I Made Gede Abandi Semeru, Yunieta Anny Nainggolan
Abstract : In forming their portfolios, investors should analyze the risk and return of each investment instrument. This is aimed at preventing investors from speculating and gambling with their investments. Conducting an investment portfolio optimization study on LQ-45 stock index, government bond, USD, gold, and Bitcoin can provide valuable insights due to unique market characteristics in Indonesia. This research analyzes the formation of investment instruments over the last 60 months, specifically from January 2018 to December 2022. The research method used in this study is quantitative research aimed at selecting several investment instruments for a portfolio in Indonesia. The portfolio aims to minimize risk and maximize return using the Markowitz method, also known as the optimal portfolio. To fulfill the objectives of this research, data on the prices of each instrument are required. An optimal portfolio can be obtained by combining two instruments: 18% bitcoin and 82% gold. This optimal portfolio can achieve an expected return of 1.29% with a risk level of 5.15%. Considering a risk-free rate of 0.375%, this portfolio forms a slope of 0.1775, which is the largest slope formed between the combination of risk-free instruments and risky portfolios. Investors should allocate their funds more wisely, considering not only the highest return but also the associated risk. High returns often come with high risks, so investors need to assess the risk-return trade-off before making investment decisions.
Bitcoin is a newly created currency, which is also considered as “Digital Gold.” Whereas, Gold is a precious yellow metal. Bitcoins are more independent of the government than gold. Both gold and bitcoins are scanty resources, and thus, prices of both these assets appreciate or deteriorate depending on the demand and supply. An attempt was made to examine the association between prices of these two currencies. For this purpose, three different models were run: independent sample t-test, correlation analysis, and regression analysis. The outcomes of the independent sample t-test revealed that there exists a significant difference between the gold and bitcoin prices. However, the findings of correlation analysis show that movements in gold prices are statistically and positively linked to bitcoin prices. These findings indicate that gold prices move in the same direction as bitcoin prices. Further, the results of regression analysis also depicted that movement of bitcoin prices depends on movement of gold prices. Since its genesis, bitcoin prices have experienced around 37,418 appreciations, which make it an extraordinary currency. However, this study argued that bitcoin is establishing itself as an investment asset for the short term only because fluctuation in prices is very abnormal and unreliable in the long run. Moreover, robustness checks further show that gold prices are increasing at a steady rate, but increments are regular and trustworthy. Finally, the study found that bitcoin provides much higher returns to investors than gold. These results are crucial for the risk-taker investors who are looking for higher returns because bitcoins are getting growing public exposure day by day and attracting investments throughout the world.
Cryptocurrency has become a popular asset in global financial markets, meaning that individual investors and asset management companies worldwide are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). Since variations on the deep learning model parameter values may also introduce variability in the results produced by the models, different statistical validations were considered part of the comparison process. Finally, this work shows that the Temporal Convolutional Network model (TCN) outperformed other architectures considered for a short-term forecast period in terms of processing time. The TCN deep learning model is also amongst the most accurate models, using an auto-regressive integrated moving average model (ARIMA) as a baseline.
I.sibel KERVANCI, Mehmet Fatih Akay, Eren Özceylan
Bitcoin has high price fluctuations, which involve high risks and high return rates for investors. These high earnings have attracted the attention of investors. This paper proposes a new model for Bitcoin price prediction that effectively reduces prediction error. Hyperparameter optimization methods such as Bayesian optimization (BO), random search and grid search with Long Short-Term Memory (LSTM), Gated Repetitive Unit (GRU), and hybrid LSTM-GRU utilised. Models with BO achieved better results than others. To improve each model's results with BO; Gradient Incremental Regression Trees (GBRT), Gaussian Process (GP), Random Forest (RF) and Extra Trees (ET) were applied to optimizers and corresponding surrogate functions. Evaluating the effects of hyper-parameter values on the problem for each method contributes to the parameter selection process for similar prediction problems. To increase comparability in the literature, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Square Error (MSE) were used. There is a least one hyper-parameter combination, which produces a result close to the best value for each model when the results obtained from the experiments are interpreted. BO with hybrid LSTM-GRU outperformed all methods in this paper and the examined literature for the value of RMSE, MSE, and MAE.
