BIO
Dr. Xi Chen has joined the University of Sussex Business School in November 2019 as a Lecturer in Finance. Before joining Sussex, she obtained her PhD from the ICMA Centre, Henley Business School, University of Reading in 2014 under the supervision of Professor Carol Alexander and Professor Charles Ward. Since then she worked for the University of Oxford as a (Visiting) Research Associate for a year and a half, and Oxford Risk Ltd (an Oxford University spin-out), as a Behavioural Scientist for four and a half years. She currently remains affiliated with Oxford Risk as an associate research fellow. Her research mainly focuses on generating insights about financial market movements and investor activities, and developing practically applicable innovations and technology.
Research Interests
Financial Markets, Asset Mispricing, Behavioural Finance, Real Options, DeFi and Crypto- currency, Machine Learning
Transferrable skills
Python, R, R Shiny, Matlab, Stata
English (Fluent), Chinese (Native)
PUBLICATIONS
Alexander, C., & Chen, X. (2021). Model risk in real option valuation. Annals of Operations Research 299, 1025-1056.
Alexander, C., Chen, X., & Ward, C. (2021). Risk-adjusted valuation for real option decisions. Journal of Economic Behavior & Organization 191, 1046-1064.
Alexander, C., Wei, W. & Chen, X. (2024). Matching Kollo measures. Journal of the Operational Research Society 75.7 (2024): 1279-1293. Github
WORKING PAPERS
Alexander, C., Deng, J., & Fu, Q. Impacts of Protocol Updates on Uniswap Decentralized Crypto Exchanges. R&R
Migration as an Option. (with McCann, P., Ward, C.)
Abstract: The analytical literature on interregional factor allocation, migration and productivity growth almost universally assumes a world in which people rent their homes. This allows for real wages to drive spatial labour mobility towards efficient outcomes which maximise interregional and national productivity. However, as we demonstrate in this paper, in a property-owning economy, real wages do not play the efficient factor allocation and mobility-driving roles that theoretical models assume. The reason is that mortgage-financed property transactions, and any subsequent wealth-accrual profoundly distort interregional factor allocation and migration behaviour towards very different outcomes from theoretical models. To examine this, we reframe the labour migration decision as a migration-housing decision and we focus on the behaviour of homeowners who borrow in order to finance the purchase of their home either in one region or another region, in a setting where nominal regional wages and house prices differ significantly between the two regions, even if real wages are equal. We adopt a real options approach in order to explore the sensitivities of key factors in determining the capacity and motivation to migrate over and above real wages. Our findings demonstrate that over the lifetime of the homeowner, the motivation to migrate from one region to another is radically affected by home ownership and the related effects of housing equity and pension fund accrual arising from different house prices across the regions. In particular, irrespective of real wage conditions, as the interregional differences in nominal house prices and nominal wages increase, migration from weaker regions to more prosperous regions becomes ever more financially difficult even where households wish to migrate, while migration from stronger regions to weaker is disincentivised for financial reasons, even where households might wish to migrate. In a property-owning economy, greater nominal interregional disparities discourage interregional migration.
Rarity Metrics for Non-Fungible Tokens. (with Alexander, C.)
Abstract: Within the new asset class of non-fungible tokens, personal profile picture (PFP) collections are avatars-with-benefits. Like Pok\'emon cards, the value of a token should depend on its rarity, indeed some very rare tokens have sold for several millions of dollars. However, each Pok\'emon card carries symbols from which its rarity can be determined at-a-glance, whereas the rarity of a PFP token must be measured from the characteristics defined in the metadata of the collection when it is minted to a blockchain. Unfortunately, there is nothing yet in the public domain that measures rarity correctly, except for a few special collections that have been designed to have independent traits. Although numerous metrics are now used by the PFP industry, different metrics can give vastly different results. This obscures any fundamental relationship between rarity and price and leads to great inefficiency in the PFP market. Our invariance results clarify the state-of-the-art, allowing classification of the numerous supposedly-different metrics into just four distinct cases, each a special case of weighted power mean. Importantly, neither the NFTGo Jaccard distance nor the OpenRarity Shannon entropy differ from the most commonly used metrics, i.e. the Pythagorean means, so they are not new, as claimed. We derive tests for trait independence, showing that most of the $\sim$200 PFP collections analysed have dependent traits, so none of these metrics is mathematically correct. For traders in PFPs we present two novel visualization tools, with code, which identify how discordant different rarity rankings can be, depending on the marketplace used.
A Dynamic BW Index of Stock Market Sentiment. (with Bian, J., Burgess, L., Deng, J. & Zhang, X.) Github
Abstract: We explore a dynamic design of market sentiment aggregation, augmenting Baker and Wurgler (2006)'s seminal index on the basis that aggregate sentiment must cover a wide range of investors or investment activities; such aggregation must adapt to changes in how different sentiment indicators reflect sentiment; and the aggregation must not be informed by future information. We demonstrate that each basis has a measurable impact. Ignoring these points when constructing models of sentiment can cause empirical effects to be over- or under-estimated and constrain the usability of the modelled sentiment in practical applications. Comparing the indices with market events, we show that our index is much more closely aligned with the boom and bust periods of the stock market, and much more significantly correlated with market-level variables in the expected direction. We also test our index against eleven anomaly portfolio returns and confirm the classic sentiment-induced mispricing effect on short legs, while observing increasing sentiment-induced mispricing on the long legs post-2000.
MODULE CONVENING
FinTech and Financial Transformation
Formerly, Introduction to FinTech
(Module developed, UG, Large group)
2019/20 -- Now
Programming in Finance
(Module developed, UG, Large group)
2022/23 -- Now
Portfolio Management
(Module taught, PG, Small group)
2019/20 -- 2021/22
Theory of Investment (Elective)
(Module taught, UG, Large group)
2019/20, 2020/21