EDITORIAL STATEMENT IN HONOR OF PROFESSOR MICHAEL MCALEER

It is with profound sadness that we write this statement for the former editor of this journal, our colleague and friend, Michael McAleer. Mike passed away peacefully on July 8, 2021, and he will be sorely missed by his vast number of colleagues and friends. Mike served on the editorial board of the Annals of Financial Economics (AFE) for more than 16 years and was the Editor-in-Chief since 2016. Mike was a wonderful friend, colleague, and mentor to all that knew him, and provided countless hours of service to AFE. He touched our lives deeply and was ever ready to lend a hand in any way he could, whether through his vast knowledge of econometrics, his willingness to work together on research projects, his efforts on behalf of this journal, or his contagious joie de vivre. We will miss him greatly. In the remainder of this editorial, we include a short biography, as well as a number of statements from co-authors, colleagues and friends of Mike.

Research on enterprise financial economics early warning based on machine learning method

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

The Oil Futures and Options Markets in 2020: The “Message from Markets”

This paper considers the response of the equity and oil markets to the onset of crisis conditions after February 15, 2020. Based on derivative markets for equities and WTI (West Texas Intermediate) crude-oil futures contracts, implied equity and oil volatilities quantify the depth of the crisis and contrast it with the previous ones. The estimated Black [(1976) Journal of Financial Economics, 3, 167–179] vol skew and Merton [(1976) Journal of Financial Economics, 3, 125–144] option model parameters are able to discern between demand- and supply-side facets. The time when the futures curve is in contango identifies the beginning and, to date, conclusion of the crisis. Using the CAPM, co-movement of oil and equity prices permits computing forecasts of spot oil prices. In considering these events, we recognize the essential role of prices in financial markets: They are conveyors of information, the “Message from Markets,” in which financial theory proves useful, practical and applicable.