Д. В. Фирсов, S. N. Silvestrov, Nikolay Kuznetsov, Evgeny V. Zolotarev · 5 authors
This paper identifies hidden patterns between trading volumes and the market value of an asset. Based on open market data, we try to improve the existing corpus of research using new, innovative neural network training methods. Dividing into two independent models, we conducted a comparative analysis between two methods of training Proximal Policy Optimization (PPO) models. The primary difference between the two PPO models is the data. To showcase the drastic differences the PPO model makes in market conditions, one model uses historical data from Binance trading history as a data sample and the trading pair BNB/USDT as a predicted asset. Another model, apart from purely price fluctuations, also draws data on trading volume. That way, we can clearly illustrate what the difference can be if we add additional markers for model training. Using PPO models, the authors conduct a comparative analysis of prediction accuracy, taking the sequence of BNB token values and trading volumes on 15-minute candles as variables. The main research question of this paper is to identify an increase in the accuracy of the PPO model when adding additional variables. The primary research gap that we explore is whether PPO models specifically trained on highly volatile assets can be improved by adding additional markers that are closely linked. In our study, we identified the closest marker, which is a trading volume. The study results show that including additional parameters in the form of trading volume significantly reduces the model's accuracy. The scientific contribution of this research is that it shows in practice that the PPO model does not require additional parameters to form accurately predicting models within the framework of market forecasting. Doi: 10.28991/ESJ-2023-07-04-012 Full Text: PDF
Purpose This study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC). Design/methodology/approach The study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions. Findings The study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F -statistic indicates the general model's acceptability in the results. Practical implications The study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market. Originality/value This study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.
Pairs trading is a popular quantitative trading strategy with the advantage of a similarity in price movement to financial assets. Assuming that the price spreads of trading pairs are mean-reverting, this strategy exploits the disequilibrium in financial markets to find arbitrage investment opportunities. Pairs trading has been widely applied to stock, ETF, and commodity markets. However, the effectiveness of this method for cryptocurrency markets has yet to be properly explored. Therefore, we examine the profitability of pairs trading for 26 cryptocurrencies traded on the Binance exchange at high frequencies of 1, 5, and 60 min. In addition to the traditional statistical methods of distance, correlation, cointegration, and stochastic differential residual (SDR), we focus on two evolutionary algorithms: genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). During the 79-trading-day period from 11 January to 31 March 2018, NSGA-II showed the best results at all frequencies, with an average return of 2.84%. Among the statistical models, SDR ranks first, whereas Correlation ranks last, with average returns of 1.63% and −0.48%, respectively. The z-test results show that the models are statistically significantly different. We propose NSGA-II as the best candidate for use in pairs trading strategies in cryptocurrency markets.
Bitcoin is the very first digital or cryptocurrency based on Block chain concept. The objective of this paper is to forecast the price of Bitcoin using various Machine Learning Time Series models like: Moving Averages (MA), Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM). As we know that the price of Bitcoin is very volatile in nature so producing appropriate predictions is difficult. Also we know that Bitcoin Price nature is not stationary, so we have converted our non-stationary data to stationary for models like ARIMA which works properly on stationary data only. At last, we have compared the results of MA, ARIMA, XGBoost and LSTM for Bitcoin prediction based on RMSE and we found that first three models have given somewhere similar results whereas LSTM has given different.
Cryptocurrency, a digital form of currency, has emerged as a prominent asset class with unique characteristics such as decentralization, security, and global accessibility. As the adoption of cryptocurrencies continues to grow, the need for accurate price prediction using machine learning (ML) algorithms becomes crucial for various stakeholders in the financial ecosystem. This paper presents a comprehensive approach to cryptocurrency price prediction, focusing on the following key aspects: The data for this study is collected from the Binance platform, a leading cryptocurrency exchange known for its extensive market data and liquidity. The dataset includes historical price data across different time frames, including 1-hour, 4-hour, and daily intervals. Prior to analysis, the collected data undergoes thorough preprocessing steps to ensure data quality and consistency. This process includes handling missing values, removing outliers, and standardizing data formats for further analysis. Long Short-Term machine learning model algorithm is employed for price prediction. This model is chosen for its ability to capture complex patterns and dynamics in cryptocurrency price movements. The prediction phase involves training and testing the ML model using the preprocessed data. Performance evaluation metrics such as R-squared, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE) are utilized to assess the accuracy and robustness of the prediction model across different time frames. Accurate cryptocurrency price prediction is essential for various stakeholders, including investors, traders, businesses, and regulators. It facilitates informed decision- making, risk management, market analysis, trading strategy development, business planning, and regulatory compliance in the dynamic cryptocurrency market. By addressing these key elements, this study aims to contribute to the advancement of cryptocurrency price prediction methodologies using ML techniques, thereby enhancing decision-making processes and fostering a more efficient and transparent digital asset market ecosystem.
