How Does Optimize Peer to Peer Lending Investment
Wahyu Mulyadi1,
Budi Purwanto2, Wita Juwita Ermawati3, Nurhidayah
Kusumaningrum Fadhilah4
wahyu.mulyadi@nusaputra.ac.id,
budipurwanto@apps.ipb.ac.id,
witaman@apps.ipb.ac.id,
nhkfadhilah@nusaputra.ac.id
Abstract
This
research uses general data about loans in 5 Credit Grades A, B, C, D and E
which can be obtained from the KoinWorks P2PL factsheet platform. The research
results show that there are 4 combinations of funding assets in the calculation
of the optimal portfolio of the Markowitz Model with the lowest risk
preferences consisting of funding assets in Credit Grades A, B, D and E with an
expected portfolio return of 24.29% for the year and 2.02. % for monthly and
the best risk level in a year of 1.39% for annual and 0.11% for monthly.
Meanwhile, in the optimal portfolio planning of the Markowitz model with sharpe
ratio, there are 3 combinations of funding assets consisting of Credit Grades
A, B and D which obtain an expected portfolio return of 18.29% in the current
year and 1.52% in that month. and the level of risk. best in a year of 1.39%
for this year and 0.48% for this month, and portfolio performance of 13.1.
Keywords: peer to
peer lending, portfolio, Markowitz model, sharp ratio
Introduction
The development of information technology means that the
financial industry must be ready to change and transform (Omarini,
2017). Now information technology has
entered the era of financial industrial revolution 4.0. This era is
increasingly embedded in changing people's lifestyles in Indonesia, changes in
consumption patterns and people's desires for something easy and fast (Muljani
& Ellitan, 2019). The change in behavioral patterns
in the financial sector has been followed by the proliferation of financial
technology (fintech) for both payments and funding or better known as
peer-to-peer lending (P2PL). Along with advances in information technology, it
is not balanced with the public's understanding of investing in peer to peer
lending fintech platforms (Setiawan
et al., 2020).
The lack of knowledge regarding investment instruments,
especially those related to the capital market, is the factor that most
influences the low investment interest of the Indonesian people (Prayudha
& Kuswanto, 2019). Regarding the importance of
investment and the types of investment instruments available in making it
easier for people to choose suitable investments, both in terms of profits and
risks, therefore the public needs to receive basic education about investing.
These transactions are mostly carried out by specialized platforms, where
financial institutions only serve as intermediaries (Havrylchyk
& Verdier, 2018). P2PL lending platforms create a new
market environment for borrowers and investors (lenders). Borrowers generally
explain their reasons and objectives for applying for a loan and include
various information about their financial status (income, credit history, home
ownership, debt, etc.)
On the other hand, a low rate of return on people's deposits
and savings can change overall investment behavior (Dolan
et al., 2012). This creates a new environment that
is very profitable for P2PL platforms which provide new alternatives for people
in investing compared to traditional methods such as opening savings or
deposits in banking. People want to be independent in their investment
decisions and not depend on financial institutions. P2PL platforms provide a
suitable marketplace where borrowers and lenders make their own decisions
without financial institutions as financial intermediaries (Slavin,
2007). Apart from that, low interest rates
on savings and deposits after the Covid-19 pandemic have forced people to look
for new forms of investment to provide more attractive returns. For a
comparison, the rate of return on investing in the form of savings and deposits
is around 2% to 6%. It will be more attractive for people to invest using a
P2PL platform that claims a rate of return of 15% or higher.
In 2016, the new Koinworks P2PL platform was launched in
Indonesia, on this platform investors or lenders can find out information about
their borrowers in forming interest rates in a fact sheet which contains
information in the form of: loan amount, term, loan purpose, type of payment,
type of loan and form of business. All the information contained in the
factsheet is then entered into a credit grade form ranging from Grade A to
Grade E, so that investors can know the returns they will get and the risks they
may face later.
