THE IMPACT OF INTRADAY MOMENTUM ON STOCK RETURNS: EVIDENCE FROM S&P500 AND CSI300

This paper analyzes the statistical impact of COVID-19 on the S&P500 and the CSI300 intraday momentum. This study employs an empirical method, that is, the intraday momentum method used in this research. Also, the predictability of timing conditional strategies is also used here to predict the intraday momentum of stock returns. In addition, this study aims to estimate and forecast the coefficients in the stock market pandemic crisis through a robust standard error approach. The empirical findings indicate that the intraday market behavior an unusual balanced; the volatility and trading volume imbalance and the return trends are losing overwhelmingly. The consequence is that the first half-hour return will forecast the last half-hour return of the S&P500, but during the pandemic shock, the last half-hour of both stock markets will not have a significant impact on intraday momentum. Additionally, market timing strategy analysis is a significant factor in the stock market because it shows the perfect trading time, decides investment opportunities and which stocks will perform well on this day. Besides, we also found that when the volatility and volume of the S&P500 are both at a high level, the first half-hour has been a positive impact, while at the low level, the CSI300 has a negative impact on the last half-hour. In addition, this shows that the optimistic effect and positive outlook of the stockholders for the S&P500 is in the first half-hours after weekend on Monday morning because market open during the weekend holiday, and the mentality of every stockholder’s indicate the positive impression of the stock market.

Registering content with Crossref or DataCite

This blog started registering DOIs for its content with Crossref last week, and all 450+ blog posts so far were registered by Monday morning. This enables the easy import into reference managers (here using Zotero):Zotero entryUsing Zotero or any other ...

The sleep loss insult of Spring Daylight Savings in the US is observable in Twitter activity

Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use “Spring Forward” events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs 15 to 30 min later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that the window of sleep opportunity is compressed in Twitter data, revealing Spring Forward behavioral change.

Heart Rate Variability in Elite Swimmers before, during and after COVID-19 Lockdown: A Brief Report on Time Domain Analysis

Background: Many athletes worldwide have endured home confinement during the COVID-19 pandemic, and their opportunities to train were strongly limited. This study describes the impact of lockdown on training volume and heart rate variability (HRV) in elite swimmers. Methods: HRV data of seven elite males were collected each Monday morning over 20 weeks, including 8 weeks of lockdown. The training volume was quantified retrospectively. Results: During the lockdown period (weeks 4–11) swimming was not allowed, and the total training volume was reduced by 55.2 ± 7.5% compared to the baseline volume (from 27.2 to 12.2 training hours). This drop was associated with a decrease in vagal activity (a 9.2 ± 5.4% increase in resting HR and a 6.5 ± 3.4% decrease in the natural logarithm of rMSSD from baseline values). After the lockdown (weeks 12–20), the training volume was gradually increased before attaining 68.8% and 88.2% of the baseline training volume at weeks 15 and 17, respectively. Resting HR and Ln rMSSD returned to baseline values four weeks after the lockdown. Conclusions: The lockdown period induced a decreased training volume which was associated with a decrease in vagal activity. However, HRV values returned to the baseline 4 weeks after the resumption of swimming training.

GreatDeal and NewChicken Merger: Designing an Omni-Channel Supply Chain

On Monday morning, Alice Smith, vice president of supply chain (VP-SC) for GreatDeal, sat at her desk wondering how she and Rosie Dartmouth, the supply chain executive for NewChicken, are going to navigate the merger between their two companies. The merger was finalized last Friday, and NewChicken is now absorbed into GreatDeal. This week, Alice and Rosie, along with their team of experts, have to start the long and arduous process of reconfiguring their now combined supply chains to ensure the most profitable path for the company. Alice and Rosie are expected to give a presentation to the new chief executive officer, Michelle Shalhoub. The expectations from the merger are high, and there is no room for error. Furthermore, Ms. Shalhoub is known for asking for multiple competitive proposals so that she has the option to choose the best one. Depending on which proposal Ms. Shalhoub accepts, Alice may stay in her position as a VP or she may lose her title and office to Rosie. Hence, the stakes are high for Alice at a personal level too. As Alice took another sip of her coffee, she contemplated the changes and challenges ahead. She collected all of her notes on the merger, data sources, related news stories, and business magazines and then pulled up her sleeves to get started.

The Sleep Loss Insult of Spring Daylight Savings in the US Is Observable in Twitter Activity

Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use \Spring Forward" events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs 15 to 30 minutes later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that the window of sleep opportunity is compressed in Twitter data, revealing Spring Forward behavioral change.

#YardFruits: Twitter as a Tool to Disseminate Horticulture Education during a Pandemic

Since late Mar. 2020, many universities halted normal operations due to the Coronavirus Disease 2019 (COVID-19) pandemic. Although extension uses many different techniques to educate consumers, it has been slow to grasp the power of social media. Faced with a dilemma of using digital methods instead of in-person field days, short courses, and workshops, Twitter was a viable alternative, especially for broad audience engagement. Tweet threads were posted on Twitter every Monday morning from 6 Apr. to 8 June 2020. Each thread consisted of 10 tweets. A hashtag #YardFruits was used to start the thread and for later reference. For the first nine threads only one fruit species was discussed per thread. The final thread consisted of single tweets of several species. Engagement percentage did not differ over time but did differ among the crop species. Tweets that did not include a photo received less engagement (2.7%) than those that did include a photo (4.7%). My Twitter account saw a 6.5% increase in followers during the series. Grape (Vitis sp.), passion fruit (Passiflora sp.), fig (Ficus carica), and pear (Pyrus communis) threads had the least engagement and were different from the Other Fruits thread. All other threads were similar. Extension educators can grow their influence by using well-targeted, focused tweets and tweet threads, especially those that use hashtags and photos.