Since its advent in 2009, Bitcoin and many other cryptocurrencies have been gaining popularity in recent times and attracting attention from investors and regulators alike. The low entry barrier, together with huge profit potentials, has made cryptocurrencies an attractive option for investors. However, participants in this market are also exposed to a high level of volatility and risk as prices can fluctuate strongly on a daily basis. To succeed in this environment, investors need to develop a solid understanding about different aspects of these digital assets.
Return is one of the most fundamental aspects of cryptocurrencies (and financial assets in general) and one that investors are very interested in because it affects their performance directly. There has been some research in this area. For example regarding price behaviours, Urquhart (2017) finds that Bitcoin prices tend to cluster at round numbers. Regarding the relationship between returns and other variables, Balcilar et al. (2017) show that trading volume can help predict cryptocurrency returns to some extent. In a related study, Bouri et al. (2019) show that volume can help forecast extreme returns (both positive and negative). Also related to trading activities, Koutmos (2018) argues that the number of transactions has a delayed positive effect on Bitcoin returns but this effect is minor and short-lived. On the other hand, Bitcoin returns have been found to be negatively correlated with uncertainty about economic policies (Demir et al., 2018). Panagiotidis et al. (2018) confirm some other important contributing factors to Bitcoin returns including Google search intensity and gold returns.
This project aims to contribute to the literature on return determinants of Bitcoin in particular as well as other cryptocurrencies in general. The starting point could be adapting well-known factors in other asset classes. For instance, we can construct a market index for cryptocurrencies similar to the market index for equity (which is used in CAPM and Fama-French factor models, among others). We can also investigate factors specific to the cryptocurrency market (e.g. related to Blockchain). Findings from this project have direct implications for market participants and practitioners. Understanding the return-generating process will give investors an edge to achieve better results, maximising their profits while managing risks.
In addition to the Director of Studies (Dr. Thong Dao), the supervisory team may include Dr. Jeremy Cheah and Dr. Linzhi Tan.
Balcilar, M., Bouri, E., Gupta, R. and Roubaud, D., 2017. Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, pp.74-81.
Bouri, E., Lau, C.K.M., Lucey, B. and Roubaud, D., 2019. Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Research Letters, 29, pp.340-346.
Demir, E., Gozgor, G., Lau, C.K.M. and Vigne, S.A., 2018. Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 26, pp.145-149.
Koutmos, D., 2018. Bitcoin returns and transaction activity. Economics Letters, 167, pp.81-85.
Panagiotidis, T., Stengos, T. and Vravosinos, O., 2018. On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, pp.235-240.
Urquhart, A., 2017. Price clustering in Bitcoin. Economics letters, 159, pp.145-148.
The candidate should have:
A strong interest in financial markets and innovations such as cryptocurrencies and Blockchain.
High level of motivation and intellectual curiosity
A good background in numerical analysis with reasonable knowledge of and experience in quantitative methods (e.g. regression, VAR, cointegration)
(desirable) The ability to do programming in one or more applications (e.g. R, Matlab)
For more information please visit the NTU Doctoral School - Research Degrees webpages.
How to apply
Please visit our how to apply page for a step-by-step guide and make an application.
For informal enquiries about this project, please contact: Dr Lisa Siebers or Dr Michael Ehret on marketingPhDs@ntu.ac.uk (please put Dr Siebers’ or Dr Ehret’s name in the title line of your email).
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Guidance and support
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