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CoinExams: A Fundamentals-Based Market Liquidity System

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Abstract and Figures

Modern market liquidity is mostly being provided based on two asset pairs. This can be improved by adding additional pairs in a mixed basket of assets to lower the risk. However, without considering fundamental factors automated systems are left to technical indicators or speculative sentiment analysis. This would mean an asset that is worthless can be seen as an attractive buy, e.g. at a low relative strength index (RSI) or when influencers are marketing it on social media. This can be mitigated by dynamically assigning relative valuation scores to each asset based on their fundamentals, e.g. asset utility. Assets can then be traded based on these scores to provide liquidity regardless of market conditions, while maintaining a relative lower risk. This system can increase asset volumes vs. a static portfolio. In some examples, providing liquidity using this system increased total volume of assets by up to 12.2% monthly in one case, while the average monthly increase was about 6.2% in the volume of assets and 3.3% in total value of assets, for portfolios considered in this study.
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CoinExams: A Fundamentals-Based Market Liquidity System
Prince Hasn1
hasn@coinexams.com
coinexams.com
Abstract . Modern market liquidity is mostly being provided based on
two asset pairs. This can be improved by adding additional pairs in a
mixed basket of assets to lower the risk. However, without considering
fundamental factors automated systems are left to technical indicators
or speculative sentiment analysis. This would mean an asset that is
worthless can be seen as an attractive buy, e.g. at a low relative strength
index (RSI) or when influencers are marketing it on social media. This can
be mitigated by dynamically assigning relative valuation scores to each
asset based on their fundamentals, e.g. asset utility. Assets can then be
traded based on these scores to provide liquidity regardless of market
conditions, while maintaining a relative lower risk. This system can
increase asset volumes vs. a static portfolio. In some examples, providing
liquidity using this system increased total volume of assets by up to
12.2% monthly in one case, while the average monthly increase was
about 6.2% in the volume of assets and 3.3% in total value of assets, for
portfolios considered in this study.
Key w ords: market liquidity, asset management, investment strategy
1 Introduction
The most comparable systems to the studied fundamentals-based market liquidity system
are automatic rebalancing systems utilising automated algorithms, mostly based on pre-
defined allocation percentages against a fiat currency value. This led many researches to
focus on either the best allocation percentages [1] or the best rebalancing frequency [2].
Some managed to recognise the importance of dynamically adjusting the allocation
percentage [3]. Those methods however might not be best suited for long-term investing
as they are not based on fundamentals.
In order, to capture market changes and asset fundamentals multiple factors have to be
considered. The system studied in this paper solves for this by utilising a reference ranking
score to guide the trading strategy. The methodology can thus mitigate potential incorrect
market sentiment or wide spread lack of awareness about the true value of assets.
1 Crypto researcher and full-stack developer at CoinExams
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2 Scope
This paper focuses on results of using this system on a selected number of crypto assets
within a portfolio guided by Binance's crypto exchange limitations. These limitations
include the minimum trade being limited to the equivalent of USD 10 in most cases. The
trading fee which is a percentage of the total executed trade value is also considered. This
percentage is about 0.075% for most verified accounts.
This study is limited to six (6) coins all having a limited maximum supply of coins. The
crypto assets/coins selected for the study are Bitcoin (BTC), Algorand (ALGO), IoTex (IOTX),
BNB Coin (BNB), Nano (XNO), and Cardano (ADA). Bitcoin is selected for being the largest in
market cap [4] and the rest of coins are selected based on the expectation they could be
best in terms of utility within the near future [5]. All of these assets have an estimate of
more than 0.5 coin transactions per second, which is relatively high compared to other
crypto coins [6]. This combination also ensures a good mix between three (3) coins with
large market caps and another three (3) with smaller market caps.
The study utilises CoinExams' application, which uses the system to provide liquidity to the
market and benefit from market volatility [7]. This is done while benefiting from the
assurance of holding quality crypto assets, otherwise referred to as HODLing. The study if
focused on the simulation feature within the application which allows back-testing the
system using historical market data. Although the application allows back-testing as far
back as 24 months, this study will focus on the periods covering the past 12 months. The
trading activities simulated in this study are based on a single evaluation everyday at 00:00
GMT. Actual trading frequency within the application can be set anywhere between 6
minutes and 6 hours. The studied portfolios all have a value of EUR 1000 at the end of the
period to have them comparable for this study.
3 Methodology
Fundamentals-based Market Liquidity System
Ranking scores are first given to each asset within the portfolio based on a set of metrics
representing asset fundamentals. These scores are then added to a market cap factor
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calculated for each asset. Additional factors such as all-time high prices are also considered
for better estimations of included assets' relative value.
Resulting ranking scores are summed up to create a total score. The total portfolio value is
then divided by the total score and later multiplied by the score of each asset to obtain
target allocation of each asset. Defined target allocations help determine if any asset
allocation is to be increased or reduced based on how it compares to its current existing
allocation.
An example on assets ranking based on asset fundamental metrics is the ranking list
maintained by on CoinExams crypto assets utility ranking list [6]. The list calculates a rank
for crypto coins based on a set of listed consistent metrics that can give an indication to the
utility of a certain crypto asset, e.g. transaction fees and speed. The platform goes a step
further by applying this methodology to identify the most undervalued coins within a list of
selected coins [7]. This could then be used with a linked exchange account to provide
liquidity to the crypto market around the clock based on user settings and coins selections.
4 Results
This section presents results of utilising the system based on simulations offered by
CoinExams' application. The results are presented in the form of charts showing the
historical market value of selected assets if traded using the system, against the value of
the same assets if held without trading.
