Blockchain Papers

Haritima Manchanda, Swati Aggarwal

No abstract is available for this record.

Yasuo Matsuyama

This study interrelates three adjacent topics in data evaluation. The first is the establishment of a relationship between Bregman divergence and probabilistic alpha-divergence. In particular, we demonstrate that square-root-order probability normalization enables the unification of these two divergence families. This yields a new alpha-divergence, which can be used to jointly derive the alpha-EM algorithm (alpha-expectation-maximization algorithm) and the traditional log-EM algorithm. The second topic is the application of the alpha-EM algorithm in the evaluation of graders scoring raw data over a network. We estimate multinomial mixture distributions in this evaluation problem. We note that the convergence speed of the alpha-EM algorithm is significantly higher than that of the log-EM algorithm. Finally, the third topic is the use of this increase in convergence speed to assign the winning evaluator and miner in a blockchain environment. This is achieved by proof-of-review using evaluation scores, which is a class of proof-of-stake. In the second and third topics, we select terminology from wine tasting for brevity in the exposition. However, this formulation can be applied to a broader class of data in a network environment comprising blockchains.

Waddah Waheeb, Habib Shah, Mohammed Jabreel, Domènec Puig

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods.

Navid Parvini, Mahsa Abdollahi, Amir Nozari

The Theta method has attracted academic attention lately due to its simplicity and superior performance. This paper proposes a new hybrid forecasting approach based on combining the Theta decomposition method and support vector regression (SVR) for forecasting highly volatile and noisy Bitcoin price time series. Using Theta decomposition with coefficients ranging from 0 to 2 with 0.1 steps, we extracted 20 Theta lines from the original time series. Each of these 20 lines is used for a univariate regression. Then the results of each forecasts aggregated to construct the final predicted values. Moreover, we used the Theta lines to construct a predictor space for multivariate regression using SVR. However, due to poor performance of the multivariate regression and to further enhance its performance, we eliminated inefficient Theta lines from the predictor space. Enhanced MASE by 10.45% and 5.68% in comparison to the Theta-SES (classic Theta) and SVR, the results indicate the superiority of the proposed hybrid Theta-SVR.

Febri Liantoni, Arif Agusti

Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.

Tri Wijayanti Septiarini, Muhammad Rifki Taufik, Mufti Afif, Atika Rukminastiti Masyrifah

Abstract The objective of this study were (i) to construct the classical statistic and artificial intelligent model for predicting bitcoin cryptocurrency, and (ii) to compare the predicting performance by using root mean square error (RMSE) and mean square error (MSE) as forecasting evaluation tool. The observation data used in this study were collected during January, 5 2017 to October, 1 2019 (in total 1,000 daily observation data). The statistical method used in this study were ARIMA (Autoregressive Moving Average) and Exponential Smoothing. The artificial intelligent model were used in this study were fuzzy time series and ANFIS (Adaptive Neuro Fuzzy Inference System). The partitions data set were of 75%-25% of training and testing, respectively. The cryptocurrency investigated was bitcoin (BTC) which is the top three of most widely traded cryptocurrency. The forecasting results show that the classical method has the smallest value of RMSE and MSE which is exponential smoothing with 9749.81 for MSE and 98.74 for RMSE. However, the performance of forecasting method cannot be guaranteed from either classical or modern forecasting method. Analyzing with different method can be considered for future study, for example machine learning, neural network, modified fuzzy time series, etc.

Kiki Ramadani, Dodi Devianto

Bitcoin is electronic money that can be used as an alternative for investment. Investors will get benefit buying bitcoin when the price of bitcoin is down and reselling it when bitcoin prices are increasing. The fluctuating bitcoin prices cause forecasting as a basis for investors to make decisions, where the time series method is used as a forecasting model, then a pattern can be found to predict future events. The classical time series methods are often violating the statistical assumptions. To face these problems, then it is used free assumptions methods, the method with the Fuzzy Time Series Markov Chain, the Chen Logical Method, and its segmented methods due to unbalancing forecasting results. This study is built the forecasting model of the price of bitcoin for the coming period based on the data from 2010 to 2020. The proposed methods have a better fit for bitcoin time series data prices. Besides, the Fuzzy Time Series Markov Chain method has the slightly smallest accuracy error based on Mean Absolute Percentage Error (MAPE) comparing to the Fuzzy Time Series Segmented Chen Logical Method and Fuzzy Time Series Chen Logical Method.

