This study presents evidence that much of the trading on macro-economic news occurs prior to the scheduled news announcement times. Examining the trading patterns ahead of the ISM Manufacturing Index and Construction Spending announcement, we find that the trading on the not-yet-publicly...

Using Artificial Intelligence (AI) techniques, we quantitatively examine the mentions of ESG terms in the U.S. corporate filings with the SEC over the 2019-20 period. We find that in our sample of companies, the management tends to focus ESG discussions in corporate filings along three...

Corporate disclosures have been studied for many years. We add a new dimension to the analysis: we show that the filing frequency of disclosures has a negative impact on future stock returns. Specifically, we show that every additional filing within the past month preceding a given filing...

The crypto market is growing at an unprecedented rate, but it can be difficult to make money in this market if you don't understand how it works. Most people are still using traditional methods to trade in the crypto market, which can lead to losses due to significantly adverse pricing, known as...

Six key problems are known to plague traditional econometric analysis and much of pure machine learning: 1) overfitting, 2) inability to deal with noise in the data, 3) difficulties dealing with missing observations, 4) data non-stationarity, 5) researcher ability and creativity, and 6)...

Traditional econometrics and even machine learning models suffer from three key problems hampering many an analysis: noise, non-stationarity and model overfitting. This paper shows how artificial intelligence helps overcome the issues embedded in traditional econometric models and even...

Using daily returns on 34 futures contracts over the 2010-2022 period, we examine the factors driving these returns. We show that all commodities can be grouped by their drivers into intuitive groups based on their factorization into 1) food, 2) metals and oil and 3) precious metals. The three...

We show a simple way to let the data speak for itself. Specifically, we show how a large mixed bag of data, potentially embedded with missing data points and collinearities, and therefore unsuitable for traditional econometric analysis, can be useful in building fast and meaningful big data and...

We show a simple way to let the data speak for themselves. Specifically, we show how a large mixed bag of data, potentially embedded with missing data points and collinearities, and therefore unsuitable for traditional econometric analysis, can be useful in building fast and meaningful big data...