Profitable opportunities around macroeconomic announcements in the U.S. Treasury market

This thesis studies the impact of macroeconomic announcements on the U.S. Treasury market and investigates profitable opportunities around macroeconomic announcements using data from the eSpeed electronic trading platform. We investigate how macroeconomic announcements affect the return predictability of trade imbalance for the 2-year, 5-year, IO-year U.S. Treasury notes and 30-year U.S. Treasury bonds. The goal of this thesis is to develop a methodology to identify informed trades and estimate the trade imbalance based on informed trades. We use the daily order book slope as a proxy for dispersion of beliefs among investors. Regression results in this thesis indicate that, on announcement days with a high dispersion of beliefs, daily trade imbalance estimated by informed trades significantly predicts returns on the following day. In addition, we develop a trade-imbalance based trading strategy conditional on dispersion of beliefs, informed trades, and announcement days. The trading strategy yields significantly positive net returns for the 2-year T-notes.

A Shift in Role: The Perspectives of Ontario Secondary Principals Working in Data-Driven Environments

This study occurred in 2009 and questioned how Ontario secondary school principals perceived their role had changed, over a 7 year period, in response to the increased demands of data-driven school environments. Specifically, it sought to identify principals' perceptions on how high-stakes testing and data-driven environments had affected their role, tasks, and accountability responsibilities. This study contextualized the emergence of the Education Quality and Accountability Offices (EQAO) as a central influence in the creation of data-driven school environments, and conceptualized the role of the principal as using data to inform and persuade a shift in thinking about the use of data to improve instruction and student achievement. The findings of the study suggest that data-driven environments had helped principals reclaim their positional power as instructional leaders, using data as an avenue back into the classroom. The use of data shifted the responsibilities of the principal to persuade teachers to work collaboratively to improve classroom instruction in order to demonstrate accountability.

The time-course of lexical activation in Japanese morphographic word recognition: Evidence for a character-driven processing model

This lexical decision study with eye tracking of Japanese two-kanji-character words investigated the order in which a whole two-character word and its morphographic constituents are activated in the course of lexical access, the relative contributions of the left and the right characters in lexical decision, the depth to which semantic radicals are processed, and how nonlinguistic factors affect lexical processes. Mixed-effects regression analyses of response times and subgaze durations (i.e., first-pass fixation time spent on each of the two characters) revealed joint contributions of morphographic units at all levels of the linguistic structure with the magnitude and the direction of the lexical effects modulated by readers’ locus of attention in a left-to-right preferred processing path. During the early time frame, character effects were larger in magnitude and more robust than radical and whole-word effects, regardless of the font size and the type of nonwords. Extending previous radical-based and character-based models, we propose a task/decision-sensitive character-driven processing model with a level-skipping assumption: Connections from the feature level bypass the lower radical level and link up directly to the higher character level.

Macroeconomic News and LOB in Foreign Exchange ECN Market

We investigate the macroeconomic news effect on the dynamics of the limit order books (LOB) for euro-dollar ECN market in different economic states between Jan. 2006 to Dec. 2009. Using a VAR-STR model on the news surprise, pure news, aggregated good and bad news, we show that news effects on the LOB dynamics vary in different states of economy. The LOB dynamics are measured by depth, spread, slope and volatility. In contract to slope and volatility, depth and spread strongly respond to news surprise and pure news during recession and expansion. These characteristics are more affected by aggregated good and bad news during expansion. News effects are robust to alternative characteristic measures, the different sides of the LOB and the different levels in the LOB.