• Booms, busts and heavy-tails: the story of Bitcoin and cryptocurrency markets?

      Fry, John (2018-10)
      We develop bespoke rational bubble models for Bitcoin and cryptocurrencies that incorporate both heavy tails and the probability of a complete collapse in asset prices. Empirically, we present robustified evidence of bubbles in Bitcoin and Ethereum. Theoretically, we show that liquidity risks may generate heavy-tails in Bitcoin and cryptocurrency markets. Even in the absence of bubbles dramatic booms and busts can occur. We thus sound a timely note of caution.
    • Elementary modelling and behavioural analysis for emergency evacuations using social media

      Fry, John; Binner, J.M. (2016-03)
      Social media usage in evacuations and emergency management represents a rapidly expanding field of study. Our paper thus provides quantitative insight into a serious practical problem. Within this context a behavioural approach is key. We discuss when facilitators should consider model-based interventions amid further implications for disaster communication and emergency management. We model the behaviour of individual people by deriving optimal contrarian strategies. We formulate a Bayesian algorithm which enables the optimal evacuation to be conducted sequentially under worsening conditions.
    • How easy is it to understand consumer finance?

      Burke, M.; Fry, John (2019-04)
      We consider the readability of payday loan websites against conventional lenders. Our findings show that credit card websites are harder to read and contain more complex terminology. Our central contribution is to provide the first known measurement of readability in consumer finance — something regulators have found helpful in other domains.
    • Managing performance expectations in association football

      Fry, John; Serbera, J-P.; Wilson, R.J. (2021-10)
      Motivated by excessive managerial pressure and sackings, together with associated questions over the inefficient use of scarce resources, we explore realistic performance expectations in association football. Our aim is to improve management quality by accounting for information asymmetry. Results highlight uncertainty caused both by football’s low-scoring nature and the intensity of the competition. At a deeper level we show that fans and journalists are prone to under-estimate uncertainties associated with individual matches. Further, we quantify reasonable expectations in the face of unevenly distributed resources. In line with the statactivist approach we call for more rounded assessments to be made once the underlying uncertainties are adequately accounted for. Managing fan expectations is probably impossible though the potential for constructive dialogue remains.
    • Modelling corporate bank accounts

      Fry, John; Griguta, V.; Gerber, L.; Slater-Petty, H.; Crockett, K. (2021)
      We discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer a principled treatment of a genuine industrial problem. The corporate bank accounts in our study constitute spare, irregularly-spaced time series that may take both positive and negative values. We thus builds on previous models where the underlying is real-valued. We describe an intra-monthly effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and end of each month and highest in the middle. However, our theory also allows for the opposite effect to occur. In-sample applications demonstrate the statistical significance of the hypothesised monthly effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA approach.
    • Negative bubbles and shocks in cryptocurrency markets

      Fry, John; Cheah, E-T. (2016-10)
      In this paper we draw upon the close relationship between statistical physics and mathematical finance to develop a suite of models for financial bubbles and crashes. The derived models allow for a probabilistic and statistical formulation of econophysics models closely linked to mainstream financial models. Applications include monitoring the stability of financial systems and the subsequent policy implications. We emphasise the timeliness of our contribution with an application to the two largest cryptocurrency markets: Bitcoin and Ripple. Results shed new light on emerging debates over the nature of cryptocurrency markets and competition between rival digital currencies.
    • An options-pricing approach to election prediction

      Fry, John; Burke, M. (2020-10)
      The link between finance and politics (especially opinion polling) is interesting in both theoretical and empirical terms. Inter alia the election date corresponds to the effective price of an underlying at a known future date. This renders a derivative pricing approach appropriate and, ultimately, to a simplification of the approach suggested by Taleb (2018). Thus, we use an options-pricing approach to predict vote share. Rather than systematic bias in polls forecasting errors appear chiefly due to the mode of extracting election outcomes from the share of the vote. In the 2016 US election polling results put the Republicans ahead in the electoral college from July 2016 onwards. In the 2017 UK general election, though set to be the largest party, a Conservative majority was far from certain.
    • Quantifying the sustainability of Bitcoin and Blockchain

