This is the first article that studies BitCoin price formation by considering both the traditional determinants of currency price, e.g., market forces of supply and demand, and digital currencies specific factors, e.g., BitCoin attractiveness for investors and users. The conceptual framework is based on the Barro (1979) model, from which we derive testable hypotheses. Using daily data for five years (2009–2015) and applying time-series analytical mechanisms, we find that market forces and BitCoin attractiveness for investors and users have a significant impact on BitCoin price but with variation over time. Our estimates do not support previous findings that macro-financial developments are driving BitCoin price in the long run.

https://www.econstor.eu/bitstream/10419/142671/1/EERI_RP_2014_08.pdf

The economics of BitCoin price formation

Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.

/pdf/the-effects-of-twitter-sentiment-on-stock-price-returns-2ln71tkzim.pdf

The effects of Twitter sentiment on stock price returns

The paper seeks to address what Bitcoin looks like. Specifically, we attempt to identify the main determinants of Bitcoin price by means of rigorous evaluation through ARDL Bounds Testing method. Our findings reveal the extremely speculative behavior of Bitcoin, its partial usefulness in trade trans- actions without overlooking its dependence to the Shangai stock market and the hash rate. There is no sign of Bitcoin being a safe haven. Taking a step further, we re-investigate the focal link by accounting for the Chinese trad- ing bankruptcy. The results appear fairly robust. Bitcoin is still perceived as speculative foolery and thus far from being a long-term promise.

/pdf/what-does-bitcoin-look-like-htjawfhb4h.pdf

What Does Bitcoin Look Like

Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

Infectious Disease Modeling of Social Contagion in Networks

Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets.

/pdf/cohesiveness-in-financial-news-and-its-relation-to-market-5dmd2fslg2.pdf

Cohesiveness in financial news and its relation to market volatility.

Motivated by recent financial crises significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said about influence of financial news on financial markets. We propose a novel measure of collective behaviour in financial news on the Web, News Cohesiveness Index (NCI), and show that it can be used as a systemic risk indicator. We evaluate the NCI on financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and financially related news. We hypothesized that strong cohesion in financial news reflects movements in the financial markets. Cohesiveness is more general and robust measure of systemic risk expressed in news, than measures based on simple occurrences of specific terms. Our results indicate that cohesiveness in the financial news is highly correlated with and driven by volatility on the financial markets.

/pdf/news-cohesiveness-an-indicator-of-systemic-risk-in-financial-g3z6txgmfq.pdf

News Cohesiveness: an Indicator of Systemic Risk in Financial Markets.

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume.

Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain

In this paper we describe the system for real-time machine vision recognition of chess table and figures. Input data are two synchronized video sequences from a top-view and side-view camera showing the game of chess between two players. The top-view is used mainly for determining the positions of the figures on the table while side-view enables correct recognition of figures in cases where they are not occluded by another figure in front of them. Even in cases where that happens the top-view helps to track the occluded figures along the table. No prior knowledge of the chess rules is used, so there are no assumptions on how and where particular figures are allowed to move. OpenCV algorithm implementations are used for most of the preprocessing and analysis, including the identification of the chess table and classification of figures with SVM classifier.

https://www.zora.uzh.ch/id/eprint/206576/1/Computer_vision_system_for_the_chess_game_reconstruction.pdf

Computer vision system for the chess game reconstruction

This paper presents MultiNets: a Javascript library for multilayer network visualization. MultiNets provides reusable HTML components with functions for loading, manipulation and visualization of multilayered networks. These components can be easily incorporated into any web page, and they allow users to perform exploratory analysis of multilayer networks and prepare publication quality network visualizations. MultiNets components are easily extendable to provide custom-based visualizations, such as embedding networks on geographical maps, and can be used for building complex web-based graphical user interfaces for data mining services that operate on multilayered networks and multirelational data in general.

/pdf/multinets-web-based-multilayer-network-visualization-4mwp4gjy16.pdf

Matija Piskorec

Papers

Cohesiveness in financial news and its relation to market volatility.

News Cohesiveness: an Indicator of Systemic Risk in Financial Markets.

Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain

Computer vision system for the chess game reconstruction

MultiNets: Web-Based Multilayer Network Visualization