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Machine learning in finance: A metadata-based systematic review of the literature

Warin, Thierry; Stojkov, Aleksandar - In: Journal of Risk and Financial Management 14 (2021) 7, pp. 1-31

Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper's central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine...

Persistent link: https://www.econbiz.de/10013200986

COVID-19 pandemic and financial contagion

Chevallier, Julien - In: Journal of Risk and Financial Management 13 (2020) 12, pp. 1-25

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of...

Persistent link: https://www.econbiz.de/10012611536

Do post-Corona European economic policies lift growth prospects? Exploring an ML-methodology

Herzog, Bodo - In: Journal of Risk and Financial Management 15 (2022) 3, pp. 1-13

This article explores the determinants of people's growth prospects in survey data as well as the impact of the European recovery fund to future growth. The focus is on the aftermath of the Corona pandemic, which is a natural limit to the sample size. We use Eurobarometer survey data and...

Persistent link: https://www.econbiz.de/10013201423

Credit risk model based on central bank credit registry data

Doko, Fisnik; Kalajdziski, Slobodan; Mishkovski, Igor - In: Journal of Risk and Financial Management 14 (2021) 3, pp. 1-17

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to model the probability of...

Persistent link: https://www.econbiz.de/10012611695

The rich get richer and the poor get poorer: Social media and the post-IPO behavior of investors in biotechnology firms. The relationship with Twitter volume

Siev, Smadar - In: Journal of Risk and Financial Management 14 (2021) 10, pp. 1-17

of social media-specifically, Twitter-on these returns. The results indicate positive yet insignificant cumulative …-IPO years. An examination of Twitter activity and share returns demonstrates a robust correlation between the two, suggesting … that investors' attention to firms may be reflected in their Twitter usage. …

Persistent link: https://www.econbiz.de/10013201140

Threshold of depression measure in the framework of sentiment analysis of tweets: Managing risk during a crisis period like the COVID-19 pandemic

Mba, Jules Clement; Biyase, Mduduzi - In: Journal of Risk and Financial Management 16 (2023) 2, pp. 1-12

The COVID-19 pandemic has had a devastating impact on the world. The surge in the number of daily new cases and deaths around the world and in South Africa, in particular, has increased fear, psychological breakdown, and uncertainty among the population during the COVID-19 pandemic period,...

Persistent link: https://www.econbiz.de/10014332862

Content analysis of passengers' perceptions of airport service quality: The case of Honolulu International Airport

Bae, Wonmi; Chi, Junwook - In: Journal of Risk and Financial Management 15 (2022) 1, pp. 1-19

This paper explores passengers' perceptions toward airport service quality through a content analysis. Using 1341 review comments posted on the Skytrax website, we identify satisfiers, dissatisfiers, and performance factors that determine passengers' experiences at the Honolulu International...

Persistent link: https://www.econbiz.de/10013201309

Machine learning applications to land and structure valuation

Mayer, Michael; Bourassa, Steven C.; Hoesli, Martin; … - In: Journal of Risk and Financial Management 15 (2022) 5, pp. 1-24

In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a "black box". An important example is hedonic property valuation modeling, where machine learning techniques...

Persistent link: https://www.econbiz.de/10014332394

Deep partial hedging

Hou, Songyan; Krabichler, Thomas; Wunsch, Marcus - In: Journal of Risk and Financial Management 15 (2022) 5, pp. 1-5

Using techniques from deep learning, we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by Föllmer and Leukert. Not only does this approach better accommodate the realistic setting of...

Persistent link: https://www.econbiz.de/10014332424

A machine learning framework towards bank telemarketing prediction

Koumétio Tékouabou, Stéphane Cédric; Gherghina, … - In: Journal of Risk and Financial Management 15 (2022) 6, pp. 1-19

The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in...

Persistent link: https://www.econbiz.de/10014332470