajemen Investasi:

1. Portfolio Manager – WMI, Market,

Ekonomi, Finance, Statistika & Sains

Data

2. Portfolio Analyst - WMI, Ekonomi,

Finance, Statistika & Sains Data

3. Sales/Marketing – WPPE/WAPERD,

Market

PROFESI di Perusahaan Efek & Manajemen Investasi:

Golden Rules

Buy low, sell high

Buy high, sell low

BUY LOW,

SELL HIGH

Risk profiling

Managing risk & return: mencari return yang optimal pada tingkat risiko yang dapat ditoleransi

HIGH RISK,

HIGH RETURN

4

Mengapa Peran Statistician & Data Scientist Dibutuhkan?

1. Big Data: Di akhir 2019, terdapat 1.570 instrument investasi dengan market cap & outstanding lebih dari Rp10 ribu triliun. Di sepanjang 2019, rata-rata turnover harian transaksi di pasar modal senilai Rp38 triliun.

2. Data extraction: sehingga berguna dan dapat mendengarkan suara pasar.

3. Simplicity: analisa, research report, teaser, panggilan telepon.

4. Timing: siapa cepat dia dapat.

Sumber: IDX Yearly Statistics December 2019

Instrument NumberMarket Cap &

Outstanding (IDR bn) Daily Volume (bn unit) Daily Value (IDR bn) Daily Frequency (X)

Equity 668 7,265,016 15 9,106 469

Corporate Bond 805 445,101 1,579 1,570 149

Government Bond 97 2,752,741 28,059 27,660 1,123

Total 1,570 10,462,858 29,653 38,336 1,741

5

Proses Statistika & Sains Data

Hipotesis

Pengumpulan data: dari Bloomberg, Thomson Reuters, BPS, CEIC, Datastream, KSEI, OJK, BI, BEI, Perusahaan, Pemerintah, Kementerian terkait, Asosiasi Sektor terkait, berita.

Pemrosesan data: Excel, Database, Programming.

Analisa data: eksploratif, kuantitatif.

Kesimpulan/Rekomendasi: valuasi, beli, jual, nilai forecast, dll.

Tidak harus metode statistika yang canggih, metode statistika sederhana bisa bermanfaat.

6

Year 0

Saham Close Price Market Value Price x MV

Saham A 2,500 16,500 41,250,000

Saham B 12,600 56,040 706,104,000

Saham C 380 667,000 253,460,000

Saham D 770 2,090 1,609,300

Saham E 800 365,090 292,072,000

Total 1,106,720 1,294,495,300

Index Harga Saham

Average Price (Equal Weighting) 3,410

Wieghted Average Price by MV 1,170

Year Index Return Return + 1 Rebase Index

Year 0 (Initial) 1,170 100

Year 1 1,521 30.00% 1.30 130

Year 2 882 -42.00% 0.58 75

Year 3 1,014 15.00% 1.15 87

Year 4 1,095 8.00% 1.08 94

Year 0 to Year 4 Performance

Simple Annual Growth -1.59% -1.59%

Compounded Annual Growth Rate -1.63% -1.63%

Arithmetic Mean 2.75%

Geometric Mean -1.63%

Menghitung index

Memanfaatkan index

01. Perhitungan Index (1)INDEKS HARGA SAHAM

=simple average close price

=weighted average price by MV

=(Index Year 4 /Index Year 0 – 1 ) / 4

=(Index Year 4 /Index Year 0 ) ^ (1/4) – 1

=Rataan Return Year 1 s.d. Year 4

=Rataan Geometrik Return+1

𝐴𝑟𝑖𝑡ℎ𝑚𝑒𝑡𝑖𝑐 𝑀𝑒𝑎𝑛 =1

𝑛

𝑖=1

𝑛

𝑥𝑖

𝐺𝑒𝑜𝑚𝑒𝑡𝑟𝑖𝑐 𝑀𝑒𝑎𝑛 = ෑ

𝑖

𝑛

𝑥𝑖

1𝑛

𝐶𝐴𝐺𝑅 =𝑥𝑛𝑥0

1𝑛

− 1

7

IHSG dihitung Bursa setiap saat pada jam bursa

01. Perhitungan Index (2)

Sumber: INDICES METHODOLOGY – 13 July 2015 | Farid Abdurrahman – BCA Sekuritas

Indices

Interest

Return

Index

Price

Return

Index

Total

Return

Index

WA

Yield (%)

WA

Tenor

(years)

