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The authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.

The authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.

The authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.

The proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.

The objective of this paper is to investigate how firms react to cybersecurity information sharing environment where government organizations disseminate cybersecurity threat information gathered by individual firms to the private entities. The overall impact of information sharing on firms' cybersecurity investment decision has only been game-theoretically explored, not giving practical implication. The authors therefore leverage the Cybersecurity Information Sharing Act of 2015 (CISA) to observe firms' attitudinal changes toward investing in cybersecurity.

The authors design a quasi-experiment where they set US cybersecurity firms as an experimental group (a proxy for total investment in cybersecurity) and nonsecurity firms as a control group to measure the net effect of CISA on overall cybersecurity investment. To enhance the robustness of the authors’ difference-in-difference estimation, the authors employed propensity score matched sample test and reduced sample test as well.

For the full sample, the authors’ empirical findings suggest that US security firms' overall performance (i.e. Tobin's Q) improved following the legislation, which indicates that more investment in cybersecurity was followed by the formation of information sharing environment. Interestingly, big cybersecurity firms are beneficiaries of the CISA when the full samples are divided into small and large group. Both Tobin's Q and sales growth rate increased for big firms after CISA.

The authors’ findings shed more light on the research stream of cybersecurity and information sharing, a research area only explored by game-theoretical approaches. Given that the US government has tried to enforce cybersecurity defensive measures by building cooperative architecture such as CISA 2015, the policy implication of this study is far-reaching.

The authors’ study contributes to the research on the economic benefits of sharing cybersecurity information by finding the missing link (i.e. empirical evidence) between “sharing” and “economic impact.” This paper confirms that CISA affects the cybersecurity industry unevenly by firm size, a previously unidentified relationship.