Ioannis Chalkiadakis, Anna Zaremba, Gareth W. Peters, Michael J. Chantler

This paper establishes a new framework for assessing multimodal statistical causality between cryptocurrency market (cryptomarket) sentiment and cryptocurrency price processes. In order to achieve this, we present an efficient algorithm for multimodal statistical causality analysis based on Multiple-Output Gaussian Processes. Signals from different information sources (modalities) are jointly modelled as a Multiple-Output Gaussian Process, and then using a novel approach to statistical causality based on Gaussian Processes (GPs), we study linear and non-linear causal effects between the different modalities. We demonstrate the effectiveness of our approach in a machine learning application by studying the relationship between cryptocurrency spot price dynamics and sentiment time-series data specific to the crypto sector, which we conjecture influences retail investor behaviour. The investor sentiment is extracted from cryptomarket news data via methods developed in the area of statistical machine learning known as Natural Language Processing (NLP). To capture sentiment, we present a novel framework for text to time-series embedding, which we then use to construct a sentiment index from publicly available news articles. We conduct a statistical analysis of our sentiment statistical index model and compare it to alternative state-of-the-art sentiment models popular in the NLP literature. In regard to the multimodal causality, the investor sentiment is our primary modality of exploration, in addition to price and a blockchain technology-related indicator (hash rate). Analysis shows that our approach is effective in modelling causal structures of variable degree of complexity between heterogeneous data sources and illustrates the impact that certain modelling choices for the different modalities can have on detecting causality. A solid understanding of these factors is necessary to gauge cryptocurrency adoption by retail investors and provide sentiment- and technology-based insights about the cryptocurrency market dynamics.

Lu Xu

This paper mainly studies the retailer's reorder quantity and the profits of retailer, supplier and entire supply chain with inventory financing under incomplete information based on entropy. First we establish a inventory financing model to derive the reorder quantity of retailer under centralized supply chain and decentralized supply chain respectively. Then introduce the maximum entropy method to predict the market demand distribution. Fianlly, the results of our experiment indicate that information value can be higher when the market demand fluctuates severely and the best reorder quantity decision for retailer should be considered from entire supply chain except the fluncation of demand is big under incomplete information, while the retailer's decentralized decision under incomplete infromation will lead the entire supply chian to the worst profit.

Vijaydeep Siddharth, Mansoor Hussain, Sanjay Arya

BACKGROUND: An efficient inventory control system would help optimize the use of resources and eventually help improve patient care. OBJECTIVES: The study aimed to find out the surgical consumables using always, better, and control (ABC) and vital, essential, and desirable (VED) technique as well as calculating the lead time of specific category A and vital surgical consumables. METHODS: This was a descriptive, record-based study conducted from January to March 2016 in the surgical stores of the All India Institute of Medical Sciences, New Delhi. The study comprised all the surgical consumables which were procured during the financial year 2014-2015. Stores ledger containing details of the consumption of the items, supply orders, and procurement files of the items were studied for performing ABC analysis and calculating the lead time. A list of surgical consumables was distributed to the doctors, nursing staff, technical staff, and hospital stores personnel to categorize them into VED categories after explaining them the basis for the classification. RESULTS: ABC analysis revealed that 35 items (14%), 52 items (21%), and 171 items (69%) were categorized into A (70% annual consumption value [ACV]), B (20% ACV), and C (10% ACV) category, respectively. In the current study, vital items comprised the majority of the items, i.e., 73% of the total items and essential (E) category of items comprised 26% of all the items. The average internal, external, and total lead time was 17 days (range 3-30 days), 25 days (range 5-38) and 44 days (range 18-98 days), respectively. CONCLUSIONS: Hospitals stores need to implement inventory management techniques to reduce the number of stock-outs and internal lead time.

Weifan Jiang, Jian Liu

Overconfidence is a universal psychological behavior. Overconfidence on demand awareness will have a significant impact on operation decisions. The supplier estimated the demand with excessive precision which influences the inventory financing decision-making deeply. We built the demand function based on the supplier’s overconfidence. Then we established the retailer, supplier, and the Bank’s profit function, respectively. Through the analysis of the bilevel Stackelberg game, we obtained the order quantity of the retailer with the capital constraint, the wholesale price of overconfident supplier, and the loan-to-value ratio of Bank, and we analyzed the influence of overconfidence on the decision variables. We have several findings as follows. First, the overconfidence makes the decisions of the retailer, supplier, and Bank deviate from the rational decisions. Second, the space of the market profit will affect the decision variables in the joint decision-making. Third, the financing supply chain (including the Bank and supply chain) should have a positive attitude towards the overconfidence of the supplier. Forth, in the joint decision-making, the supplier need determines the buyback price according to the capital demand; and in the decentralized decision-making, the supplier should try to use high buyback price strategy.