      Fry, John; Serbera, J-P. (2020)
      Purpose: We develop new quantitative methods to estimate the level of speculation and long-term sustainability of Bitcoin and Blockchain. Design/Methodology/Approach: We explore the practical application of speculative bubble models to cryptocurrencies. We then show how the approach can be extended to provide estimated brand values using data from Google Trends. Findings: We confirm previous findings of speculative bubbles in cryptocurrency markets. Relatedly, Google searches for cryptocurrencies seem to be primarily driven by recent price rises. Overall results are sufficient to question the long-term sustainability of Bitcoin with the suggestion that Ethereum, Bitcoin Cash and Ripple may all enjoy technical advantages relative to Bitcoin. Our results also demonstrate that Blockchain has a distinct value and identity beyond cryptocurrencies - providing foundational support for the second generation of academic work on Blockchain. However, a relatively low estimated long-term growth rate suggests that the benefi ts of Blockchain may take a long time to be fully realised. Originality/value: We contribute to an emerging academic literature on Blockchain and to a more established literature exploring the use of Google data within business analytics. Our original contribution is to quantify the business value of Blockchain and related technologies using Google Trends.
    • Regional bias when benchmarking services using customer satisfaction scores

      Brint, A.; Fry, John (2021)
      Regional monopoly service organisations such as electricity, gas and water distributors, health trusts, public transport, and local government are subject to regulatory oversight. A common element in this is benchmarking an organisation against similar organisations based in different regions. Customer satisfaction is often an important part of this competitive benchmarking. However, if people from different regions give a different average satisfaction score for the same experience, then this disadvantages some companies. Therefore, regional satisfaction was investigated in an environment where differences in customer service levels are controlled for. The average online satisfaction ratings people from different regions of the UK gave to the same overseas holiday hotels were investigated. The 24,154 ratings were analysed using linear mixed effects and ordinal models. The average ratings given by people from the London region were significantly lower than those from elsewhere. Regional correction factors are developed and applied to published satisfaction ratings for electricity distributors. The adjustment was sufficient to move the London distributor from the penalty category to a borderline position. Hence, customer satisfaction ratings should be used cautiously when benchmarking regional organisations. This investigation of the potential for regional bias contributes to the large literature on customer satisfaction and behavioural intentions.
    • Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin

      Cheah, E-T.; Fry, John (2015-05)
      Amid its rapidly increasing usage and immense public interest the subject of Bitcoin has raised profound economic and societal issues. In this paper we undertake economic and econometric modelling of Bitcoin prices. As with many asset classes we show that Bitcoin exhibits speculative bubbles. Further, we find empirical evidence that the fundamental price of Bitcoin is zero.
    • Takeover deterrents and cross partial ownership: the case of golden shares

      Serbera, J-P.; Fry, John (2019-04)
      We analyse takeovers in an industry with bilateral capital‐linked firms in cross partial ownership (CPO). Before merger, CPO reduces the profitability of involved firms, confirming the “outsider effect.” However, the impact of CPO upon merger profitability is two‐sided in a Cournot setting. CPO, by cointegrating profits, increases output collusion leading to anticompetitive effects with facilitated mergers in most cases. Nonetheless, a protective threshold exists for which CPO arrangements can reduce the incentives for hostile takeovers. This has potentially significant regulatory implications. An illustrative example showcases the potential relevance of CPO as a defence against hostile takeovers across different industries.
    • The valuation of no-negative equity guarantees and equity release mortgages

      Dowd, K.; Buckner, D.; Blake, D.; Fry, John (2019-11)
      We outline the valuation process for a No-Negative Equity Guarantee in an Equity Release Mortgage loan and for an Equity Release Mortgage that has such a guarantee. Illustrative valuations are provided based on the Black ’76 put pricing formula and mortality projections based on the M5, M6 and M7 mortality versions of the Cairns–Blake–Dowd (CBD) family of mortality models. Results indicate that the valuations of No-Negative Equity Guarantees are high relative to loan amounts and subject to considerable model risk but that the valuations of Equity Release Mortgage loans are robust to the choice of mortality model. Results have significant ramifications for industry practice and prudential regulation.
    • A Variance Gamma model for Rugby Union matches

      Fry, John; Smart, O.; Serbera, J-P.; Klar, B. (2021)
      Amid much recent interest we discuss a Variance Gamma model for Rugby Union matches (applications to other sports are possible). Our model emerges as a special case of the recently introduced Gamma Difference distribution though there is a rich history of applied work using the Variance Gamma distribution – particularly in finance. Restricting to this special case adds analytical tractability and computational ease. Our three-dimensional model extends classical two-dimensional Poisson models for soccer. Analytical results are obtained for match outcomes, total score and the awarding of bonus points. Model calibration is demonstrated using historical results, bookmakers’ data and tournament simulations.
    • Would two-stage scoring models alleviate bank exposure to bad debt?

      Abdou, H.A.; Mitra, S.; Fry, John; Elamer, Ahmed A. (2019-08-15)
      The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multi-layer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures.