Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach

The growth of market capitalisation and the number of altcoins (cryptocurrencies other than Bitcoin) provide investment opportunities and complicate the prediction of their price movements. A significant challenge in this volatile and relatively immature market is the problem of predicting cryptocurrency prices which needs to identify the factors influencing these prices. The focus of this study is to investigate the factors influencing altcoin prices, and these factors have been investigated from a causal analysis perspective using Bayesian networks. In particular, studying the nature of interactions between five leading altcoins, traditional financial assets including gold, oil, and S\&P 500, and social media is the research question. To provide an answer to the question, we create causal networks which are built from the historic price data of five traditional financial assets, social media data, and price data of altcoins. The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins. Furthermore, it is not possible to generalise the coins' reactions against the changes in the factors. Consequently, the coins need to be studied separately for a particular price movement investigation

Patterns of Change: Can modifiable software have high coupling?

There are few aspects of modern life that remain unaffected by software, and as our day-to-day challenges change, so too must our software. Software systems are complex, and as they grow larger and more interconnected, they become more difficult to modify due to excessive change propagation. This is known as the ripple effect. The primary strategies to mitigate it are modular design, and minimization of coupling, or between-module interaction. However, analysis of complex networks has shown that many are scale-free, which means that they contain some components that are highly connected. The presence of scale-free structure implies high coupling, which suggests that software systems may be hard to modify because they suffer from the ripple effect. In this thesis, a large corpus of open-source software systems is analysed to determine whether software systems are scale-free, whether scale-free structure results in high coupling, and whether high coupling results in ripple effects that propagate change to a large proportion of classes. The results show that all systems in the corpus are scale-free and that that property results in high coupling. However, analysis of system evolution reveals that existing code is modified infrequently and that there is rarely sufficient evidence to be confident that ripple effects involving a high proportion of classes have actually occurred. This thesis concludes first that while it is desirable to avoid excessive interconnectivity, it is difficult to completely eliminate high coupling; and second, that the presence of high coupling does not necessarily imply poor system design

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

Cryptocurrencies as an Alternative Asset Class

Bitcoin was the first digital currency to rely on a decentralized peer-to-peer network instead of a trusted third party. This was achieved through Bitcoin’s revolutionary underlying technology based on cryptographic proof: the blockchain. After Bitcoin’s emergence, many other so called cryptocurrencies entered the market and we have seen enormous price increases that romised large returns for early users. The return characteristics of cryptocurrencies have been studied by various scholars and some have even declared cryptocurrencies to be an asset class instead of a digital currency. Due to the fast changes in the cryptocurrency market and the increased importance of other cryptocurrencies than Bitcoin, we believe that research focusing on the financial performance of cryptocurrencies should be renewed on a regular basis. Therefore, with this work we aim to shed light on the return characteristics of cryptocurrencies in relation to traditional asset classes and on the potential of cryptocurrencies to improve portfolio diversification. In addition, we investigate the cryptocurrency market, describe selected cryptocurrencies in more detail and provide an overview of potential technological risks arising with the use of cryptocurrencies. Our results indicate that cryptocurrencies provide large return potentials with high levels of volatility but compared to traditional asset classes provide a higher level of return per level of risk. We also find that selected cryptocurrencies can improve diversification in a cryptocurrency portfolio, as well as in a portfolio of international equity and private equity investments. Keywords: Alternative Asset Classes, Cryptocurrency, Portfolio Diversification, Risk-Reward Profile und Cryptocurrency Risk

Quantifying economic benefits for rail infrastructure projects

Investment in rail infrastructure is necessary to maintain existing service and to cater for future growth in freight and passenger services. Many communities have realized the importance of investment in rail infrastructure projects and set up goals and visions to achieve economic development through investing in such projects. Due to limited funds available, communities have to select a single or very few projects from a variety of projects. It is very critical that right projects must be selected at the right time for a community to realize economic development. The limited methods for quantifying the economic benefits to the stakeholders often cause a problem in the selection process. Most of the conventional methods focus mainly on the economic impact of the project and ignore the metrics that convey the economic impacts in meaningful ways to the key stakeholders involved. This leads to uncertainty in the project selection and planning process and often leads to failure in achieving the goals of the project. This study aims to provide a mathematical framework that quantifies economic benefits of investment in rail infrastructure projects in meaningful ways to the key stakeholders through three different approaches, namely, Leontief-based approach, Bayesian approach and system dynamics approach. The Leontief-based approach is the easiest of all the three approaches provided that historical data is available. Bayesian approach is also very beneficial as it can be used by coupling small data with surveys and interviews. Also, system dynamics model is very useful to conduct qualitative analysis, but the quantitative analysis part can become very complex --Abstract, page iii

Mind the Gap: Trade-Offs between Distributed Ledger Technology Characteristics

When developing peer-to-peer applications on Distributed Ledger Technology (DLT), a crucial decision is the selection of a suitable DLT design (e.g., Ethereum) because it is hard to change the underlying DLT design post hoc. To facilitate the selection of suitable DLT designs, we review DLT characteristics and identify trade-offs between them. Furthermore, we assess how DLT designs account for these trade-offs and we develop archetypes for DLT designs that cater to specific quality requirements. The main purpose of our article is to introduce scientific and practical audiences to the intricacies of DLT designs and to support development of viable applications on DLT