Cryptocurrency prices are highly variable. Predicting changes in cryptocurrency price is a hugely important topic to investors and researchers, with much existing research on demand-side factors. The goal of this research project is to design and implement machine learning models to predict future cryptocurrency price change direction based primarily on supply-side factors. Different unsupervised machine learning techniques are used to build the predictive models. These techniques include K Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayesian Classifier, and Random Forest Classifier. A dataset of 10 daily supply-side metrics for three prominent cryptocurrencies (Bitcoin, Ethereum, and Litecoin) at four different time horizons (ranging from one day to 30 days) are used to build and test the machine learning models. The outputs of these models indicate the predicted direction of the price movement over the time horizon (i.e., whether the price would go up or down), not the magnitude of the movement. Experimental results show that predictions were very unreliable for the shorter time spans but very reliable for the longest time spans. The Artificial Neural Network and Random Forest classifiers consistently outperformed the other techniques and achieved a prediction accuracy of over 90% in most models and over 95% in the best models. Experimental results show also that there is no significant difference in predictability between the three prominent cryptocurrencies.
This study examined the relationship between cryptocurrency shocks and exchange rate behaviour in Nigeria. Selected cryptocurrencies for the study are Bitcoin, Ethereum, Litecoin, Ripple and Binance coin which are the most traded cryptocurrencies in Nigeria. Augmented Dickey-Fuller (ADF), Johansen Cointegration and Vector Autoregressive (VAR) tests were used to analyze the monthly data of exchange rate and selected cryptocurrencies for four years (45 months). The result of the cointegration test revealed the existence of a long-run relationship among the variables. ECM result showed that about 6% of the short-run disequilibrium are being corrected and integrated into the long-run equilibrium relationship. In addition, the Variance Decomposition result showed that Ripple has the highest variations to exchange rate in the short and long runs. The present value of exchange rate adjusts slightly to changes in cryptocurrency. Ripple and Bitcoin have the highest shocks on the exchange rate. Therefore, monetary authorities should give adequate attention to cryptocurrency transactions and make policy decisions on how to reduce the prevailing high exchange rate in Nigeria by integrating crypto transactions in their systems. Transaction in cryptocurrency is still at the early stage, especially in Nigeria; only five years data can be gotten on commonly traded cryptocurrencies in Nigeria. This is a limitation to the study in terms of the number of cryptocurrencies used in the study. More cryptocurrencies can be included in future studies.
Purpose This paper aims to examine the predictive power of the volume of Economic Uncertainty Related Queries and the Macroeconomic Uncertainty Index on the Bitcoin returns. Design/methodology/approach Data consists of 118 monthly observations from September 2010 to June 2020. Due to the departure of series from Gaussian distribution and the existence of outliers, the authors use the quantile analysis framework to investigate the persistency of the shocks, the long-run relationships and Granger causality among the variables. Findings This research provides several important findings. First, the substantial differences between conventional and quantile test results stress the importance of the method selection. Second, throughout the conditional distribution of the series, stochastic properties of the variables, long-run and the causal relationships between the variables might be significantly different. Third, rich information provided by the quantile framework might help the investors design better investment strategies. Originality/value This study differs from the previous research in terms of variable selection and econometric methodology. Therefore, it presents a more comprehensive framework that suggests implications for empirical researchers and Bitcoin investors.
Purpose Bitcoin has emerged as a phenomenal asset earning abnormal profits. However, the factors with predictability power over its price are not widely studied. Therefore, this study aims to explore the factors that determine bitcoin prices. The analysis explores the determinants belonging to four categories – macro economic, financial, technical and fundamental factors. Design/methodology/approach The study employs random effects regression on the panel data of five countries. Then Granger causality test is applied on the time series of all the variables. Lastly, diagnostic tests are conducted to confirm the findings to be robust and reliable. Findings The findings suggest that oil price, bitcoin supply, trading volume and market capitalization significantly impact the price of bitcoin in the long run. In short run, bitcoin returns are only caused by oil price and market capitalization. Interestingly, bitcoin returns influence its attractiveness to investors, market capitalization, S&P 500 returns and trading volume, in the short run. Practical implications The technical analysis is found to be redundant in the short run. In the long run, technical as well as fundamental analysis are useful. The bitcoin is found to be a good diversification tool as it has no linkages with the stock markets and gold market. It is also an inflationary hedger owing its limited supply. Originality/value The studies on cryptocurrency market have not conducted the analysis across countries. This study captures the cross-sectional effects along with time effects. The study also includes 17 variables belonging to four categories.
In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel quantile-on-quantile approach to uncover the dependence between the news-based index associated with coronavirus outbreak and Bitcoin and gold returns. Our results reveal that the daily levels of positive and negative shocks in indices induced by pandemic news asymmetrically affect the Bearish and Bullish on Bitcoin and gold, and fear sentiment induced by coronavirus-related news plays a major role in driving the values of Bitcoin and gold more than other indices. We find that both commodities, Bitcoin and gold, can serve as a hedge against pandemic-related news. In general, the COVID-19 pandemic-related news encourages people to invest in gold and Bitcoin.
A. Thavaneswaran, You Liang, Sulalitha Bowala, Alex Paseka · 5 authors
Recently there has been a growing interest in applying neural network modelling from natural language processing to financial time series prediction problems in computational finance. Cryptocurrency price prediction is a challenging problem with non-stationary market price and volatility clustering. Cryp-tocurrency data tends to be non-stationary, which means that predictive information extracted using deep learning techniques on observed data can not be used with future data. Moreover, there is a very little signal in cryptocurrency data to indicate the future direction of the market. This paper proposes a sensible way to frame the prediction problem as a dynamic regression problem by defining the features in the feedforward neural networks and the target as an appropriate average of the historical data. The novelty of this paper is to use deep learning algorithms and statistical bootstrapping to obtain cryptocurrency price prediction and the corresponding prediction intervals. It is shown that neural networks are capable of modelling nonlinearity directly for nonlinear time series models. The proposed hybrid approach is evaluated using simulated and cryptocurrency data through numerical experiments. Moreover, Gaussian and boot-strap prediction intervals for the price and the volatility of the prediction errors, are also discussed in some detail.
This paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoin’s daily return’s volatility from June 27, 2013 to November 7, 2019 with an econophysics perspective, so as to avoid the forecasting model misspecification. Firstly, this paper studies the multifractal and chaotic nonlinear characteristics of Bitcoin volatility by using multifractal detrended fluctuation analysis (MFDFA) and largest Lyapunov exponent (LLE) methods. Then, from the perspective of nonlinearity, the measured values of multifractal and chaos show that the volatility of Bitcoin has short-term predictability. The study of chaos and multifractal dynamics in nonlinear systems is very important in terms of their predictability. The chaos signals may have short-term predictability, while multifractals and self-similarity can increase the likelihood of accurately predicting future sequences of these signals. Finally, we constructed a number of chaotic artificial neural network models to forecast the Bitcoin return’s volatility avoiding the model misspecification. The results show that chaotic artificial neural network models have good prediction effect by comparing these models with the existing Artificial Neural Network (ANN) models. This is because the chaotic artificial neural network models can extract hidden patterns and accurately model time series from potential signals, while the benchmark ANN models are based on Gaussian kernel local approximation of non-stationary signals, so they cannot approach the global model with chaotic characteristics. At the same time, the multifractal parameters are further mined to obtain more market information to guide financial practice. These above findings matter for investors (especially for investors in quantitative trading) as well as effective supervision of financial institutions by government.
Market traders buy and sell volatile assets frequently, with a goal to maximize their total return. We have been asked to develop a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. The assets that can be traded are Bitcoin and gold. We will start with $1000 on 9/11/2016 and try to maximize the total return until 9/10/2021. We will start from forecasting prices and developing trading strategies. In terms of price prediction, we use MSE and Trend_Acc as indicators, and use XGBoost, a representative strong learning algorithm in traditional machine learning, and LSTM, which is good at time series prediction in deep learning, to fit and forecast the data respectively. At first glance, the curve fitting MSE are satisfactory , but, a closer look reveals that the model either firmly remembers the data of the training set, resulting in a lack of generalization ability for unknown data (XGBoost), or tends to take the previous day's results as the predicted results, resulting in a significant lag in the prediction curve (LSTM), all of which are reflected in the models' poor performance in predicting whether prices will rise or fall in the future. In our view, since a large number and complexity of factors affecting prices, it is unrealistic to predict future prices accurately from past prices alone, unless we can get rid of the limitation of the problem, use additional data to assist the prediction, or use all the data as a training set for fitting, we cannot achieve good results in the prediction, but such behavior is inconsistent with our original intention. We established restricted trading model based on composite index judgment. The model not only applies the traditional economic Relative Strength Index and Stochastics Oscillator Index, but also introduces the K-Lipschitz limitation in deep learning into the model. The model dynamically adjusts each transaction strategy according to the changes of working capital and total assets, purchase cost, selling profit and other factors, and the total income of the model is $132433.1. In the horizontal comparison, the profit of our model is more than 39.6%-283.6% than that of the traditional moving average strategy and RSI-STC strategy, and 51.3% higher than that of the random walk model using Montmarlowe algorithm. In addition, we collected the transaction data of bitcoin and gold from 2012-01-01 to 2022-02-21, and applied the model to the historical data, and received good returs. For example, from 2012-1-1 to 2022-2-21, the return was $8418074.9. It is proved that the model has high generalization performance and strong stability. To test the sensitivity of the model to transaction costs, we also analyze the changes in trading strategies that should occur when fees rise. The analysis shows that the traders' single trading volume decreases first and then increases with the increase of the commission fee. The results of sensitivity analysis show that the return change is less than 3%, which proves the robustness of the model. Finally, we made a memo to summarize our work.
Purpose The aim of the paper is to investigate the risk-hedging and/or safe haven properties of environmental, social and governance (ESG) index during the COVID-19 in China. Design/methodology/approach This paper employs the DCC, VCC, CCC as well as Newey–West estimator regression. Findings The findings provide empirical evidence of the risk hedging properties of ESG indexes as well as of the environmental, social and governance thematic indexes during the outbreak of the COVID-19 crisis. The results also support the superior risk hedging properties of ESG indexes over cryptocurrency. However, the authors do not find any safe haven properties of ESG, Bitcoin, gold and West Texas Intermediate (WTI). Practical implications The paper offers therefore, practical policy implications for asset managers, central bankers and investors suggesting the pandemic risk-hedging opportunities of ESG investments. Originality/value The study represents one of the first empirical contributions examining safe-haven and hedging properties of ESG indexes compared to traditional and innovative safe haven assets, during the eruption of the COVID-19 crisis.
Inzamam Ul Haq, Apichit Maneengam, Supat Chupradit, Chunhui Huo
This article aims to explore the co-movement of daily returns among S&P green bonds (GB/GBs), the top five sustainable cryptocurrencies, Bitcoin, the Dow Jones Sustainability World Index (DJSWI) and the Dow Jones Sustainability Emerging Market Index (DJSEMI) to determine whether GBs, Bitcoin and sustainable cryptocurrencies are truly sustainable; in addition, it investigates hedging and diversification opportunities. Using a partial wavelet coherence framework to capture the bivariate co-movement, our findings show strong (weak) positive co-movements among GB (sustainable cryptocurrencies) and DJSWI returns, where GBs (sustainable cryptocurrencies) have a heterogeneous leading role in the short-term and long-term horizons. Results indicate moderate positive (negative) co-movement among GBs and sustainable cryptocurrencies (Bitcoin) and DJSWI in the short run (long run). Overall, the results show GB (sustainable cryptocurrencies) acts as a diversifier for Bitcoin and sustainable cryptocurrencies in most cases (DJSWI). However, increasing Bitcoin returns adversely impacts the DJSWI in the long run. Findings are equally imperative for green investors, crypto traders and policymakers, where investors and traders can earn financial and social returns, and policy-makers can deploy suitable policies for the development of sustainable cryptocurrency mining processes. The role of Bitcoin is alarming for the United Nations Sustainable Development Goals and global greener economy.
Cryptocurrency as a financial asset has emerged as a fad among investors, academicians and policymakers alike. In a financial purview, this study intends to empirically test the behaviour of the cryptocurrency return, inferring its market efficiency. For this purpose, daily data of five cryptocurrencies (Bitcoin, Ethereum, Litecoin, Tether and Ripple) have been collected from 1 January 2016 to 31 March 2021 to investigate the well-known financial theory of random walk hypothesis for this young market. To provide statistical evidence and ensure the robustness of results, analysis is performed using the variance ratio test, augmented Dickey–Fuller test, Philip–Perron test, Breusch–Godfrey serial correlation LM test and ARIMA model. The statistical results illustrated strong evidence refuting the presence of the random walk hypothesis in this emerging market, thus implying inefficiency in the cryptocurrency market. Furthermore, the absence of random walk in the cryptocurrency makes this financial asset predictable, giving investors an arbitrage edge to earn abnormal gains using trading strategies, which is euphoria.
Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.