By using this information, investors or lenders who want to
invest should have the ability to diversify in forming an optimal investment
portfolio regarding the expected returns and possible risks that will be faced
in the investment process, but the data used in this research is past data
whether the portfolio which is formed by minimizing risk and can maintain
investment value nominally and in real terms (Suryono
et al., 2021).
The Koinworks P2PL platform provides borrowers with
information about the level of risk and rate of return which is described in
interest rates which are then classified into Credit Grade A, B, C, D and E,
which will be However, investors on the P2PL platform, especially Koinworks
products, do not yet understand and realize that risks such as the risk of
default that will arise as a result of investing can be mitigated in the form
of optimal portfolio diversification. As a result of this lack of understanding,
many investors who are risk averse only invest in Koinworks products which have
a low level of risk but the returns given are small, such as investing in
products or borrowers that are classified as Credit Grade A and B and vice
versa, if investors are risk-taking and want high returns, they will tend to
invest in products or borrowers in Credit Grades C and D, even though if
investors understand how to invest in a portfolio by paying attention to the
risk-return combination for each product offered by the platform P2PL Koinworks
can be on the efficient frontier of optimal portfolios. However, the data used
in this research is past data on whether the portfolio formed by minimizing
risk can maintain investment value in nominal and real terms (Campbell
et al., 2001).
Method
This research was conducted in February 2019-December
2023. The data in this research consists of secondary data totaling 7876
borrowers which can be obtained on the KoinWorks P2PL platform factsheet. The
quantitative data that will be used includes historical data in the form of
borrower or borrower information, starting from Grade A which has the lowest
returns but low risk to Class E which has high returns but also has high risks.
KoinWorks P2PL Platform 2019-2023. The object of the research is the KoinWorks
P2PL platform which has information on borrowers or borrowers in credit ratings
from Grade A to Grade E in the 2019-2023 period. The population of this study
is information on borrowers or borrowers available on the KoinWorks P2PL
platform starting from Grade A to Grade E. Sampling uses a non-probability
sampling technique with a census method, namely a sampling technique if all
members of the population are used as samples or examples.
This portfolio analysis uses two methods as a tool in
forming an optimal portfolio, namely the Mean-Variance method and the Sharp
Ratio method. The Markowitz Model portfolio analysis relies on parameters in
the form of return, variance and covariance for each funding in credit grading.
The assumption used in using this method is that investors ignore funding in
risk-free funding assets. The Mean-Variance portfolio optimization model is
formulated with the following stages:
Calculating the Expected Return
E(R)=_files/image002.gif){kind=small}
(1)
In
where : E(R) = expected return, Ri = I-th return that
may occur, pri = probability of the I-th return, n = number of possible returns
Calculating Risk
varians return= _files/image004.gif){kind=small}
=[ Ri-E(R)_files/image006.gif){kind=small}
pri. (2)
And
Standar deviasi=
s = (_files/image008.gif){kind=small}
(3)
In where :
s2 = variance of return, s = standard deviation, E(R) = expected
return, Ri = possible I-th return, pri = probability of the i-th return
Calculating the Correlation Coefficient
ρ = _files/image010.gif){kind=small}
(4)
In
where ; = Correlation coefficient, n = number of
funding results in grades that may occur, RA = funding returns in Grade A, RB =
funding returns in Grade B
Calculating the Expected Return of the
Portfolio
E(Rp)=Ʃ_(i=n)^nWiE(Ri) (5)
In where: E(Rp) = expected
return from the portfolio, Wi = weight of the I-th portfolio, E(Ri) = expected
return of the I-th, n = total amount of funding on the grade in the portfolio
Calculating Portfolio Risk
sp= [_files/image012.gif){kind=small}
A A + B_files/image014.gif){kind=small}
B +2(_files/image016.gif){kind=small}
)(_files/image018.gif){kind=small}
)(_files/image020.gif){kind=small}
)_files/image022.gif){kind=small}
(6)
In where: _files/image024.gif){kind=small}
= standar
deviasi portfolio, _files/image026.gif){kind=small}
= portfolio
weight on grade A _files/image028.gif){kind=small}
= correlation
coefficient grade A and B
Sharp Index Method
R/Vs = _files/image030.gif){kind=small}
(7)
In where : R/Vs = Indeks Sharpe (reward to variability ratio), _files/image032.gif){kind=small}
= Average portfolio return, _files/image034.gif){kind=small}
= risk-free investment interest, _files/image036.gif){kind=small}
= Standard deviation of portfolio return.