Cryptocurrencies have become a major element in enterprises and financial market showing promising potential during the past ten years. Predictions that are accurate can help cryptocurrencies investors to make the best decisions and possibly enhance their earnings. For this work 4 cryptocurrencies namely Bitcoin, Binance Coin, Ethereum and Tether (USDT) are considered. The considered dataset is for around 5 years, which has a total of 9 columns, One Date Column ranging from year 2017 to 2022 and for each of the Coin there is the Closing price and the Volume. The Dataset is collected from Kaggle. Attempts are made to build the models in various ways, by dealing with certain features or by taking a subset of the dataset. All the models, according to the way the dataset was dealt were evaluated based on certain evaluation metrics like R-squared, mean square error and root mean square error. The models used are KNN, Decision Tree, Random Forest, XGBoost and CatBoost. Visualized and understood how the prices of those cryptocurrencies were impacted by other features of the dataset. The models that outperformed are Random Forest and CatBoost. Achieved RMSE was 365, 2.99, 29.99 and 0.0023 for Bitcoin, Binance, Ethereum and USDT respectively.
This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.
The volatility and complexity of Bitcoin make it a challenging task to accurately predict its price. While past research has implemented machine learning to enhance the precision of Bitcoin price prediction, limited attention has been given to examining the viability of employing diverse modeling techniques to datasets with varying data structures and dimensional attributes. In order to forecast Bitcoin prices using machine learning techniques at different intervals, this study initiates by categorizing Bitcoin prices into daily prices and highfrequency prices. This project aims to predict the price of Bitcoin using machine learning techniques, specifically the Random Forest Classifier algorithm.
Mansour Davoudi, Mina Ghavipour, Morteza Sargolzaei-Javan, Saber Dinparast
<title>Abstract</title> This study focuses on analyzing four of the most significant cryptocurrencies in the field of decentralized storage, including Filecoin, Arweave, Storj, and Siacoin. Our method consists of three main components: Network Analysis, Textual Analysis, and Market Analysis. Network Analysis involves identifying relevant entities associated with the target cryptocurrencies to construct a network of entities. During this component, the embeddings of each entity are then extracted using node2vec which are fed into a convolutional neural network. In the second component, Textual Analysis, we first employ the T5 summarization model to encapsulate the content of related news articles. Subsequently, by utilizing the FinBert model the sentiment of news articles and tweets associated with the identified entities are extracted. We then use transformer encoders to process the resulting feature vectors. Ultimately, similar to the Textual component, by leveraging the transformer encoders the financial market information of target cryptocurrencies is evaluated during the Market Analysis component. As the final step, the outputs of these components are combined to predict the price trend of the target cryptocurrencies within a specified time frame. The proposed model’s accuracy in forecasting the future price trend of Filecoin, Storj, Arweave, and Siacoin is 76%, 83%, 61%, and 74% respectively.
In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.
Many researchers agree that sentiment analysis can improve the performance of quantitative trading models. We develop two off-the-shelf solutions for analyzing the sentiments of cryptocurrency-related social media posts. First, we posttrain and fine-tune a Twitter-oriented model based on the bidirectional encoder representations from transformers (BERT) architecture, BERTweet, on the cryptocurrency domain, resulting in CryptoBERT. Second, we generate the language-universal cryptocurrency emoji (LUKE) sentiment lexicon and prediction pipeline, utilizing the sentiment of emojis prevalent in social media. CryptoBERT is highly accurate, while LUKE is suitable for non-English posts, thus allowing for direct classification and noisy label generation in less popular languages. Our research can help cryptocurrency investors develop trading software supported by sentiments mined from social media.
