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The underlying assumption of using investor sentiment to predict stock prices, stock market returns, and liquidity is that of synergy between stock prices and investor sentiment. However, this synergistic relationship has received little attention in the literature. This paper investigates the synergistic pattern between stock prices and investor sentiment using social media messages from stock market investors and natural language processing techniques. At the macro level, we reveal extremely significant positive synergy between investor sentiment and stock prices. That is, when a stock price rises, investor sentiment rises, and when a stock price falls, investor sentiment falls. However, this synergy may be reversed or even disappear over a specific time period. Through a segmented measurement of the synergy between stock prices and investor sentiment over the course of a day, we also find that investor sentiment on social media is forward looking. This provides theoretical support for using investor sentiment in stock price prediction. We also examine the effect of lockdowns, the most draconian response to COVID-19, on synergy between stock prices and investor sentiment through causal inference machine learning. Our analysis shows that external anxiety can significantly affect synergy between stock prices and investor sentiment, but this effect can promote either positive or negative synergy. This paper offers a new perspective on stock price forecasting, investor sentiment, behavioral finance, and the impact of COVID-19 on the stock markets. Copyright (c) 2022 Borsa Istanbul Anonim S, irketi. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
RESUMEN
Purpose/Objective(s): To develop a tutoring program to help physician and physics residents to learn pancreas stereotactic body radiation therapy (SBRT) treatment planning via carefully collected cases and a series of specially designed knowledge models as teaching aid. Such programs are especially important during the Covid pandemic when most traditional hands-on medical teaching had to be moved online and asynchronous. Materials/Methods: The pilot tutoring program was composed of 5 teaching cases (1 benchmark case and 4 teaching cases), an interactive knowledge module (IKM) and a performance grading system. The tutoring program started with the benchmark case completed by each trainee independently to benchmark the baseline skill level. This same case was re-tested to evaluate the performance and clinical planning readiness after the trainee completes the tutoring program. 4 teaching cases were included: simple (1), intermediate (2) and complex (1). All 5 cases have simultaneous integrated boost prescription of 25Gy to the elective volume and 33Gy to the gross tumor volume. The IKM included a dose-volume prediction knowledge model for pancreas SBRT and is integrated via a graphic user interface in the treatment planning system. The trainee can seek reference and guidance from the IKM during learning – mimicking the hands-on tutoring of human expert. The grading system summarize the plan quality by weighing key dosimetric endpoints and their relative importance. Grade point average (GPA) was introduced to qualitatively appraise the plan quality into A, B and C (within 3%, 3-10% and > 10% difference of clinical plan score, corresponding to 4, 3, 2 point respectively). 5 trainees with minimum planning experience completed the teaching course. Results: Trainees achieved an average of 65.1% of total points (3.6 GPA) with 84 minutes planning time for the benchmark case pre-teaching, and improved to an average of 75.7% (4.2 GPA) using 48 minutes post-teaching. The clinical plan scored 72.7% of total points. All trainees improved their teaching plans’ scores after taking the virtual tutoring program. Post-teaching, all trainees received the GPA of A (clinical quality level) on the benchmark case planning. The total teaching time for each trainee ranged between 5 and 7 hours. Conclusion: The tutoring program with knowledge support modules provides encouraging learning outcomes in pancreas SBRT planning for inexperienced planners. This AI-enabled virtual teaching tool could provide valuable addition to the traditional human resource heavy in-person teaching of IMRT and SBRT treatment planning.