WA

Modified

Duration

WA

Convexity

Sensitivity:

Price changes if

yield -10bps

Sensitivity:

Price changes if

yield +10bps

WA

Coupon

(%)

Number of

Constituent

Total

Market

Value (Rp T)

Total

Outstanding

(Rp T)

Government bond broad indices

3-Feb-09 100.00 100.00 100.00 11.67 10.45 4.99 47.12 0.50 -0.50 11.86 40 354.43 365.38

30-Dec-09 110.63 110.60 122.35 9.58 10.79 5.34 54.64 0.59 -0.59 11.72 38 400.94 376.59

30-Dec-10 121.81 122.87 149.65 7.74 11.16 5.93 64.27 0.73 -0.73 11.03 37 510.27 443.05

30-Dec-11 133.02 138.91 184.76 6.20 12.39 7.00 84.21 0.98 -0.97 10.46 38 677.65 542.69

28-Dec-12 143.59 145.77 209.29 5.50 12.71 7.44 92.08 1.09 -1.08 9.66 39 721.36 557.26

30-Dec-13 155.42 116.76 181.45 8.59 12.06 6.56 73.53 0.77 -0.76 9.38 41 713.61 705.11

30-Dec-14 168.96 121.67 205.55 8.05 11.98 6.77 75.62 0.83 -0.82 9.33 39 1,029.87 978.35

8-Jul-15 176.40 120.01 211.68 8.28 11.47 6.52 71.19 0.79 -0.78 9.33 39 1,033.04 994.45

Government bond short-term indices

3-Feb-09 100.00 100.00 100.00 10.79 3.12 2.43 8.87 0.24 -0.24 12.95 18 115.51 107.38

30-Dec-09 110.98 104.22 115.66 8.04 2.77 2.25 7.76 0.23 -0.23 12.70 17 122.05 109.13

30-Dec-10 122.19 105.81 129.29 6.10 2.50 2.12 7.05 0.22 -0.22 11.89 13 123.13 108.59

30-Dec-11 133.45 105.22 140.41 4.95 2.58 2.21 7.84 0.23 -0.23 11.28 11 122.68 108.13

28-Dec-12 144.05 102.76 148.02 4.51 2.95 2.53 9.49 0.26 -0.26 9.93 9 113.52 99.74

30-Dec-13 155.91 92.63 144.41 7.72 2.83 2.40 8.70 0.22 -0.22 9.57 10 113.04 110.26

30-Dec-14 169.49 92.81 157.31 7.62 3.42 2.85 11.29 0.27 -0.26 9.46 10 150.85 145.80

8-Jul-15 176.96 92.13 163.03 7.77 2.91 2.46 8.77 0.23 -0.23 9.46 10 146.92 142.85

Government bond medium-term indices

3-Feb-09 100.00 100.00 100.00 11.47 7.21 4.77 31.81 0.48 -0.48 11.01 8 61.27 63.77

30-Dec-09 109.67 110.64 121.34 9.48 7.30 4.91 33.84 0.55 -0.54 11.05 7 63.63 59.62

30-Dec-10 120.75 123.62 149.26 7.30 7.54 5.22 37.66 0.65 -0.64 10.78 8 88.21 74.22

30-Dec-11 131.86 134.06 176.76 5.84 8.11 5.68 44.11 0.76 -0.76 10.60 9 132.28 102.47

28-Dec-12 142.34 139.24 198.17 5.13 8.34 5.96 47.26 0.83 -0.83 10.41 11 179.31 134.74

30-Dec-13 154.06 114.02 175.64 8.42 8.00 5.54 41.82 0.63 -0.63 9.86 11 193.12 184.13

30-Dec-14 167.48 117.05 196.03 7.87 8.21 5.72 44.29 0.67 -0.67 9.77 11 336.74 314.44

8-Jul-15 174.86 114.38 199.99 8.26 7.64 5.38 39.34 0.62 -0.61 9.77 11 337.96 322.69

Government bond long-term indices

3-Feb-09 100.00 100.00 100.00 12.36 16.34 6.74 77.28 0.68 -0.67 10.94 14 177.64 194.23

30-Dec-09 110.75 114.24 126.52 10.61 16.37 7.22 87.37 0.83 -0.82 10.87 14 215.26 207.83

30-Dec-10 121.94 132.54 161.61 8.68 15.79 7.72 95.69 1.03 -1.02 10.46 16 298.92 260.24

30-Dec-11 133.17 158.57 211.12 6.74 16.58 8.79 118.91 1.40 -1.39 9.89 18 422.70 332.09

