statistika & sains data di dunia pasar modal · statistika & sains data di dunia pasar...
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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
LIBERIKA