Results and Discussion
Company
Profile PT. Lunaria Annua Technology (KoinWorks)
PT. Lunaria Annua Teknologi or better known as
KoinWorks is a provider of Financial Technology (Fintech) based money lending
and borrowing services using the peer to peer lending
(P2PL) method, where borrowers who need funding are connected with potential
investors or lenders. Koinworks offers a payment system, loan assessment system
and technology that provides a better experience for investors and borrowers.
Koinworks is the first information technology-based money lending and borrowing
service provider that has been officially registered and supervised by the
Financial Services Authority (OJK) since May 4 2017 with registered letter
S1862/NB.111/2017.
KoinWorks is here as a Super Financial App, which is
the solution to all personal and business financial needs. KoinWorks wants to
make all the financial dreams of lenders and borrowers come true in the future
with just one dashboard. PT Sejahtera Lunaria Annua (PT SLA), which
collaborates with PT. Lunaria Annua Teknologi as an affiliate in organizing the
KoinWorks Super Financial App, has been registered as a Digital Financial
Innovation Organizer in the Aggregator Cluster at the Financial Services Authority
(OJK), with registration letter number No. S-87/MS.72/2020 dated 10 February
2020. KoinWorks also has Electronic System Operator Registration Certificate
(PSE) No. 00257/DJAI.PSE/02/2020 and has been registered as a member of the
Indonesian Fintech Association (AFTECH),
Data
Characteristics of KoinWorks Peer to Peer Lending Platform
Credit grade is a system usually used by financing
or banking institutions to determine whether or not it is appropriate to
receive a loan. Credit grade is done by analyzing all borrower data which is
collected through the filling they have done previously for the loan
application. So, it could be said that transaction history, such as paying
bills correctly or not or how much credit you have, can also be used as a
determinant of credit grade.
Credit grade really helps banks or other financial
institutions in analyzing credit applications in addition to other factors (Min & Lee, 2008). Currently debtor credit
report data or now better known as the Financial Information Services System
(SLIK), which replaces BI Checking, can only be viewed directly by the
Financial Services Authority (OJK). In this credit assessment, there are also
many factors that can be taken into consideration, such as age, marital status,
residence status, education, type of work, length of work and others. Apart
from banks which usually implement a credit grading system, Peer-to-Peer
Lending (P2PL) financial technology (fintech) companies, especially KoinWorks,
also implement the same thing.
KoinWorks uses credit grading in selecting potential
borrowers. Each P2PL platform, including KoinWorks, has its own credit grade
model, such as analyzing Cashflow or cash flow from prospective borrowers,
analyzing the collateral provided (which can be in the form of bills from
Invoices and inventory), as well as analyzing Credit Behavior. CoinWorks credit
grade results will have an impact on the amount of expected return or interest
rate charged to prospective borrowers. As a reference, the following is a credit
grade table based on interest rates and risks on the KoinWorks P2PL platform:
Table 1 Credit Grade Grouping Based on Interest
Rates and Risk
|
Credit Grade |
Expected Return/ Interest Rate |
Protection Fund |
|
A (Lowest Risk/Return) B C D E (Highest Risk/Return) |
15-19% 19-24% 24-29% 29-34% 34-38% |
100% 80% 60% 40% 20% |
Source: Processed data (2023)
Based on the data above, peer-to-peer lending (P2PL)
fintech startup KoinWorks is the only platform that provides protection
initiatives in the form of Protection Funds. Protection funds aim to minimize
investor capital losses if a borrower fails to pay. The loan will be
categorized as failed if the borrower does not pay the installments within 90
days and does not provide information regarding the delay. Within 30 days of
the announcement of the loan as default, KoinWorks will take protection funds
to be paid to investors to reduce capital losses. Capital loss is the
difference between the initial capital amount and the total payments received
from loan installments. In the same time frame, KoinWorks will immediately
write off the loan after the announcement of default.