28-Dec-12 143.75 170.38 244.88 5.97 17.12 9.36 132.72 1.61 -1.58 9.09 19 428.53 322.78

30-Dec-13 155.59 130.69 203.31 8.93 16.55 8.20 106.54 1.08 -1.06 9.01 20 407.46 410.72

30-Dec-14 169.14 138.30 233.89 8.29 16.71 8.52 112.97 1.19 -1.17 8.99 18 542.28 518.10

8-Jul-15 176.59 136.80 241.55 8.43 16.13 8.30 107.56 1.14 -1.13 8.99 18 548.17 528.91

Date

Statistics GOVERNMENT BOND INDICES

Kriteria Seleksi

IssuerGovernment of Indonesia.

CurrencyBonds must be issued in Indonesia Rupiah (IDR) denomination.

Market of issueBonds must be issued in Indonesia and listed in the Indonesia Stock Exchange (IDX).

CouponBonds must have a fixed coupon schedule or FR series only.

MaturityEach bond must have maturity greater than six month.

AgeEach bonds must have age greater than one month.

PricingEach bond must have daily close price. If there is no price in the particular date, previous day price is used.

SizeAll eligible bonds with any outstanding amount are included into the indices.

8

Index Obligasi tidak disediakan Bursa

02. Prediksi Harga/Yield

Variabel Independen YIELD SUN vs Variabel Dependen YIELD SUN 10-tahun

Sumber: Bloomberg, BCAS

0

2

4

6

8

10

12

14

16

18

20

0

2

4

6

8

10

12

14

1-J

an-0

5

1-D

ec-0

5

1-N

ov-

06

1-O

ct-0

7

1-S

ep

-08

1-A

ug-

09

1-J

ul-

10

1-J

un

-11

1-M

ay-1

2

1-A

pr-

13

1-M

ar-1

4

1-F

eb

-15

%%

BI Rate Yield 10-yr (RHS)

0

2

4

6

8

10

12

14

16

18

20

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1-J

an-0

5

1-J

an-0

6

1-J

an-0

7

1-J

an-0

8

1-J

an-0

9

1-J

an-1

0

1-J

an-1

1

1-J

an-1

2

1-J

an-1

3

1-J

an-1

4

1-J

an-1

5

%

USDIDR(t)/USDIDR(t-12mo) Yield 10-yr (RHS)

0

2

4

6

8

10

12

14

16

18

20

0

1

2

3

4

5

6

1-J

an-0

5

1-D

ec-0

5

1-N

ov-

06

1-O

ct-0

7

1-S

ep

-08

1-A

ug-

09

1-J

ul-

10

1-J

un

-11

1-M

ay-1

2

1-A

pr-

13

1-M

ar-1

4

1-F

eb

-15

%%

UST yield 10-yr Yield 10-yr (RHS)

Analisis Sensitivitas: simulasi monte-carlo

Sumber: BCAS

UST yield

10-yr (%)BI Rate (%) USDIDR

StDev 0.26 0.26 420 0.41

+3 StDev 3.65 7.79 15,386 9.73

+2 StDev 3.39 7.53 14,966 9.32

+1 StDev 3.14 7.26 14,545 8.91

Mean 2.88 7.00 14,125 8.50

-1 StDev 2.62 6.74 13,705 8.09

-2 StDev 2.36 6.48 13,284 7.68

-3 StDev 2.10 6.22 12,864 7.27

68

.27

%

95

.45

%

99

.73

%

AssumptionsYield SUN

10-yr F (%)

Probability of

Yield SUN 10-yr F

Sumber: MARKET OUTLOOK – 25 January 2015 | Farid Abdurrahman – BCA Sekuritas

PREDIKSI YIELD OBLIGASI SUN 10-TAHUNPREDIKSI INDEKS HARGA SAHAM

1. Pendekatan Bottom Up

• Analisis Fundamental

• Agregat Saham Kapitalisasi

Besar karena menggunakan

weighted arithmetic mean

2. Pendekatan Top Down

• Variabel Makroekonomi & pasar

• IHSG bersifat forward looking

Ekonomi

OLS, VAR, VECM

9

Variabel ekonomi dan pasar

Coupon Bond Yield Curve

Discount Factor

Zero Coupon Curve

Coupon Bond Yield

NSS Yield Curve Obligasi SUN

6.0

6.5

7.0

7.5

8.0

8.5

0 5 10 15 20 25 30

Yield Curve

Actual Yield

Yie

ld to M

aturity

(%

)

