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Jermain Kaminski

Publications and source records attributed to Jermain Kaminski.

3 recordsLinked to original sources

The Software Complexity of Nations

Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records, e.g. data on exports, patents, and employment, that have blind spots when it comes to the digital economy. In this paper we use data on the geography of programming languages used in open-source software to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity index (ECIsoftware) and show that it complements the ability of measures of complexity based on trade, patents, and research to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's entries and exits in programming languages are partly explained by its current pattern of specialization. Together, these findings help extend economic complexity ideas and their policy implications to the digital economy.

econ.GN

New Technology Assessment in Entrepreneurial Financing - Can Crowdfunding Predict Venture Capital Investments?

Recent years have seen an upsurge of novel sources of new venture financing through crowdfunding (CF). We draw on 54,943 successfully crowdfunded projects and 3,313 venture capital (VC) investments throughout the period 04/2012-06/2015 to investigate, on the aggregate level, how crowdfunding is related to a more traditional source of entrepreneurial finance, venture capital. Granger causality tests support the view that VC investments follow crowdfunding investments. Cointegration tests also suggest a long-run relationship between crowdfunding and VC investments, while impulse response functions (IRF) indicate a positive effect running from CF to VC within two to six months. Crowdfunding seems to help VC investors in assessing future trends rather than crowding them out of the market.

cs.CY

Nowcasting the Bitcoin Market with Twitter Signals

This paper analyzes correlations and causalities between Bitcoin market indicators and Twitter posts containing emotional signals on Bitcoin. Within a timeframe of 104 days (November 23rd 2013 - March 7th 2014), about 160,000 Twitter posts containing "bitcoin" and a positive, negative or uncertainty related term were collected and further analyzed. For instance, the terms "happy", "love", "fun", "good", "bad", "sad" and "unhappy" represent positive and negative emotional signals, while "hope", "fear" and "worry" are considered as indicators of uncertainty. The static (daily) Pearson correlation results show a significant positive correlation between emotional tweets and the close price, trading volume and intraday price spread of Bitcoin. However, a dynamic Granger causality analysis does not confirm a statistically significant effect of emotional Tweets on Bitcoin market values. To the contrary, the analyzed data shows that a higher Bitcoin trading volume Granger causes more signals of uncertainty within a 24 to 72-hour timeframe. This result leads to the interpretation that emotional sentiments rather mirror the market than that they make it predictable. Finally, the conclusion of this paper is that the microblogging platform Twitter is Bitcoin's virtual trading floor, emotionally reflecting its trading dynamics.

cs.SI