=Paper=
{{Paper
|id=Vol-2104/paper_161
|storemode=property
|title=Implementation of Robo-Advisors Tools for Different Risk Attitude Investment Decisions
|pdfUrl=https://ceur-ws.org/Vol-2104/paper_161.pdf
|volume=Vol-2104
|authors=Oleksii Ivanov,Oleksandr Snihovyi,Vitaliy Kobets
|dblpUrl=https://dblp.org/rec/conf/icteri/IvanovSK18
}}
==Implementation of Robo-Advisors Tools for Different Risk Attitude Investment Decisions==
https://ceur-ws.org/Vol-2104/paper_161.pdf
Implementation of Robo-Advisors Tools for Different
Risk Attitude Investment Decisions
Oleksii Ivanov1, Oleksandr Snihovyi1, and Vitaliy Kobets1[0000-0002-4386-4103]
1
Kherson State University, 27, 40 Universitetska st. Kherson, 73000, Ukraine
sink2385@gmail.com, snegovoy@hotmail.com, vkobets@kse.org.ua
Abstract. We researched: how to use Machine Learning in the financial indus-
try on an example of Robo-Advisors; defined the basic functionality of Robo-
Advisor; an implementation of Robo-Advisors based on analysis of the most
popular financial services such as Betterment, FutureAdvisor, Motif Investing,
Schwab Intelligent and Wealthfront. We compared their functionality, formu-
lated a list of critical features and described the high-level architecture design of
a general robo-advisor tool. Using Markowitz model we prepared a proof of
concept of a robo-advisor application for investors with different attitudes to
risks. Results of our investigation proposed data processing automatization
from open sources of cryptocurrencies as the top trend nowadays.
Keywords: robo-advisor, Markowitz model, financial instruments.
1 Introduction
Intelligent data analysis is one of the areas of artificial intelligence, which solves the
problem of learning automatic systems without their explicit programming, focuses
on developing algorithms that are self-learning based on the proposed data [1].
Financial corporations that need to adapt quickly to the environment have realized
that it is more efficient to develop self-learning systems that manually improve exist-
ing systems as needed. It saves the resources of the company and optimizes the pro-
cess of developing a financial software product.
However, according to a Bloomberg survey in 2017 in New York, only 16% of
firms have introduced Machine Learning into their investment strategies and software
[2].
The purpose of this paper is to review the financial software that uses Machine
Learning to consider the working principle and formulate the main functionality of
the regular Robo Advisor as software for managing investment portfolios [2, 3], and
to implement a proof of concept of the robo advising algorithm.
The paper has the following structure: it consists of 4 main parts. Part 2 examines
the main ways of using data mining in the financial sector, especially in the concept
of constant living income. Part 3 examines the functionality and capabilities of exist-
ing tools and formulates a list of main features. Part 4 includes experiment of robo-
advisor application for investors with different attitudes to risks. The last part is the
conclusion, which summed up the results of the research.
2 Machine Learning role in the financial industry
The ability of computer programs to learn and improve themselves has become a
conventional technology continuously growing in all industries. Large companies like
Google, Facebook, Amazon, use Machine Learning (ML) to improve performance,
user experience, and data security. In the financial industry, the following areas were
affected by ML [4]:
• Fraud Prevention;
• Risk Management;
• Customer Service;
• Virtual Assistant;
• Network Security;
• Algorithmic Trading;
• Investment Portfolio Management.
All of these areas combine such a process as forecasting. Also, they all carry a vast
array of data that can be combined to create a detailed view [4]. It is the primary
component of ML. Having an extensive multi-layered data where each layer affects
others the goal is to find a pattern and to forecast the next values, or based on found
values provide the most profitable solution. Also, it is not the only one advantage,
because the ML’s knowledge base always increases, the later forecasts will be much
accurate than were in the beginning. Let’s consider some examples of situations and
possible scenarios of using ML in FinTech. For example, Virtual Assistant (VA) is
an integral part of any high-quality product, especially financial software like online
banking. VA can save bank’s money and minimize the cost of real assistance. How-
ever, even it cannot resolve all issues and acts as the “first barrier” between the cus-
tomer and the real assistant who participates in case of VA's impossibility to resolve
the issue. So, what kind of issues the VA can solve? For example, instruction about
how to open a deposit in a bank, help with closing an account, actual offers. The main
“trump card” of the virtual assistant is not only easy access to all information in the
bank, and it is personalization to the customer, but also training on customer's actions.