This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.
Md Hakim Ali, Chrıstophe Schınckus, Akther Uddin, Saeed Pahlevansharif
Purpose Even though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns. Design/methodology/approach The authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019. Findings First, the authors found a strong evidence of Bitcoin’s mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification. Originality/value The study significantly extends the empirical literature of Bitcoin’s portfolio diversification by taking EPU into consideration. To the best of authors’ knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin’s hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.
The fame of cryptocurrencies soars in 2017 because of a few consecutive months of the exponential development of their market capitalization. Even though machine learning has been fruitful in anticipating stock market costs through a large group of various time series models, its application in foreseeing cryptocurrency costs has been very prohibitive. The reason behind this is clear as the costs of cryptocurrencies rely upon a ton of factors like technological progress, internal competition, pressure on the markets to deliver, economic problems, security issues, political factors and so on Their high volatility prompts the incredible capability of high benefit if savvy designing systems are taken. Sadly, because of their absence of lists, cryptocurrencies are somewhat capricious contrasted with traditional financial predictions like stock market predictions. The proposed paper describes how Cryptocurrency works, its use, legal prospect, security and what is the technology behind it
The aim of this study is to gauge the impact of global economic policy uncertainty and natural resource prices, that is, oil prices and gold prices, on Bitcoin returns by using monthly data spanning from May 2013 to December 2021. The study applies ARDL and nonlinear ARDL for evaluating the symmetric and asymmetric effects of Global Economic Uncertainty (GU), oil price (O), and natural gas price on Bitcoin volatility investigated by using the ARCH-GARCH-ERAGCH and non-granger causality test. ARDL model estimation establishes a long-run cointegration between GU, O, G, and Bitcoin. Moreover, GU and oil price exhibits a negative association with Bitcoin and positive influences running from gold price shock to Bitcoin in the long run. NARDL results ascertain the long-run asymmetric relations between GU, oil price, gold price (G), and Bitcoin return. Furthermore, GU’s asymmetric effect and positive shock in gold price negatively linked to Bitcoin return in the long run, whereas asymmetric shock in oil price and negative shocks in gold price established a positive linkage with Bitcoin. The results of ARCH effects disclose the volatility persistence in the variables. The causality test reveals that the feedback hypothesis explains the causal effects between GU and Bitcoin and unidirectional causality running from Bitcoin to gold price and oil price to Bitcoin.
Cryptocurrencies are growing rapidly, with various altcoin being introduced recently, despite the fact that the market is very volatile, cryptocurrency now holds trillions of dollars in the market and has plenty of platforms for trading and owning cryptocurrencies, like Binance, Coinbase, and others. In particular, Bitcoin has caught the atten-tion of many people over the year with a current market cap. of 731.56 billion dollars circulating in the market. One of the major problems in cryptocurrencies is volatility, and often the prices can vary due to the external events that trigger the market. That is, Twitter sentiment. The objective of the article is to investigate people’s opinion about the cryptocurrency market on social media using collected tweets for 2 popular hashtags of Bitcoin and investigating the tweets using sentiment analysis. The study found that sentiment scores could be related to observed price fluctuations.
Tamara Zuvela, Sara Lazarevic, Sofija Djordjevic, Marko Arsenović · 5 authors
Cryptocurrency is a type of digital or virtual currency that uses cryptography to secure and verify transactions as well as to control the creation of new units, it uses Blockchain properties for the same. Blockchain is a decentralized digital ledger technology that records transactions securely and transparently. Blockchain technology and cryptocurrency are closely connected. Cryptocurrencies rely on blockchain technology to operate, as blockchain serves as the decentralized ledger that records all transactions and ensures their security and transparency. [7] As the internet becomes more accessible and convenient, an increasing number of people and organizations are turning to digital transactions. Digital payment systems are significantly faster, less expensive, and more efficient. As a result, it's not unexpected that innovative digital payment system types are quickly emerging. No other approach even comes close to the colossus that is cryptocurrencies. Predicting cryptocurrency prices can be useful for a variety of reasons. For traders and investors, predicting cryptocurrency prices can help them make informed decisions about when to buy or sell cryptocurrencies, maximizing their profits or minimizing their losses. For prediction, the algorithms used are GRU (gated recurrent unit), LSTM (longshort-term memory), and Bi-LSTM (Bi-directional long-short-term memory) algorithms to predict the future price of a cryptocurrency. An ensemble model is also created using the three models, and prices could be accurately predicted using these models and displaying the obtained results.
