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<span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px;" class="">This is an announcement of Jingwen Jiang's Dissertation Defense.</span>
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Candidate: Jingwen Jiang</div>
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Date: Tuesday, December 06, 2022</div>
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Time: 1 pm CST</div>
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Remote Location: <a href="https://uchicago.zoom.us/j/97772156520?pwd=bGl5QnByWTA5Vk8rbjdsUitMYWhzdz09" class="">https://uchicago.zoom.us/j/97772156520?pwd=bGl5QnByWTA5Vk8rbjdsUitMYWhzdz09</a></div>
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Title: Expected Returns and Foundation Models of Language</div>
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Abstract: We extract contextualized representations of news text to predict returns using the state-of-the-art foundation models in natural language processing. The contextualized representation of news reflects its content more accurately than the bag-of-words
representation prevalent in the literature. In particular, the latter approach is more prone to errors when negation words appear in news articles. Moreover, we provide polyglot evidence on news-induced return predictability in 16 international equity markets
with news written in 13 different languages. Information in newswires is assimilated into prices with an inefficient delay that is broadly consistent with limits-to-arbitrage, yet can be exploited in a real-time trading strategy. Furthermore, a trading strategy
that exploits fresh news in the form of news alerts leads to even higher Sharpe ratios.</div>
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Advisors: Dacheng Xiu</div>
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Co-advisor: Janos Simon</div>
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<span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px;" class="">Committee Members: Dacheng Xiu, Janos Simon, and Chenhao Tan</span>
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