In a case of regular money receipt to a customer’s account and positive account bal-
ance after all withdrawals; well-trained VA can propose a profitable type of deposit.
Also if the bank has an assignment in partnership with MasterCard (or any other
company), VA may offer to all owners of MasterCard cards some unique bonus.
However, if the customer regularly rejects the same bonuses in the past, VA can mark
such customer as not a part of the target audience (but of course he can find all infor-
mation about bonuses by himself). Another excellent example of using intelligent
data analysis is algorithmic trading – a method of executing a large order using a pro-
grammed algorithm based on trading instructions. Usually, to succeed such software
should have a big dataset with all values even those which affect the main one (for
example, goods prices, the costs of raw materials, the costs for creating and sale) for
an extended period. Having so much information to learn, the ML algorithm can fore-
cast numbers, and traders will know either they need to buy or to sell or to wait.
However, constant living income (CLI) can be the most common usage of ML in
the financial industry. It is a type of income that does not depend on daily activities.
(e.g., investment, ownership or deposits). CLI combined all ML areas used in finan-
cial industry. ML can automate the process of getting CLI through offering new types
of income, different forecasts, and metrics) and this process will be improved contin-
uously.
3 Robo-Advisors as financial software
A good example of financial software for making passive income and managing a
financial investment portfolio is Robo Advisor (RA). Now, this software is common,
but until 2008, this term did not even exist [2, 3].
RA is a set of algorithms, which calibrates investment portfolio based on custom-
er's goals and risks. The customer enters his goal, age, current income and financial
assets. For example, 30 years old man with a salary of $120 000 per year has accumu-
lated $100 000, and he wants to retire at the age of 50 with $10 000 000 savings. The
system begins to offer the expansion of investment between classes of assets and fi-
nancial instruments to achieve customer’s goals. Also, it calibrates the expansion
based on changes to the customer’s goals and market changes in real time. So, Robo-
Advisor always tries to find what is most closely related to the goals of the client [5,
6].
Unfortunately, RAs algorithms are unknown to the public because they are a com-
mercial secret. However, there are few techniques what they can use [7].
Firstly, it can be Modern Portfolio Theory (MTP) as a theory of optimizing or
maximizing expected return by risk-averse investors based on a given level of market
risk. The algorithm of portfolio construction could use MTP if the customer is a risk-
averse person [4]:
=∑ ( ), (1)
where is a return of the portfolio, is a return on asset and is the weighting of
a component asset (that is the proportion of asset “i” in the portfolio).
Also, RAs can use Fisher equation to show the customer’s real interest rate under
inflation [8]:
≈ + , (2)
where is the real interest rate, is the nominal interest rate and is the inflation rate.
However, the Black-Litterman model also can help to optimize the portfolio, and
also can be used in RAs [3]. E. g. Betterment and Wealthfront use this model to pre-
dict the expected rates of return, but Schwab Intelligent uses completely different
approach [9].
= ( Σ) + Ω ( Σ) Π + Ω , (3)
where is a scaling factor, Σ is a yield covariance matrix of instrument ( × ma-
( × matrix). is a diagonal covariance matrix of standard forecast errors that is
trix). is the is the assets identifying matrix that is the subject of investor’s forecasts
reflecting forecasts uncertainty ( × matrix). is the expected equilibrium return
vector ( × 1 vector-column). is the forecast’s vector ( × 1 vector-column). is
investor’s forecasts number and is the assets number in the portfolio. Of course,
there might be completely different formulas, especially because of using them with
ML. There are many of RAs, but only 5 of them were chosen, as the most popular, to
review and to define the main functionality. Bellow the comparison of some features
is shown in the following table 1.
Table 1. Ras features comparison
Feature Betterment FutureAdvi- Motif In- Schwab Wealthfront
sor vesting Inteli-
gent
The user can + + + + +
create own
account.