Term to Maturity (years)

03. Yield Curve: Term Structure of Interest Rates

• Non-Linear Curve Fitting

• Kandidat Model:

• Spline Model

• Polinominal Model

• Nelson Siegel Svensson (NSS) Model: level, slope, two humps

• Berbasis Time Value of Money

Konsep NSS Model: Time Value of Money

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=883856

Constant YTM &

Coupon effect

10

• Tujuan: meminimumkan Sum

Square Yield Error

• Program: Solver Excel

• Metode:

• Generalized Reduced

Gradient (GRG) Non-Linear

Multistart: Smooth Non-

linear Global Minima

• Simplex LP: Linear

• Evolutionary: Non-smooth

Non-linear Sumber: engineerexcel.com

NSS Model

04. Trading Ideas: Valuasi berdasarkan Yield CurveDua tujuan:

• Investor: mengoptimalkan return of investment

• Perusahaan Efek: meningkatkan brokerage fee.

cheap vs dear . z-value

1 FR0055

2 FR0060

3 FR0028

4 FR0066

5 FR0032

6 FR0038

7 FR0048

8 FR0069

9 FR0036

10 FR0031

11 FR0034

12 FR0053

13 FR0061

14 FR0035

15 FR0043

16 FR0063

17 FR0046

18 FR0039

19 FR0070

20 FR0044

21 FR0040

22 FR0037

23 FR0056

24 FR0059

25 FR0042

26 FR0047

27 FR0064

28 FR0071

29 FR0052

30 FR0073

31 FR0054

32 FR0058

33 FR0065

34 FR0068

35 FR0072

36 FR0045

37 FR0050

38 FR0057

39 FR0062

40 FR0067

-1.97

-1.15

-1.09

-1.14

1.67

1.03

1.46

-0.23

1.14

-1.37

0.14

-0.54

0.61

0.50

-0.97

2.67

-0.36

-0.24

0.41

-0.81

0.79

-0.68

-0.04

0.08

-0.10

-1.91

0.32

-0.22

0.00

-0.08

-0.08

0.29

0.04

0.42

-0.04

0.05

-0.05

-0.01

0.40

-0.12

-0.36

1.96

0.20

1.34

-1.27

0.84

1.04

-0.26

-1.46

-0.01

-0.01

0.05

0.03

-0.06

0.16

-0.05

0.05

0.06

-0.10

-0.21

-0.23

0.11

0.04

-0.16

0.61

0.00

-0.48

-5.2bps

Jul-38

May-41

Aug-30

May-31

Jul-31

Jun-32

May-33

Mar-34

Sep-26

May-27

Sep-24

Sep-25

Sep-26

10.00%

5.25%

15.00%

11.60%

9.00%

7.88%

11.00%

8.75%

9.50%

8.25%

6.63%

12.00%

8.38%

7.00%

10.25%

10.00%

10.25%

5.63%

Nov-20

Jun-21

Jul-21

May-22

Jun-22

Sep-16

May-18

Apr-17

Apr-19

Sep-18

Aug-18

Jul-18

May-36

May-37

11.75%

8.38%

10.00%

11.50%

11.00%

12.80%

8.25%

7.00%

12.90%

6.13%

9.50%

-15.6bps100.25

Jul-17

model

price yield

8.43

8.0420.88 9.59 144.27 117.03 -4.0bps

8.0319.88 9.80 146.51 103.99 -8.7bps 102.15

17.71 9.12 103.84127.18 104.17 -7.4bps

117.19

101.96 108.13 -8.4bps

27.63 10.39 189.71 ##### -1.7bps 106.23 8.18

80.61 8.1925.80 11.01 200.31

Jul-23

Aug-23

Mar-24

Sep-19

6.4bps 0.31

-0.23

8.1224.88 10.17 170.14 113.35 6.2bps 114.59

22.05 9.39 148.82 124.57 9.3bps 125.02 8.06

-0.44

-58.9bps

19.9bps

80.923

43.8bps

121.99

7.78

87.38 7.77

7.88

16.88 9.50 130.10 88.29 1.8bps 87.70

103.32 -4.7bps 103.07

7.72 89.81 122.16 -5.6bps

7.91

7.89-15.1bps

7.8615.05

122.65

15.96 8.89 113.09

14.88

8.09 100.27

7.96

