Effects of COVID-19 on Football Stock Market’s Return and Uncertainty

Bedir, Fatih; Bozma, Gürkan; Turan, Metin; Mizrak, Orcan

doi:10.32731/ijsf/173.082022.02

Cited by 4 publications

(1 citation statement)

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“…The postponement of football competitions to prevent the spread of COVID-19 led to reduced revenues for clubs, and for those actively involved in the stock market, a decrease in revenues could cause the potential for a decline in share prices. This fact leads to volatility in the share prices of European football clubs (Bedir et al, 2022). So, given the insecurity in the investment of assets in the football industry, more research and instruments are required to accurately estimate the value of its stocks and to forecast the rate of return of an asset.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for estimating Macro Asset Pricing model in football clubs

Alaminos

Esteban

Salas

2023

Intell Sys Acc Fin Mgmt

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Summary The recent crisis caused by COVID‐19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non‐linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price‐dividend ratio. Long‐term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry.

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Section: Introductionmentioning

confidence: 99%