Two-factor + (sms - - - -
authentica- only)
tion
Portfolio + + - + +
rebalancing
Advice + (Human) + (Automat- + (Auto- + (Hu- + (Auto-
ed) mated) ma) mated)
Customer + + + + +
Service
Mutual funds + + - - +
Fees Digital - 0.50%/year $9.95/trade 0.28%; 0.25%, but
0.25%/year $900 first
; Premium quarterly $10,000 is
– cap free
0.40%/year
Retirement + + + + +
Planning
Automated + + + + +
Investments
Betterment is the one of the oldest RA (dashboard screen is shown in the following
fig.2). The company has developed reliable software to help novice investors. The
user should set how much they plans to invest into ETFs (Exchange Trade Fund, the
investment fund), and how much into ETFs bonds. There is no minimum deposit to
open an account. One commission is charged in the range of 0.15% to 0.35% based
on the balance of the account. RA has easy-to-use tools which help investors decide
on the distribution of stocks, bonds and other financial instruments as cryptocurren-
cies [7, 10].
Fig. 1. Betterment dashboard screen [11].
FutureAdvisor (dashboard screen is shown in the following fig. 2) is the RA that
works with Fidelity, an American holding company, one of the largest asset manage-
ment companies in the world, and TD Ameritrade, an American company that set up
an electronic trading platform. This RA offers a reliable investment evaluation tool.
Users can associate existing investment account in the system for free. It assesses the
investments feasibility based on productivity, diversification, commissions, and taxes.
Also, this product may provide guidance on changing the investor’s assets distribution
[12].
Fig. 2. FutureAdvisor dashboard screen [13].
Motif Investing is a product for active traders which allows users to create stock
baskets and ETFs (dashboard screen is shown in the following fig. 3). After the creat-
ing, the user can buy up to 30 stocks of ETFs for $9.95. Investors can create their
baskets; invest money in other ones which were created by the service itself or in
those created by other users [14, 15].
Fig. 3. Motif Investing dashboard screen [16].
Schwab Intelligent is one of the best RA according to NerdWallet’s review [17] at
the beginning of 2018. It offers advisory service with automated portfolio manage-
ment and unlimited access to certified financial planners including personal financial
guidance (dashboard screen is shown in the following fig. 4). However, generated
portfolios have high allocation in cash (means a part of a customer’s money remains
not invested permanently) and investors must be comfortable with it.
Fig. 4. Schwab Intelligent Portfolios dashboard [18].
Wealthfront (dashboard is shown in the following fig. 5) is a crucial force in the
online advisor industry what offers one of the most robust tax-optimization services
available with no human advice offering at all (strict robo-advisor as the opposite to
the Betterment) [19].
Fig. 5. Wealthfront dashboard [20].
Based on the selected RAs the following functionality can be defined as the basic
[10, 12, 15]:
• Account creation and goals setup;
• Personal data analysis;
• Recommendations for investing and distributing assets;
• Communications between users for mutual investments;
• Active trading and investing in ETFs, stocks, bonds;
• User’s data protection;
• Portfolio rebalancing;
• Retirement planning.
As described above the basic functionality is quite suitable for ML due to their perfect
matching to the areas of usage. RAs even should include all possible ML use cases for
Financial Industry. This fact makes RA one of the most difficult systems from devel-
opment perspective. Based on the described functionality we can define the high-level
architecture design for a general RA in the following fig. 6.
Fig. 6. High-level architecture design for a general RA.
Data Source is either stocks data, cryptocurrencies statistics etc. (means it is what
RA will use to build investment plan). Parser module should do ETL (Extract-
Transfor-Load) work and process valid and appropriate data to the Parse Data or run
Clean Up module to remove invalid records. Investment Plan Module will build and
keep updated users investments plans. Calculation module will do all required calcu-
lations for the Investment Plan Module. Security Module is a hub between the In-
vestment Plan Module, Front-end module and Users Data storage. It is necessary to
keep users data safe and visible only to them. Front-end module is a UI (user inter-
face) of RA application (web and/or mobile client). Users Data is a storage of person-
al users data and their investments plans.