7.81

113.90 -7.4bps 114.29

107.68

70.2bps

7.60

-2.03

-0.88

0.12

7.58 82.21 109.64 -5.0bps 109.73

117.20

11.88 7.97 83.94 87.18

7.7942.7bps

7.7311.63 6.99 70.55 117.00

7.63

7.66

7.72

7.5678.1bps 104.82 7.68

106.9bps

49.28

7.70

-0.2bps

5.96 4.35 24.50 125.56 2.8bps

5.88 4.69 27.28 97.69 -0.2bps 97.56

-26.2bps 114.477.61

125.58

5.00 34.14 110.15

8.21 5.61

7.05

7.56 0.6bps 104.53

122.40

42.69 114.38 3.9bps

6.88 5.48 36.79

7.42

2.21 1.94 5.08 103.67 3.4bps 103.68

5.05 3.95 20.58 103.44 -8.3bps

4.96 3.79 18.38 121.93 0.9bps

7.47

7.167.16-7.9bps

34.9bps 103.217.42

-5.6bps

4.38 3.48 15.49 113.32 -4.3bps

7.31

7.27

2.63 9.23 111.81 4.1bps

2.80 2.44 7.66 101.61 -4.8bps 101.48

7.3816.3bps 113.15

6.957.8bps 114.78

7.2211.9bps

-0.03

0.06

2.13 1.82 4.65 108.23 0.0bps

2.05 1.69 4.26 115.07 -4.6bps

7.13

0.11

-0.12

-0.02

7.350.0bps

1.88 1.74 3.98 -4.0bps 96.817.2bps96.85

0.80

7.96

7.82

7.86

7.9371.6bps

8.0545.7bps

-114.4bps

-72.7bps

7.8589.3bps

6.38%

9.00%

10.50%

Apr-42

Feb-44 8.75%

7.80-16.4bps

Jul-27

Feb-28

May-28

Mar-29

7.72-7.0bps

7.90

54.7bps

76.4bps

1.0bps 94.75

5.4bps

10.88 7.35 71.33 94.74

10.21 6.75 62.14 105.72

-46.3bps

11.05 6.69 65.26 118.64

7.75

-27.9bps 3.3bps7.71 118.69

61.8bps9.21 5.95 7.69

-10.8bps

10.21 6.26 55.78 130.01

14.13

7.70

2.4bps

12.71

121.55 -8.5bps

7.87

7.57

7.71 5.54 40.59 104.69 -4.0bps

6.05 4.39 26.37 112.79

89.70

7.30-14.2bps 111.76

-17.6bps

0.21 6.04

7.507.50

1.0bps

7.13 4.88

7.49

Jul-22

May-23

32.99 7.62

25.6bps 89.73 7.57

0.1bps 113.10 7.51

-22.5bps

7.57

7.61 3.9bps 110.47 7.55

7.58

7.52

-45.6bps

108.66

7.09

103.20

2.0bps

8.5bps 122.82 7.55

6.590.76 0.96 99.73 -2.5bps 99.69 6.65

6.766.84

3.21

chg last

1.05 0.95 1.46 103.12

GOV'T SECURITIES VALUATION convexdurtenor

7.38%

6.25%

price

7.07

-12.9bps 130.31

8.09

43.1bps

yield

7.09

8.276

8.156

121.84 7.45

-53.9bps

7.43

8.38%

8.25%

9.75%

10.50%

9.50%

chglast

8.23

3.0bps0.21 0.15

-0.5 -0.25 0 0.25 0.5 -1 -0.5 0 0.5 1

Sumber: BCAS INSIGHT– 29 Juni 2016 | Farid Abdurrahman – BCA Sekuritas

Cheap Dear: spread yield aktual vs yield model menggunakan data historis trailing 20 hari.

Z-Value: Standardisasi atau Normal Baku Cheap Dear, menggunakan data historis trailing 20 hari.

Benchmark Bonds: tenor 5, 10, 15, 20 tahun selalu Dear?

NSS Yield Curve Obligasi SUN

6.0

6.5

7.0

7.5

8.0

8.5

0 5 10 15 20 25 30

Yield Curve

Actual Yield

Yie

ld to M

aturity

(%

)

Term to Maturity (years)

Gagasan Valuasi:

Titik di bawah kurva: Dear

Titik di atas kurva: Cheap

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05. Pengukuran Risk and Return

• Mencari instruments: higher return, sesuai dengan profil risiko.

• Data: return instrument investasi dalam interval waktu tertentu.

