Forecasting Earnings and Returns

Subscribe to newsletter

Data science and machine learning have made great progress in the past few years. They are being applied successfully in many areas such as computer vision, natural language processing, and predictive analytics.

In the financial market, however, there are still many uncertainties and risks that the new technology cannot predict. The difficulty in forecasting the financial market is due to the unpredictable nature of financial data, a low signal-to-noise ratio in available variables, and model uncertainty. Specifically, financial time series are notoriously non-stationary, and the model parameters are often unstable.

Reference [1] provided an overview of the current state of research on forecasting earnings and returns. It pointed out,

Subscribe to newsletter https://harbourfrontquant.substack.com/ Newsletter Covering Trading Strategies, Risk Management, Financial Derivatives, Career Perspectives, and More

Following prior research, we highlight three major challenges for a forecaster when working with financial data: unpredictability of earnings and returns, noisy X variables, and model uncertainty. Using these challenges as a way to organize the literature, we discuss recent research that advances our collective ability to understand and predict the cross-sections of earnings and returns.

Here we reiterate some important insights from the literature. First, even with recent advancements, finding new meaningful predictors remains an important effort. Second, new out-of-the-box methods may have limited usefulness, but the thoughtful use of estimation methods and constraints seems to present promising opportunities. Third, it continues to be the case that finding earnings predictors that provide better forecasts than lagged earnings is challenging. Fourth, sorting through, combining, and understanding different models and methods likely has a long way to go before we achieve anything close to recommended best practices.

In short, forecasting the financial market is still a challenging task. In our opinion, most of the new forecasting methodologies that use machine learning were developed without good domain knowledge. It’s not a surprise that they do not perform well.

Let us know what you think in the comments below.

References

[1] Green, Jeremiah and Zhao, Wanjia, Forecasting Earnings and Returns: A Review of Recent Advancements. https://ssrn.com/abstract=4033164

Further questions

What's your question? Ask it in the discussion forum

Have an answer to the questions below? Post it here or in the forum

LATEST NEWSOil Steadies at End of Volatile Week as US and Iran Keep Talking
Oil Steadies at End of Volatile Week as US and Iran Keep Talking

Oil steadied at the end of a bumpy week, as talks between the US and Iran continued despite a flare-up in fighting that drove a steep drop in traffic through the Strait of Hormuz.

Stay up-to-date with the latest news - click here
LATEST NEWSSK Hynix edges higher after pricing $26.5 billion U.S. ADR offering
SK Hynix edges higher after pricing $26.5 billion U.S. ADR offering
Stay up-to-date with the latest news - click here
LATEST NEWSMorning Bid: Japan calling capital home
Morning Bid: Japan calling capital home
Stay up-to-date with the latest news - click here
LATEST NEWSWhy is Fast Retailing stock sliding today?
Why is Fast Retailing stock sliding today?
Stay up-to-date with the latest news - click here
LATEST NEWSVeteran Banker Christina Tonkin to Leave ANZ at End of September
Veteran Banker Christina Tonkin to Leave ANZ at End of September

Christina Tonkin, managing director of corporate finance at ANZ Group Holdings Ltd.’s institutional banking division, will leave the firm at the end of September after over 20 years of service, according to people familiar with the matter.

Stay up-to-date with the latest news - click here

Leave a Reply