Browsing by Subject "Trading"
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Item Crunch the market : a Big Data approach to trading system optimization(2013-12) Mauldin, Timothy Allan; Aziz, AdnanDue to the size of data needed, running software to analyze and tuning intraday trading strategies can take large amounts of time away from analysts, who would like to be able to evaluate strategies and optimize strategy parameters very quickly, ideally in the blink of an eye. Fortunately, Big Data technologies are evolving rapidly and can be leveraged for these purposes. These technologies include software systems for distributed computing, parallel hardware, and on demand computing resources in the cloud. This report presents a distributed software system for trading strategy analysis. It also demonstrates the effectiveness of Machine Learning techniques in decreasing parameter optimization workload. The results from tests run on two different commercial cloud service providers show linear scalability when analyzing intraday trading strategies.Item Determinants of mutual fund flows(2011-05) Gallaher, Steven Timothy; Starks, Laura T.; Titman, Sheridan; Almazan, Andres; Anderson, Edward; Hartzell, JayI investigate mutual fund flows at the individual fund and at the fund family level. At the individual, I use SEC filings to decompose fund flows into inflows and outflows. This decomposition of net flows into its component parts provides a way to examine differences in how search costs and investor learning affect investors who are entering a fund (or adding to their investments) versus those investors who are leaving a fund (or decreasing their investments). I then examine the effect of the existence of an advertisement for the fund on these investors. At the mutual fund family level, I examine how the characteristics and performance of mutual fund families affect the flows to the family as a whole. I then examine the effects of advertising expenditures on flows to the fund family.Item The determinants and trading consequences of automated information acquisition(2023-12) Kettell, Laura; McInnis, John M.; Bauguess, Scott; Chen, Shuping; Koonce, Lisa; Zhao, WuyangI examine the determinants and trading consequences of automated acquisition of financial reports. Prior literature predominately studies human-based information acquisition, yet capital market participants are increasingly relying on automation to acquire financial reports. Using a novel approach to identify automated downloads of 10-Ks, I find strong evidence that automated and human downloads are positively correlated, suggesting that humans’ own interests influence how machines are programmed. I also find that machines selectively download the 10-Ks for firms with higher expected information frictions and/or misvaluation (e.g., smaller, value firms with complex filings). Using staggered XBRL adoption as an instrument for automated downloads, I find that automated downloads increase trading volume, particularly for firms with information frictions. However, I do not find evidence that automated downloads impact price movements or price efficiency. Taken together, the evidence suggests that automated acquisition of 10-Ks leads to differential interpretations, resulting in more trading but not necessarily more informed trading.