Financial Market Innovation and Product Innovation: Evidence from Commodity Futures Markets and Stock Markets

1 online resource (142 pages) : PDF

2021

University of North Carolina at Charlotte

This dissertation contains three connected essays that feature financial market innovation and product market innovation. Two essays feature return predictability in commodity futures, which have been financialized during the past two decades. One essay studies the relation between CEO’s external job market tournament and product innovation in the stock market. The first essay uses machine learning tools to study the serial dependence (lead-lag relations) of commodity futures returns. We use LASSO to select the predictors because the number of independent variables is large relative to the number of data points. We find significant full-sample and out-of-sample predictability. In the full sample, we find that LASSO can identify a sparse set of predictors that either come from economically linked commodities or are likely driven by excessive speculative trading. The out-of-sample forecasts based on LASSO generate statistically and economically large performance. When we use more complex machine learning models such as neural networks and regression trees to forecast commodity futures returns, the out-of-sample performance is worse than LASSO portfolios, suggesting that nonlinearities and interactions do not appear substantial in the data. We also find that index trading due to financialization drives the excess comovement among commodity futures. The second essay identifies a trend factor in commodity futures markets that exploits the short-, intermediate-, and long-run moving averages of settlement price in commodity futures markets. The trend factor generates statistically and economically large returns during the sample period 2004-2019. It beats the popular momentum factor by more than five times the Sharpe ratio and less downside risk. The trend factor cannot be explained by existing factor models and is priced cross-sectionally. Then we discover that the trend factor can be explained by funding liquidity measured by TED spread. Overall, the results indicate that there are significant economic benefits from using the information on historical prices in commodity futures markets. The third essay examines how the tournament-like progression in the CEO labor market influences corporate innovation strategies. By exploiting a text-based proxy for product innovation based on product descriptions from 10-Ks, we find that industry tournament incentives (ITIs) positively affect product innovation. We then explore the trade-off effects of ITIs on product innovation created through long-term patenting technologies and short-term "routine" product development. We discover that ITIs strengthen routine product development activities but decrease patent-based innovation.

doctoral dissertations

Finance

Ph.D.

Commodity Futures
Machine Learning
Product Innovation

Business Administration

Han, Yufeng

Chiang, I-Hsuan
Kirby, Christopher
Zillante, Arthur

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2021.

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Kong_uncc_0694D_12758

http://hdl.handle.net/20.500.13093/etd:2579