Recently, Lirong Gan, a Ph.D. student from the Department of Finance at the Southern University of Science and Technology (SUSTech), published her research article entitled “Machine learning solutions to challenges in finance: An application to the pricing of financial products” in Technological Forecasting and Social Change, one of the famous journals in SSCI. This paper proposes a machine-learning method to price arithmetic and geometric options accurately and quickly. The method is verified by empirical applications as well as numerical experiments.

In the field of finance, the return of Asian options is not sensitive to the change of the underlying asset price on the maturity date. To avoid manipulating asset prices and option prices, investors are willing to invest in Asian options. However, the traditional Asian option pricing needs a lot of numerical calculation, which is biased and time-consuming.

This paper presents a machine learning method for pricing two kinds of Asian options quickly and accurately. The machine learning method does not need specific constraints, nor does it rely on the classical Black Scholes option pricing model. The effectiveness of the machine learning method in Asian option pricing is verified by the actual data, which is helpful for investment managers or traders in the financial industry to quickly evaluate the option prices in the market and make the right investment choices in time.

Lirong Gan from SUSTech is the first author of this paper. Associate Professor Zhaojun Yang from the Department of Finance at SUSTech and Associate Professor Huamao Wang from the Department of Finance at the University of Nottingham, are the co-authors of the paper. The research was supported by the Guangdong Planning Office of Philosophy and Social Science, and the Colleges Innovation Project of Guangdong.

Lirong Gan was the only national scholarship winner for doctoral students in her major in Mathematics in 2020. The Department of Finance at SUSTech attaches great importance to personnel training, and a group of growing doctoral students from the Department of Finance are playing an increasingly important role in scientific research.

Article link: https://www.sciencedirect.com/science/article/pii/S0040162519312399