The existence of the Protection Fund is a pure
KoinWorks initiative to protect investment funds up to 100%. KoinWorks defines
five levels compensation for reducing investment fund losses, in different loan
categories through Credit Grade A to E. Credit Grade is determined based on the
results of the borrower's risk level analyzed by KoinWorks. By considering the
amount of the Protection Fund, the range of compensation given to investors
varies, starting from 20% for investors who provide grade E, to 100% for grade
A investors.
Table 1 presents the grouping of funding assets in
credit grade based on loan amount. On the KoinWorks P2PL platform there are 5
grades, namely A to E, and each grade consists of five different levels, such
as A1 to A5, where A1 is the most considered capable. to pay loans and E5 is
the lowest grade in this case. At a safe point, for example A1, the interest
rate charged is the lowest while at E5 it is the highest. For investors,
investing in E5 will provide greater profits but has higher risks, while in A1,
investor funds will be much safer but the profits will be minimal. This is
where it is useful for investors to diversify while understanding interest
rates and credit scoring, so that they can form an optimal portfolio by
calculating the returns and risks that will be obtained.
Formation
of an Optimal Portfolio with the Markowitz Model
Markowitz shows how portfolio diversification can
minimize risk. Portfolio risk is not just a weighted average of each funding
asset at credit grade in the portfolio, but must also consider the relationship
between these funding assets. The statistical concepts that are important here
are correlation and covariance (Sun & Weckwerth, 2012). Correlation is a measure
that describes the level of closeness of the return relationship between two
funding assets in the portfolio. Meanwhile, covariance is a measure that shows
the extent to which the returns from two funding assets in a portfolio tend to
move together. According to Markowitz (1952), portfolios are based on the
assumption that investment decisions only depend on the values of E(Rp) and
Ơp2 of the total return of the portfolio. With the Markowitz
model, investors can form an optimal portfolio where the portfolio is able to
minimize variance or risk with a certain expected return value
Based on the explanation in the research method
section, the procedure that must be carried out first is to calculate the
expected return and individual variance for each funding asset at credit grade
Calculate the expected rate of return (E(Ri)) which
is in the form of daily data. Calculated using the following formula equation:
E(Ri) = _files/image038.gif){kind=small}
(8)
The risk level of funding assets at credit grade is
calculated using the variance (you can also use the formula from Ms. Excel,
namely with STDEVA first to get the standard deviation) of the rate of return
for each share. Or with the following formula:
_files/image040.gif)
(9)
The following is the calculated data that will be
included in the Markowitz model portfolio presented in Table 2:
Table 2 Calculation Results of Expected Return,
Risk and Risk Free Rate
|
No |
Credit Grade |
Annual |
Monthly |
||||
|
E(Ri) |
SD |
Rf |
E(Ri) |
SD |
Rf |
||
|
1 |
A |
0.1580 |
0.0136 |
0.06 |
0.0132 |
0.0011 |
0.005 |
|
2 |
B |
0.1923 |
0.0152 |
0.06 |
0.0160 |
0.0013 |
0.005 |
|
3 |
C |
0.2419 |
0.0162 |
0.06 |
0.0202 |
0.0014 |
0.005 |
|
4 |
D |
0.2982 |
0.0158 |
0.06 |
0.0248 |
0.0013 |
0.005 |
|
5 |
E |
0.3246 |
0.0160 |
0.06 |
0.0271 |
0.0013 |
0.005 |
Source: Processed data (2023)
Based on the data in Table 2 above, the largest
funding asset risk is in Credit Grade C, namely for an annual amount of 0.0162
and a monthly amount of 0.014. Then the smallest expected return is found in
funding assets in Credit Grade A, namely 0.1580 for annual expected return and
0.0132 for monthly expected return. Meanwhile, the largest expected return is
on funding assets in Credit Grade E, namely 0.3246 for annual expected return
and 0.0271 for monthly expected return.
