Seminars hosted by the Finance & Accounting area

Seminars hosted by the Finance & Accounting area

The Finance & Accounting area hosts seminars/ webinars, regularly. On June 18th, Professor Gideon Saar, from SC Johnson College of Business, spoke om ‘Limit Order Markets under Asymmetric Information’. Prof. Saar and his fellow researchers have developed a model of dynamic limit order markets under asymmetric information that can be simplified enough to be solved analytically. They use the trader arrival and information environment of the traditional sequential trade models but swap the dealer-based trading core of these models for a dynamic limit order market. They find that informed traders tend to “make” liquidity in illiquid markets and “take” liquid-ity from more liquid markets. The arrival of marketable and limit orders as well as the passage of time may convey information, resulting in repricing of orders in the book and generating the frequent cancellations and resubmissions that have become a staple of modern markets.

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The F&A area hosted a seminar on ‘Understanding Sentiment Through Context’, by Professor Richard Crowley, from the School of Accountancy Singapore Management University, on June 11th.

Prof. Crowley and his fellow researchers examine the extent to which results based on financial sentiment of U.S. annual reports are conditional on the underlying context from which financial sentiment is derived, as well as the extent to which financial sentiment is related to the underlying context of the annual report. To achieve this, they construct a measure of context that is based on the grammar, syntax, and content of sentences in each report. They then apply sentiment measures to the phrases within each context to examine how sentiment is related to each context, and under which contexts financial sentiment works as expected or not for a variety of prediction problems. They show that sentiment encompasses a wide variety of contexts, and that positive and negative sentiment respond to different contexts. In addition, They show that there is significant noise in predicting various outcomes (stock return, volume, volatility, and material weaknesses). Specifically, only select contexts drive the primary results of each analysis, and these select contexts vary by the outcome being predicted. Furthermore, under some contexts they find results opposite to expected predictions, indicating a nontrivial amount of systematic noise or error in sentiment classification.

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