• MV Efficient Frotier - Markowitz

• Rate of Return = CAGR (GMEAN)

• Risk Tolerance = Volatility Risk (VARIANCE)

• Koefisien Keragaman = StDev/Mean

• Sharpe Ratio:

• Adj Return / Std. Deviation of Adj Return

• Adj Return = Return Portfolio – Risk Free Return

• Risk Free Return = yield SPN Pemerintah

Sumber: Investopedia

MV Efficient Frontier

Sharpe Ratio

Sumber: IPOT Fund - 29 Mei 2020 12

06. Risiko Kredit: Kasus Bisnis Operasional

Kasus Bisnis Operasional:

• Broker membutuhkan pendanaan transaksi obligasiSUN senilai 90% dari nilai transaksi dari Bank dalamhari yang sama (intraday).

• Bank memiliki risiko kredit ketika terjadi perubahanharga drastis terjadi di pasar dari tinggi ke rendahatau rendah ke tinggi dan broker gagal bertransaksisisi lawan intraday. (Ingat bahwa broker melayanisisi jual dan sisi beli investor)

• Bank tidak mau memberikan pendanaan jika risikokredit tersebut tinggi. (Bank clueless tingkat risikoyang bisa mereka terima di berapa)

Data:

Harga Low, Harga High, Harga Close, Volume, Frequency (LHCVF) harian per seri obligasi.

sumber: trindamsn.blogspot.com

Histogram spread harga high - low

YOU GUESSED IT….

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07. Cluster Analysis

Pengelompokan saham/obligasi yang memilikikarakter risk & return mirip.

• Manajemen portofolio: diversifikasi risiko.

• Kemungkinan mempermudah modelling untukcluster tertentu. Misal: ada cluster yang cepat meresponsperubahan suku bunga, nilail tukar, dll.

Diversifikasi sektor?

• Masing-masing sektor memiliki respons yang berbeda terhadap variable ekonomi.

• Kombinasi diversifikasi sektor dan hasil cluster analysis dapat memberikan dampaik diversifikasiyang positif pada portofolio.

Sektor:

Cluster:

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08. Liquidity Index

• Aspek likuiditas menurut Abdourahmane Sarr & Tonny Lybek - IMF:• Transaction cost measures: bid-ask spread

• Volume-based measures: trading volume vs outstanding

• Price-based measures: Market Efficiency Coefficient

• Market-impact measures: ketika buy harga naik, sell harga turun

• Disederhanakan menjadi satu nilai: Liquidity Index

• Manfaat:• Diversifikasi portfolio berdasarkan likuiditas

• Simulasi trading ideas melibatkan liquidity index

• Strategi investasi berdasarkan tingkat likuiditas: trading, available for sale, hold to maturity

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=880932

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Tampilan papan trading

HOTS Mirae Asset Sekuritas Indonesia

09. Credit Score Obligasi

• Credit Score:• Obligasi diperingkat oleh agen pemeringkat dengan masa evaluasi satu

tahun.• Masalah: Investors ingin mendapatkan insight risiko kredit lebih cepat dari

evaluasi agen pemeringkat.• Solusi: credit score dibuat untuk melihat perkembangan risiko kredit antar

periode evaluasi agen pemeringkat.• Biasanya hanya melihat aspek kondisi keuangan Perusahaan• Model: regresi logistik dengan peubah dependen credit score mengikuti

kategori peringkat utang agen pemeringkat

• Altman Z-Score: mengukur probability default dalam 2 tahunAltman Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E• A = working capital / total assets• B = retained earnings / total assets• C = earnings before interest and tax / total assets• D = market value of equity / total liabilities• E = sales / total assets

sumber: wikipedia

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10. Quant/Algo Trading Strategy

BASE MODELS:

• Peubah ekonomi & Industri

• Analisis fundamental:

• Account keuangan penting: revenue, net income, cash, total asset dll

• Pertumbuhan keuangan: asset growth, EBITDA growth, net income growth, dll

• Rasio keuangan: profitabilitas, likuiditas, leverage, turnover, dll

• Multiple valuation: PER, PBV, EV/EBITDA dll

• Multi-years valuation: Discounted Cash Flows.

• Analisis teknikal:

• Indikator trend

• Indikator momentum

• Pattern analysis, dll

• Berita

• Model: Bayesian, Random Forrest, Neural Network, Natural Language, dll

VARIABEL DEPENDEN:

• Kategorik vs Continue

Algo Trading ≠ High Frequency Trading

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Terima kasihFarid Abdurrahman

farid@abdurrahman.id

LIBERIKA