Vera Mita Nia, Ossi Ferli, Irvan Novikri, Roy Sembel · 5 authors
Increasing market capitalization is characterized by high volatility but doesn’t have the ability and potential for monetary function, Crypto world eventually shifted into the most attractive investment in the digital economy. Numerous published studies have required some improvement in the consistent relationship between commodities and financial assets and the authors proposed an alternative assessment with demonstrating the relationship between the trading volume activity of the most traded cryptocurrency in Indonesia (i.e., Ethereum) and other investment assets in Indonesia such as market indexes, rupiah exchange rate against the dollar, and gold, and related to cryptocurrencies in Indonesia which observed in over the last three years. A Var model as a quantitative and statistical approach introduced and tested the stationary data with significancy value to identify the level of acceptance model. Consistency results from previous studies where Ethereum has the largest average return but higher risk and Gold as safer investment, ultimately diversification of the investment portfolio is suggested considering the degree of risk aversion. ABSTRAK Kapitalisasi pasar yang meningkat ditandai dengan volatilitas yang tinggi namun tidak memiliki kemampuan dan potensi fungsi moneter, dunia Crypto akhirnya bergeser menjadi investasi paling menarik di ekonomi digital. Sejumlah penelitian yang diterbitkan memerlukan beberapa perbaikan dalam hubungan yang konsisten antara komoditas dan aset keuangan dan penulis mengusulkan penilaian alternatif dengan menunjukkan hubungan antara aktivitas volume perdagangan mata uang kripto yang paling banyak diperdagangkan di Indonesia (yaitu, Ethereum) dan aset investasi lainnya di Indonesia seperti indeks pasar, nilai tukar rupiah terhadap dolar, dan emas, serta terkait cryptocurrency di Indonesia yang diamati selama tiga tahun terakhir. Model Var sebagai pendekatan kuantitatif dan statistik memperkenalkan dan menguji data stasioner dengan nilai signifikansi untuk mengidentifikasi tingkat penerimaan model. Hasil konsistensi dari studi sebelumnya di mana Ethereum memiliki pengembalian rata-rata terbesar tetapi risiko lebih tinggi dan Emas sebagai investasi yang lebih aman, pada akhirnya diversifikasi portofolio investasi disarankan dengan mempertimbangkan tingkat penghindaran risiko.
Blockchain is the foundation of cryptocurrency and enables decentralized transactions through its immutable ledger. The technology uses hashing to ensure secure transactions and is becoming increasingly popular due to its wide range of applications. Python is a performant, secure, scalable language well-suited for blockchain applications. It provides developers free tools for faster code writing and simplifies crypto analysis. Python allows developers to code blockchains quickly and efficiently as it is a completely scripted language that does not require compilation. Different models such as SVR, ARIMA, and LSTM can be used to predict cryptocurrency prices, and many Python packages are available for seamlessly pulling cryptocurrency data. Python can also create one's cryptocurrency version, as seen with Facebook's proposed cryptocurrency, Libra. Finally, a versatile and speedy language is needed for blockchain applications that enable chain addition without parallel processing, so Python is a suitable choice.
Bitcoin is one of the cryptocurrencies that has gained popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast its future level, and other price-related features are necessary to improve forecast accuracy. We introduce a new set of time series and demonstrate that a subset is necessary to improve directional accuracy based on a machine learning ensemble. In our experiments, we study which time series and machine learning algorithms deliver the best results. We found that the most relevant time series that contribute to improving directional accuracy are open, high, and low, with the largest contribution of low in combination with an ensemble of a gated recurrent unit network and a baseline forecast. The relevance of other Bitcoin-related features that are not price-related is negligible. The proposed method delivers similar performance to the state of the art when observing directional accuracy.
Investment predicated on price trends stands as one of the most prevalent and efficacious approaches, hinging on its capacity to accurately discern the price trajectory for each asset. Such a pursuit poses itself as one of the most formidable challenges within the realm of investments. In this study, the application of machine learning models is employed, while simultaneously comparing their prognostic capabilities to evaluate their performance in forecasting cryptocurrency price trends. Additionally, the normalization technique and the Shapley Additive exPlanations (SHAP) feature selection method are employed to effectively augment the aptitude for projecting cryptocurrency price trends. The prediction period encompasses the time span from January 1, 2014, to December 31, 2021. The experimental findings reveal that the Support Vector Machine (SVM) outperforms other models such as K-Nearest Neighbors (KNN), Random Forest (RFC), Naïve Bayes, and Long short-term memory (LSTM) when forecasting periods extend 7, 15, and 30 days beyond the present, respectively. However, when the forecast horizon is extended to 90 days, the LSTM model exhibits the most optimal performance.
This research aims to analyze and explain the importance of diversification benefit of cryptocurrencies and its nature in accordance with its relation with other financial assets before and especially during the Covid-19 pandemic era. This paper will help investors to understand that how to manage a portfolio of cryptocurrencies in parallel with other financial assets and mainly cryptocurrencies since they were a safe investment option during the pandemic period due to the good defense these digital currencies activated against the covid-19 shock back in 2020. Paper used DCC-GARCH model to examine the safety of Bitcoin and Ethereum with financial market of S & P 500 and FTSE100.