The next step is to calculate the covariance and
correlation values of the returns between shares. Correlation calculations are
carried out using 'Data Analysis' contained in the MS program. Excel. Table 3
below shows the covariance values:
Table 3 Annual Markowitz Model Optimal
Portfolio Covariance Matrix Values
|
Credit Grade |
A |
B |
C |
D |
E |
|
A |
0.0001862 |
0.0001926 |
0.0002116 |
0.0000928 |
0.0000419 |
|
B |
0.0001926 |
0.0002299 |
0.0002306 |
0.0000927 |
0.0001176 |
|
C |
0.0002116 |
0.0002306 |
0.0002638 |
0.0001474 |
0.0000463 |
|
D |
0.0000928 |
0.0000927 |
0.0001474 |
0.0002481 |
-0,0000984 |
|
E |
0.0000419 |
0.0001176 |
0.0000463 |
-0,0000984 |
0.0002537 |
Source: Processed data (2023)
Table 4 Monthly Markowitz Model Optimal Portfolio Covariance Matrix
Values
|
Credit
Grade |
A |
B |
C |
D |
E |
|
A |
0.0000013 |
0.0000013 |
0.0000015 |
0.0000006 |
0.0000003 |
|
B |
0.0000013 |
0.0000016 |
0.0000016 |
0.0000006 |
0.0000008 |
|
C |
0.0000015 |
0.0000016 |
0.0000018 |
0.0000010 |
0.0000003 |
|
D |
0.0000006 |
0.0000006 |
0.0000010 |
0.0000017 |
-0,0000007 |
|
E |
0.0000003 |
0.0000008 |
0.0000003 |
-0,0000007 |
0.0000018 |
Source: Processed data (2023)
Before calculating the proportion of funding assets
in credit grades A to E, the inverse covariance matrix of returns on funding
assets in Credit Grades A to E is calculated using Ms. Excel as follows
Table 5 Inverse Value of the Optimal Portfolio Covariance Matrix for the
Annual Markowitz Model
|
Credit
Grade |
A |
B |
C |
D |
E |
|
A |
79131 |
-13181 |
30976 |
21279 |
50622 |
|
B |
-13181 |
21958 |
-51601 |
-35447 |
-84327 |
|
C |
30976 |
-51601 |
12126 |
83299 |
19816 |
|
D |
21279 |
-35447 |
83299 |
57221 |
13612 |
|
E |
50622 |
-84327 |
19816 |
13612 |
32384 |
Source: Processed data (2023)
Table 6 Inverse Value of the Optimal Portfolio Covariance Matrix Monthly
Markowitz Model
|
Credit
Grade |
A |
B |
C |
D |
E |
|
A |
-29560 |
49089 |
-11642 |
-7805 |
18958 |
|
B |
49089 |
-81519 |
19334 |
12961 |
3148 |
|
C |
-11642 |
19334 |
-45857 |
-3074 |
-7466 |
|
D |
-7805 |
12961 |
-3074 |
20608 |
-50056 |
|
E |
-18958 |
3148 |
-74669 |
50056 |
12158 |
Source: Processed data (2023)
After the inverse matrix value of the covariance
matrix for annual and monthly returns on funding assets at Credit Grade A to E
is obtained, the proportion for each funding asset in the portfolio will be
calculated by solving the algebraic equation. From this algebraic equation, the
proportion of each funding asset in Credit Grade A to E is obtained as follows.
