This paper aims to bridge the knowledge gap in the existing literature on Bitcoin, Blockchain and Fintech. It begins by clarifying the definition of these concepts. Through a systematic review and ...

Bitcoin, Blockchain and Fintech: a systematic review and case studies in the supply chain

We implement deep learning techniques to forecast the price of the three most widely traded digital currencies i.e., Bitcoin, Digital Cash and Ripple. To the best of our knowledge, this is the first work to make use of deep learning in cryptocurrency prediction. The results from testing the existence of nonlinearity revealed that the time series of all digital currencies exhibit fractal dynamics, long memory and self-similarity. The predictability of long-short term memory neural network topologies (LSTM) is significantly higher when compared to the generalized regression neural architecture, set forth as our benchmark system. The latter failed to approximate global nonlinear hidden patterns regardless of the degree of contamination with noise, as they are based on Gaussian kernels suitable only for local approximation of non-stationary signals. Although the computational burden of the LSTM model is higher as opposed to brute force in nonlinear pattern recognition, eventually deep learning was found to be highly efficient in forecasting the inherent chaotic dynamics of cryptocurrency markets.

Cryptocurrency forecasting with deep learning chaotic neural networks

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The predictive power of public Twitter sentiment for forecasting cryptocurrency prices

Cryptocurrencies represent one of the most attractive markets for financial speculation. As a consequence, they have attracted unprecedented attention on social media. Besides genuine discussions and legitimate investment initiatives, several deceptive activities have flourished. In this work, we chart the online cryptocurrency landscape across multiple platforms. To reach our goal, we collected a large dataset, composed of more than 50M messages published by almost 7M users on Twitter, Telegram and Discord, over three months. We performed bot detection on Twitter accounts sharing invite links to Telegram and Discord channels, and we discovered that more than 56% of them were bots or suspended accounts. Then, we applied topic modeling techniques to Telegram and Discord messages, unveiling two different deception schemes - “pump-and-dump” and “Ponzi” - and identifying the channels involved in these frauds. Whereas on Discord we found a negligible level of deception, on Telegram we retrieved 296 channels involved in pump-and-dump and 432 involved in Ponzi schemes, accounting for a striking 20% of the total. Moreover, we observed that 93% of the invite links shared by Twitter bots point to Telegram pump-and-dump channels, shedding light on a little-known social bot activity. Charting the landscape of online cryptocurrency manipulation can inform actionable policies to fight such abuse.

Charting the Landscape of Online Cryptocurrency Manipulation

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time...

https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180643

Dissection of Bitcoin's multiscale bubble history from January 2012 to February 2018.

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.

/pdf/when-bitcoin-encounters-information-in-an-online-forum-using-2a3c687ro1.pdf

When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

/pdf/predicting-virtual-world-user-population-fluctuations-with-2uutgmjljj.pdf

Predicting Virtual World User Population Fluctuations with Deep Learning

We propose a physics-inspired simulation framework that expresses visual effects of flowing water frozen in glaze or directional icicles. The proposed ice model considers the direction of the water flow, which affects the growth of icicles. Water dynamics are computed using a conventional particle-based simulation. Ice glaze and directional icicles are generated by incorporating our freezing solver. To determine whether a water particle is converted into ice or remains liquid, we compute the nucleation energy based on the humidity and water flow. The humidity is approximated as a virtual water film on object surfaces. The water flow is incorporated by introducing a growth direction vector to guide the direction of icicle growth. Ice-generating regions can be controlled using 3-D modeling tools such as Autodesk Maya or 3DS Max. Experiments showed that a realistic ice glaze was created on the surfaces of objects and that icicles grew in the direction of the water flow.

Nuri Park

Papers

When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.

Predicting Virtual World User Population Fluctuations with Deep Learning

Visual simulation of rapidly freezing water based on crystallization