Purpose This paper examines the effect of the holy month of Ramadan on the returns and conditional volatility of cryptocurrency markets. Design/methodology/approach The closing prices of six cryptocurrencies have been considered. The study employs different classical tests for checking if the efficiency behaviour is similar during Ramadan celebration days and non-Ramadan days. Besides, dummy variable regression technique for assessing this anomaly on returns and volatilities has been applied. Findings Although no significant effect on returns and volatility for Litecoin has been found, the results provide evidence about the existence of the Ramadan effects in cryptocurrency markets. The results of the mean equations show the existence of Ramadan effect for Ethereum, Ripple, Stellar and BinanceCoin for all considered models. Significant effect on Bitcoin returns is found with an autoregressive model of order 1. The results of conditional volatility show Ramadan effect on volatility is not detected. Originality/value First, a new contribution in the incipient study of cryptocurrency analysis. Second, a comprehensive review of recently published empirical articles about Ramadan effect on traditional assets has been carried out. Third, unlike most of the papers focussed on the study of Bitcoin, this study has been extended to six cryptocurrencies. Ramadan effect have not been analysed in cryptomarkets yet. This study come to fill this gap and analyses Ramadan effect, previously documented for traditional assets, in particular, stock index from Muslim countries, but not yet analysed in the cryptocurrency markets.
Bu çalışmanın amacı Bitcoin’in emtialar için çeşitlendirici rolünün ve emtialarla etkileşiminin incelenmesidir. İnceleme kapsamında Bitcoin, altın, gümüş, emtia endeksi, ham petrol ve enerji emtiaları endeksi değişkenlerinden oluşan 17.09.2014 - 24.11.2021 dönemini kapsayan günlük veri seti Garman-Klass serilerine dönüştürülmüş ve dinamik koşullu korelasyon modelleri uygulanmıştır. Uygulama sonucunda Bitcoin ile emtialar arasındaki etkileşimi test etmek için en uygun modelin cDCC-GARCH olduğu gözlenmiş ve Bitcoin ile emtialar (gümüş hariç) arasındaki etkileşimin negatif yönlü; emtiaların kendi aralarındaki etkileşimin pozitif yönlü olduğu tespit edilmiştir. Bulgular, Bitcoin’in emtialar için (gümüş hariç) diğer emtialara göre daha iyi bir çeşitlendirici olduğunu ve Bitcoin’in emtia bulunduran portföye dahil edildiğinde hedge etme görevi üstlendiğini göstermektedir.
Venture capital led by Bitcoin and gold has become increasingly popular in the past several years, so the research of cryptocurrencies (such as Bitcoin) becomes deeper and deeper. Many researchers have studied the collaborative investment of bitcoin and gold, which is an expective portfolio. In this paper, the authors constructed a systematic model, achieving the combination among prediction, making strategies, solving profits, and evaluation. All the study in this paper is based on the given data and constructed model with accurate references.In this paper, the authors selected the long short-term memory model (LSTM) as the basis, then designed two models called the gold price prediction model (GPPM) and the Bitcoin price prediction model (BPPM) to estimate the price of both gold and Bitcoin, standing as a trader, not a “god economist”. The error analysis shows a good performance of GPPM and BPPM, and it gives the authors confidence to make strategies and calculate final profits (investment worth).Unambiguously, the final goal of this question is to maximize the total assets (profits), so the author set up a single objective optimization model (SOOM) called the trading strategy model (TSM). The total constraint conditions are divided into six directions, including the basic trading conditions, the evaluation of financial risk, and the difference between gold and Bitcoin. Additionally, the costumers with different trading risk tolerance will acquire different assets finally, which indicates that the prudent policy generally can lead to a better result. After calculation, the asset on 2021/9/10 is about 1.59×108 USD, a considerable number.The evaluation of TSM has two parts, one is the disturbance test. This test randomly sets that several days’ trading does not occur, then has a comparison between the original model prices and the prices after disturbance. The result proves that the strategy predicted by TSM is the best strategy. The result of the sensitivity test in section 4 finds the polynomial relationship between the assets and the transaction costs. Under current conditions, the final assets will decrease by 4.2% if the transaction costs of gold increase by 1%, and will increase by 2.1% if the transaction costs of bitcoin increase by 1%.Finally, the authors wrote a memorandum for different customers & traders. We sincerely hope the memorandum can help them in the near future.