Table 7 Optimal Portfolio Proportions Markowitz
Model
|
Credit
Grade |
Annual Proportion |
Monthly Proportion |
|
A B C D E |
0.128331108 2.700686156 -3.788860267 1.831383333 0.12845967 |
0.13466709 2.688443212 -3.773033587 1.822331605 0.127591681 |
Source: Processed data (2023)
Based on the data in Table 7 above, there is a
proportion of funding assets that have a negative value, namely at annual and
monthly Credit Grade C. The proportion or weight in forming a portfolio using
the Markowitz Model method has several constraint functions, namely the first
constraint function is the total proportion invested in each funding asset for
the whole is equal to 1 or 100% and the second constraint function is the
proportion of each Each funding asset cannot have a negative value. Where, the
Markowitz Model portfolio does not allow negative proportions in each funding
asset. Therefore, funding assets in annual and monthly Credit Grade C must be
removed or eliminated from the optimal portfolio of the Markowitz Model. So
that means we have to repeat the weighting process from starting to calculate
the covariance as before.
Table 8 Optimal Portfolio Covariance Matrix Values Annual Markowitz Model
without Credit Grade C
|
Credit
Grade |
A |
B |
D |
E |
|
A |
0.0001862 |
0.0001926 |
0.0000928 |
0.0000419 |
|
B |
0.0001926 |
0.0002299 |
0.0000927 |
0.0001176 |
|
D |
0.0000928 |
0.0000927 |
0.0002481 |
-0,0000984 |
|
E |
0.0000419 |
0.0001176 |
-0,0000984 |
0.0002537 |
Source: Processed data
(2023)
Table 9 Optimal Portfolio Covariance Matrix Values Monthly
Markowitz Model without Credit Grade C
|
Credit
Grade |
A |
B |
D |
E |
|
A |
0.0000013 |
0.0000013 |
0.0000006 |
0.0000003 |
|
B |
0.0000013 |
0.0000016 |
0.0000006 |
0.0000008 |
|
D |
0.0000006 |
0.0000006 |
0.0000017 |
-0,0000007 |
|
E |
0.0000003 |
0.0000008 |
-0,0000007 |
0.0000018 |
Source: Processed data
(2023)
The optimal portfolio
covariance matrix of the Markowitz model without funding assets in Credit Grade
C that has been obtained will then be used to calculate the weight value of
each funding asset in Credit Grades A, B, D and E
Before calculating the
proportion of funding assets in credit grades A, B, D and E, the inverse
covariance matrix of returns on funding assets in Credit Grades A, B, D and E
is calculated using Ms. Excel as follows:
Table 10 Inverse Value of the Optimal Portfolio Covariance
Matrix for the Markowitz Model without Annual Credit Grade C
|
Credit
Grade |
A |
B |
D |
E |
|
A |
77100 |
-52697 |
-19380 |
-2059 |
|
B |
-52697 |
42582 |
9264 |
1099 |
|
D |
-19380 |
9264 |
12581 |
960 |
|
E |
-2059 |
1099 |
960 |
121 |
Source: Processed data
(2023)
Table 11 Inverse Value of the Optimal Portfolio Covariance
Matrix for the Markowitz Model without Monthly Credit Grade C
|
Credit
Grade |
A |
B |
D |
E |
|
A |
11064 |
-7463 |
-2840 |
-2958 |
|
B |
-7463 |
5979 |
1342 |
1551 |
|
D |
-2840 |
1342 |
1839 |
1396 |
|
E |
-295 |
155 |
139 |
174 |
Source: Processed data
(2023)
After the inverse matrix
value of the covariance matrix for annual and monthly returns on funding assets
in Credit Grades A, B, D and E is obtained, then the proportion for each
funding asset in the portfolio will be calculated by solving the algebraic equation. From this
algebraic equation, the proportion of each funding asset in Credit Grade A, B,
D and E is obtained as follows :
Table 12 Proportions and Combinations of the Markowitz Model portfolio
based on the smallest risk preference with Solver
|
Credit
Grade |
Annual Proportion |
Monthly Proportion |
|
A B D E |
0.438705506 0.036744026 0.506758089 0.017792379 |
0.476210072 0.014305177 0.492803934 0.016680817 |
|
∑ |
1.000000000 |
1.00000000 |
Source: Processed data (2023)
Based on Table 12, the Markowitz Model with the
smallest risk preference produces 4 combinations of Credit Grade A, B, C and D
funding assets. The largest fund allocation is in Credit Grade D, amounting to
50.67% annually and 49.28% monthly. The
smallest fund
location is Credit Grade E at 1.78% annually and 1.67% monthly. The combination
and proportion of these fund allocations produces the following expected levels
of return and risk.