Simulations tested cover the six (6) identified coins in the scope together and in various
mixed combinations of three (3) assets per portfolio simulation. The total simulation
combinations are 16 in addition to one with the six (6) assets together.
The total portfolio value is set to be EUR 1000 at the ending date of all simulations, the 5th
of June 2022, to have them comparable in value changes. Trades are then conducted
backwards by the system, based on the historical market data.
Simulations identify gains in the volume of assets, as well as gains in the total portfolio
value compared to the same starting volume of assets not being offered for liquidity
(traded) through the system. Simulated portfolios use the default asset distribution which
varies based on valuations of assets, where relatively undervalued assets are assigned
larger allocations.
All simulations achieved positive gains in the total portfolio value and five (5) simulations
had positive changes in volumes of all assets. The average monthly return was found to be
about 3.3% on total assets value and the average increase in assets volume was about 6.2%
monthly.
The best overall returns of the study was from the portfolio comprising IOTX, BNB, and
XNO. The portfolio, which is shown in the following chart, had an overall return on the
portfolio value of about 71% translating to about 4.6% month-over-month (MoM) return.
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Although the amount of IOTX in the above portfolio was reduced, the amount of XNO has
increased significantly. This increase can be attributed to the uncorrelated strong
movement of IOTX price relative to both BNB and XNO, as shown in the following price
comparison charts [8,9], which allows the system to benefit from providing liquidity to
buyers in the market at relatively favorable values.
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Overall results of all portfolios showed the system can achieve positive results on all fronts,
as shown in the following portfolio comprised of ALGO, IOTX, and BNB.
Four (4) other portfolios got similar results, all of which had IOTX. Those are: (BTC, ALGO,
IOTX), (IOTX, BNB, ADA), (BTC, IOTX, ADA), and (BTC, IOTX, BNB), in order of performance
from best to worse. The following is the chart representing the worse performer out of the
mentioned four (4) portfolios.
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The study's worse performer shows how within a given limited trading period, coins which
have fallen significantly in market value tend to be purchased more often. It is only when
those coins increase in value the system trades them for other coins in the portfolio. This is
confirmed by examining the following chart of that portfolio.
BTC relative resilience allowed the portfolio to buy into less stable assets mainly ADA.
Although the total value gain above a holding portfolio was only 23% or about 1.7%
monthly, the gain in the volume of ADA coins was 70%. Despite this significant gain in the
volume of some assets the performance of this portfolio pales in comparison to other
portfolios featured earlier, such as the one mentioned at the beginning of this section
comprising IOTX, BNB, and XNO. This could be attributed to the correlation between
changes in the value of BTC, ALGO and ADA coins, which can be seen from the following
price comparison charts [10,11].
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5 Conclusion
Results of this study show consistent overall positive gains for studied portfolios. This is
most likely because assets selected for the study have excellent utility. The average
monthly return was about 3.3% on total assets value and about 6.2% on assets amounts.
This means in a “bull” market the average monthly return on total value has the potential
to be even higher.
Volatility level is another factor impacting results for selected assets, but even more
important is the volatility correlation. It is clear from results, periods covering higher asset
volatility tend to produce better overall results compared to periods covering the
movement in relatively a single direction for one or more assets. Assets which usually have
correlated market moves are less likely to be traded when utilising this system and are thus
expected to produce less gains. Portfolios comprised of assets with less correlated market
values tend to perform better. The system might also be best suited for assets with limited
supply to reduce the risk of an asset inflation affecting future market value and not limiting
recovery. This might be a helpful way to identify assets suitable for this system, although
past performance is hardly a concrete guide.
In order to investigate this system further future investigations could explore different
periods afforded by the CoinExams' application currently ranging from 3 months to 2
years. Other simulations could be done following the same methodology to investigate the
system effectiveness in providing market liquidity for crypto assets as well as other assets
such as stocks.
References
[1] Harjoto, M. A., & Jones, F. J. (2006). Rebalancing strategy for stocks and bonds asset
allocation. The Journal of Wealth Management, 9(1), 37-44.
[2] Almadi, H., Rapach, D. E., & Suri, A. (2014). Return predictability and dynamic asset
allocation: How often should investors rebalance?. The Journal of Portfolio Management,
40(4), 16-27.
[3] Yu, J. R., Chiou, W. P., Hung, C. H., Dong, W. K., & Chang, Y. H. (2022). Dynamic
rebalancing portfolio models with analyses of investor sentiment. International Review of
Economics & Finance, 77, 1-13.
[4] Cryptocurrency Prices, Charts And Market Capitalizations | CoinMarketCap. Retrieved
June 5, 2022, from https://coinmarketcap.com
[5] CoinExams | crypto coins future. Retrieved June 5, 2022, from
https://coinexams.com/future
[6] CoinExams | crypto utility review. Retrieved February 1, 2022, from
https://coinexams.com/review
[7] CoinExams | crypto portfolio builder. Retrieved February 1, 2022, from
https://coinexams.com/app
[8] BNB vs IoTeX utility comparison | CoinExams. Retrieved June 7, 2022, from
https://coinexams.com/compare/binance-coin-vs-iotex
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[9] Nano vs IoTeX utility comparison | CoinExams. Retrieved June 7, 2022, from
https://coinexams.com/compare/nano-vs-iotex
[10] Algorand vs Bitcoin utility comparison | CoinExams. Retrieved June 7, 2022, from
https://coinexams.com/compare/algorand-vs-bitcoin
[11] Cardano vs Bitcoin utility comparison | CoinExams. Retrieved June 7, 2022, from
https://coinexams.com/compare/cardano-vs-bitcoin
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ResearchGate has not been able to resolve any citations for this publication.
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