Asset pricing is the process of determining the value or price of a financial asset, like stocks, bonds or real estate. This involves understanding the factors that influence an asset’s value, including macroeconomic and market conditions, risk, supply and demand, in addition to the potential return an investor expects. Correctly evaluating asset prices based on economic fundamentals such as GDP or consumption growth is vital to avoid consequences from inflation to financial instability.
To explore this topic, Professor Adelphe Ekponon has been awarded a Social Sciences and Humanities Research Council Insight Development Grant for his research project titled “Asset prices, business cycles, and machine learning.”
This research aims to develop an online tool that can measure, visualize and track macroeconomic risk across various asset classes. Integrating business cycle risk, such as the transition between economic expansion and recession, in financial models, it will improve predictions of asset price movement, particularly for stocks, bonds and cryptocurrencies. The tool will combine economic and financial modelling with computational simulations to provide real-time predictions.
“I have recently realized that data scarcity which is one of the threats to research quality can be alleviated through textual analysis,” says Ekponon.
The research project will determine the real-time price of business cycle risk in the equity market and its impact on stock prices using machine learning, among other methods. It aims to improve understanding of market dynamics and risk pricing across different asset classes.
This research could provide a more realistic framework for understanding the economy and asset pricing. Individual and institutional investors, particularly those investing in equities, crypto-assets or others financial securities, will gain insights for making informed decisions. Academics, along with students, could have access to new empirical methods and frameworks, fostering potential collaborations. Additionally, government agencies, could use the findings to improve their risk modelling and decision-making regarding financial asset valuations and policy interest rates.