4 Robo-Advisor in Action: Experimental Results
The purpose of an investor is to increase its capital through the formation of a set of
financial instruments (investment portfolio) [21, 22]. Portfolio value is formed as the
P, then through the time interval t the profitability of the portfolio will be
total value of all components of financial instruments. If the value of the portfolio is
$% $
$
x' is a share of capital spent on the purchase of a financial instrument i; d' is a return
. Let’s
of the financial instrument per 1 invested dollar. Then the return on investment port-
folio will be:
,
* = +* (4)
-
ical expectation m/ and the variance σ = r/ respectively, where:
The profitability and risk of the investment portfolio is measured by the mathemat-
,
2 = 3 ∙ (* )+. . . +3, ∙ (*, ) = + 3 ∙ 2 (5)
-
, ,
= + + 3 ∙ 36 ∙ 7 6 (6)
- 6-
where v'9 is the covariance of financial instruments. Since the returns of financial
instruments are random, then the return of the portfolio is also a random variable.
Consider the initial data of quotations of the two most popular cryptocurrencies
(https://finance.yahoo.com/cryptocurrencies) (table 2):
Table 2. A fragment of quotation data of cryptocurrencies prices (in the US dollars) (from
26.01.2018 to 19.02.2018)
Day Bitcoin Etherium
26.01.2018 11459,71 1109,09
27.01.2018 11767,74 1231,58
28.01.2018 11233,95 1169,96
29.01.2018 10107,26 1063,75
30.01.2018 10226,86 1111,31
31.01.2018 9114,72 1026,19
01.02.2018 8870,82 917,47
02.02.2018 9251,27 970,87
03.02.2018 8218,05 827,59
04.02.2018 6937,08 695,08
05.02.2018 7701,25 758,01
06.02.2018 7592,72 751,81
$% $
$
Determine the returns of financial instruments using the formula (table 3).
Table 3. A Fragment of return on cryptocurrencies (coefficients) (from 26.01.2018 till
19.02.2018)
Day Bitcoin Etherium
26.01.2018 0,032016 0,057707
27.01.2018 0,026879 0,110442
28.01.2018 -0,04536 -0,05003
29.01.2018 -0,10029 -0,09078
30.01.2018 0,011833 0,04471
31.01.2018 -0,10875 -0,07659
01.02.2018 -0,02676 -0,10595
02.02.2018 0,042888 0,058204
03.02.2018 -0,11168 -0,14758
04.02.2018 -0,15587 -0,16012
05.02.2018 0,110157 0,090536
06.02.2018 -0,01409 -0,00818
d = 0.0033, d = −0.0038. The average rate of return for bitcoin and etheruim
The average rate of return for Bitcoin and Etheruim equal correspondingly
equal correspondingly d = 0.0033, d = −0.0038.
, ,
A = + + 3 ∙ 36 ∙ 7 6 → 2 C,
?
? - 6-
? ,
+3 ∙ * = 2 ,
@
(7)
?
-
,
?
? + 3 = 1 , 3 ≥ 0.
> -
The initial distribution of the financial instruments will be set at the level x =
xF = 0.5. The objective function in Markowitz model (7) is the quadratic form
r/ = X I VX, where X I is the transposed matrix, V is the covariance matrix calculated
according to the data of table 3:
0.00545 0.00499
K=L Q
0.00499 0.00576
(8)
Adverse risk investor can achieve a certain level of return under minimal risk (7)
88% of the funds he/she needs to invest in Bitcoin and 12% in Etherium.
If investors are risk seeking and strive to maximize their returns under acceptable
risk level (9), then all 100% of their investment fund they have to invest in Bitcoin.
,
A+ 3 ∙ * = 2 → 2R3,
?
?-
? , ,
= + + 3 ∙ 36 ∙ 7 6 ,
@
(9)
- 6-
? ,
?
? + 3 = 1 , 3 ≥ 0.
> -
Over the time of investment portfolio formation using robo-advisor, these findings
for each investor will significantly depend on the availability of alternative financial
instruments and the volatility of their rates.
5 Conclusion
Machine Learning shows new ways how to develop various areas of the financial
industry. It also can give a new life to old tools which help companies and individuals
to invest, to trade and more using Robo Advisors. Even if it is not a young idea, it is
still developing the financial industry. For example, it can: use personal data to pre-
vent fraud (such as accounts duplicates, or a pre-arrangement for investing); does
investment and asset allocation guidelines which is the ideal task for ML because of a
significant amount of data to process and analyze. Despite the fact that RAs are often
criticized [23] they make investments easier and provide new tools that can radically
change an investment landscape. We implemented a proof of concept of the robo-
advisor application for investment portfolio formation with financial instruments un-
der adverse risk attitude and risk-seeking behavior of investors.
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