In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model’s performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper.

We study the establishment and development of the most successful futures market in China. We document the evolution of the market microstructures and the attempts by the Zhengzhou Commodity Exchange's management to improve the performance of the futures market. Our finding suggests that if other former centrally planned economies want to establish futures markets, they must think carefully not only of the design and microstructure of the futures market, but they also have to choose carefully what contracts to offer.

In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data.

In this paper, we applied an explainable AI model (SHAP feature importance measures) to study the dynamic relationship between stock market returns and the US presidential election outcomes. More specifically, we wanted to study how the market would react the day after the election. AI models have been criticized as black-box models and lack the clarity needed for decision-making by different stakeholders. The explainable AI model we utilized in this model provides more clarity for the outcomes of the model. Using features commonly used by previous studies related to this topic, we find that the previous market direction leading up to the election and the incumbency information combined with the political affiliation are larger drivers for a 1-day post-election market return than sentiment and which party wins the election.

This paper derives a new formula for the price-earnings growth (PEG) ratio, utilizing the insight from Mario Farina’s original equation and Peter Lynch’s assertion that for a stock to be fairly-valued, the PEG and earnings growth rate has to be the same. After deriving the new formula, I demonstrate how the new formula connects with the existing formulas and P/E Ratio. The new formula allows for more flexibility on growth rate assumptions for both earnings and price, is more intuitive, is easier to implement, and can be used to make predictions about future price and earnings growth with current price and earnings. The new formula addresses some of the concerns regarding the usefulness of the PEG Ratio.