In this project network models are developed for the structure identification within complex systems/networks. The goal is an accurate identification of the important network nodes. The models are suited to uncover the lead-lag relationship between the flow of funds, company networks, asset return relationship, etc. In this project the LISAR, TriSNAR and DINAR models were developed and the papers study the geographical lead-lag relationship between blockchain transactions as well as Bitcoin pricing series on various exchanges all over the globe. 

1. Trimborn, S., H. Peng, and Y. Chen (2024) Influencer detection meets network autoregression - Influential regions in the bitcoin blockchain, Journal of Empirical Finance, 78, 101529, https://doi.org/10.1016/j.jempfin.2024.101529 

2. Zhang, K. and S. Trimborn (2024) Influential assets in Large-Scale Vector AutoRegressive Models, https://dx.doi.org/10.2139/ssrn.4619531 

3. Hui, Y., I. Zwetsloot, S. Trimborn, and S. Rudinac (2024) Domain-Informed Negative Sampling Strategies for Dynamic Graph Embedding in Meme Stock-Related Social Networks, https://arxiv.org/abs/2411.00606

4. Chen, Y., P. Guidici, B. Hadji Misheva, S. Trimborn (2020) Detecting Lead Behavior in Crypto Networks Risks, 8(1), 4, https://doi.org/10.3390/risks8010004 

5. Trimborn, S., Y. Chen, R.-B. Chen (2020) TriSNAR: A Three-Layer Sparse Estimator for Large-Scale Network AutoRegressive Models, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3573336

6. R-package NetVAR

In this project network models are developed for the analysis of social networks. Of particular interest are the ones focusing upon meme stocks. The goal is to investigate the impact social network users have upon the return dynamics of meme stocks. 

1. Trimborn, S. and I. Zwetsloot (2024) Reddit users unleashed-understanding user behaviour and their impact on meme stocks, https://ssrn.com/abstract=4886074 

2. Wang, Z., S. Hao, I. Zwetsloot and S. Trimborn (2024) Social Network Datasets on Reddit Financial Discussion, https://arxiv.org/abs/2410.05002

3. Hui, Y., I. Zwetsloot, S. Trimborn, and S. Rudinac (2024) Domain-Informed Negative Sampling Strategies for Dynamic Graph Embedding in Meme Stock-Related Social Networks, https://arxiv.org/abs/2411.00606

In this project we investigate the impact environmental regulation has upon firm valuations. The project informs investors and regulators how to release regulation to minimise effects on companies. We further investigate the risk networks in renewable energy markets. 

In this project text data are analysed for their sentiment and the corresponding effect on asset returns. Topic Modeling is performed to uncover the shared topics market participants discuss and their dynamic relation to the market state. At the moment the project focuses upon Twitter data and the effect of experts on market returns and market states. 

1. Trimborn, S. , Y. Li (2021) Informative Effects of Expert Sentiment on the Return Predictability of Cryptocurrency, http://dx.doi.org/10.2139/ssrn.3834279

 In several papers investment methods for the cryptocurrency market are developed and/or tested, as well as the relationship of cryptocurrencies with established assets is investigated. Cryptocurrencies have little correlation with establisted assets, which makes them an attractive asset for portfolio diversification. Though the high volatility and frequent jumps also adds considerable risk to a portfolio when investing with cryptocurrencies. Besides the market risk, cryptocurrencies are often also subject to low liquidity, which adds another layer of risk. An investment methodology which incorporates market risk (volatility/quantiles) and liquidity risk is developed, called LIBRO. 

1. Petukhina, A., S. Trimborn, W.K. Härdle, and H. Elendner (2021) Investing with Cryptocurrencies – Evaluating their Potential for Portfolio Allocation Strategies, Quantitative Finance, 1-29, https://doi.org/10.1080/14697688.2021.1880023

2. Trimborn, S., M. Li and W.K. Härdle (2019) Investing with Cryptocurrencies – A Liquidity Constraint Investment Approach Journal of Financial Econometrics, 18 (2), 280-306, doi.org/10.1093/jjfinec/nbz016 Code: GitHub

3. Elendner, H., S. Trimborn, B. Ong and T.M. Lee (2017) The Cross-Section of crypto-currencies as financial assets Handbook of Digital Finance and Financial Inclusion: Cryptocurrency, FinTech, InsurTech, and Regulation. Ed. by D. Lee Kuo Chuen and R. Deng. Vol. 1. Elsevier, https://doi.org/10.1016/B978-0-12-810441-5.00007-5 Code: GitHub

The market of cryptocurrencies changes its structure frequently. New coins arise on a daily level, while many coins are no longer used after some time. This destiny is shared by small as well as previously large coins. The construction of indices is challenged by the frequently changing market structure. In this project a methodology for a cryptocurrency index, CRIX, is developed as well as for a Volatility CRIX, called VCRIX. Both indices were sold in 2021 to Royalton Partners and are available on https://www.royalton-crix.com/ with the derivations done by S&P Global. I serve on the Scientific Board for the Royalton CRIX index.

1. Trimborn, S. and W.K. Härdle (2018) CRIX an Index for cryptocurrencies Journal of Empirical Finance, 49, 107-122, https://doi.org/10.1016/j.jempfin.2018.08.004

2. Kim, A., S. Trimborn, W.K. Härdle (2021) VCRIX – a volatility index for crypto-currencies, International Review of Financial Analysis, 78, 101915, https://doi.org/10.1016/j.irfa.2021.101915 

3. R-package IndexConstruction

4. Website: https://www.royalton-crix.com/