Nor Azizah Hitam, Amelia Ritahani Ismail

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

Isil Yenidogan, Aykut Çayır, Ozan Kozan, Tugce Dag · 5 authors

This paper presents all studies, methodology, and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model, the performance metrics of PROPHET and ARIMA methods are compared on the same dataset. The dataset selected for this study starts from May 2016 and ends in March 2018, which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion and feature selection. Although the time series analysis has a univariate characteristics, it is aimed to include some additional variables to each model to improve the forecasting accuracy. Those additional variables are selected based on different correlation studies between cryptocurrencies and real currencies. The model selection for both ARIMA and PROPHET is done by using threefold splitting technique considering the time series characteristics of the dataset. The threefold splitting technique gave the optimum ratios for training, validation, and test sets. Finally two different models are created and compared in terms of performance metrics. Based on the extensive testing we see that PROPHET outperforms ARIMA by 0.94 to 0.68 in R2values.

Dian Utami Sutiksno, Ansari Saleh Ahmar, Nuning Kurniasih, Eko Susanto · 5 authors

The purpose of this study is to apply the α-Sutte Indicator and ARIMA in forecasting data. α-Sutte Indicator is a new forecasting method that was developed in 2017 by Ansari Saleh Ahmar. To see the accuracy of these methods, the forecasting results of the α-Sutte Indicator will be forecasting methods compared to other items, namely: ARIMA. Based on the results of forecasting, it is found that α-Sutte Indicator has MSE and MAE values that are lower than other methods (ARIMA). This is supported by MSE data from α-Sutte Indicator smaller than ARIMA(1,1,1).

Devavrat Shah, Kang Zhang

In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called "latent source model". The Bayesian regression for "latent source model" was introduced and discussed by Chen, Nikolov and Shah (2013) and Bresler, Chen and Shah (2014) for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace.

Eric Paulsen, Simon Perchun

Sammanfattning Examensarbete i företagsekonomi III, Ekonomihögskolan vid Linnéuniversitetet i Kalmar, Ekonomistyrning, 2FE71E, VT 2014. Författare: Eric Paulsen, Simon Perchun Handledare och examinator: Thomas Karlsson & Petter Boye Titel: Bitcoin - Risk eller möjlighet? Bakgrund: Den digitala kryptovalutan har idag blommat upp där Bitcoin står i fokus. Något som blivit uppmärksammat i bland annat media och genom politiska uttalanden. Detta har fått företag till att applicera Bitcoin som möjligt betalsätt utöver de vanliga betalmöjligheterna som exempelvis kortbetalningar. Syfte: Syftet med denna uppsats är att efter insamling av empiri kunna beskriva genom vår utvalda teoretiska referensram varför företag väljer att acceptera Bitcoin som möjligt betalmedel, samt vilka möjligheter och risker som finns förenade inom företagsbranschen, och sedan förklara vidare hur företagen hanterar dessa möjligheter och risker. Metod: För att kunna uppfylla syftet med studien så har vi utgått från en abduktiv metodsyn. Vi har samlat in empiri i form av semistrukturerade kvalitativa intervjuer från sex olika företag som mottar Bitcoin som möjligt betalsätt. Det material som samlats in förklaras sedan utifrån vår teoretiska referensram för att kunna uppfylla syftet med studien. Slutsats: Vi kom till slut fram till att Bitcoin ger företag stora möjligheter i förhållande till den lilla risk de utsätter sig för. En ny betalmöjlighet som kan leda till konkurrensfördelar. Nyckelord: Bitcoin, ekonomi, kryptovalutor, risk, möjligheter.

Robert E. Jensen

Abstract This article focuses on multiple regression analysis to cost control of decentralized operations in the consumer finance industry. There are potential accounting applications of multiple regression analysis in control of decentralized operations. Moreover, multiple regression can be a useful empirical research tool in other areas of accounting and finance. It is essential, however, to know the hidden limitations and assumptions in the approach and to perform the necessary tests to see that these assumptions are met be- fore plunging head-first into a sea of regression formulae. In cost analysis, one feature of multiple regression is the ability to use dichotomous variables. The advantage herein arises when a given characteristic may or may not exist in decentralized units. Multiple regression may be applied without assuming the disturbance terms are normally distributed. Multiple regression may be used in testing structural relationships between operating costs and various factors which are thought to affect these costs. Analysis of variance procedures may be extended to statistical tests of single coefficients and to statistical tests of the contribution to explained variation of sub-groups of factors included in the model.