Table 13 Formation of returns, risk and portfolio performance in the
Markowitz Model based on the smallest risk preference
|
|
Annual |
Monthly |
|
Return
Portofolio Standard Deviasi Risk Free Sharp
Ratio |
0.2429943 0.0239642 0.06 13.10461 |
0.0202483 0.001164 0.0048676 13.2137474 |
Source: Processed data (2023)
If investors want to invest their funds in 4
combinations of funding assets, the resulting portfolio performance will be
24.29% annually and 2.02% monthly. A portfolio with this combination is a
portfolio which can be used by investors who also consider the risk and
performance of their portfolio (risk taker).
Formation
of an Optimal Portfolio with the Sharpe Ratio
The next portfolio formation is by optimizing
portfolio performance which is measured using the Sharpe Ratio and processed
using Solver in Microsoft Excel. The following are the stock combinations and
their proportions that form the Markowitz Model portfolio based on the optimal
Sharpe Ratio.
Table 14 Combination and
allocation of portfolio funds based on the Markowitz Model Optimal Sharpe
Ratio.
|
Credit Grade |
Annual
Proportion |
Monthly
Proportion |
|
A B D |
0.762505506 0.013944026 0.224458089 |
0.730506055 0.015394626 0.264459808 |
|
∑ |
1.000000000 |
10000000000 |
Source: Processed data (2023)
Based on Table 14, the formation of the Markowitz Model portfolio
using the Sharpe Ratio produces 3 combinations of funding assets. The largest
fund allocation is in Credit Grade A funding assets at 76.25% and the smallest
fund allocation is in Credit Grade B funding assets at 1.39%. A portfolio with
this combination is a portfolio that can be used by investors who also consider
the risk and performance of their portfolio (risk averse). The combination and
proportion of these fund allocations produces the following expected levels of
return and risk.
Table 15 Formation of
returns, risk and portfolio performance for the Markowitz Model based on the
Sharp Rasio
|
|
Annual |
Monthly |
|
Return
Portofolio Standard Deviasi Risk Free Sharp Ratio |
0.1829943 0.0139641 0.06 13.10461 |
0.0152483 0.001164 0.0048676 13.2137474 |
Source: Processed data (2023)
If investors want to invest their funds in the
products listed in Table 15, they are expected to provide an overall return on
investment of 18.29% monthly and 1.53% monthly. This combination is an
efficient portfolio combination based on the preferences of investors who tend
to avoid risk (risk averse)
Conclusion
Investors or lenders need to implement
strategies in investing to avoid potential losses that may occur when investing
their capital, one of the right strategies is to invest funds in funding assets
that have the best performance that can be obtained through optimal portfolio
calculations. The combination of funding assets that have the best performance
is obtained using the Markowitz Model with an optimal Sharpe Ratio, namely
investment in Credit Grade A, B and D funding
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