GCC bourses close in the red as US-Iran tensions escalate

Most Gulf markets are reacting negatively due to the ongoing geopolitical tensions and the situation may continue for some time. (Reuters)
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Updated 06 January 2020

GCC bourses close in the red as US-Iran tensions escalate

  • Egypt’s blue-chip index also hard hit as all its shares ended lower

DUBAI: Gulf bourses saw steep declines on Sunday, with Kuwait falling the most following tensions between the US and Iran, while outside the Gulf Egypt was also hard hit as all its shares ended lower.

Iranian military commander Qassem Soleimani was killed on Friday in a US drone strike on his convoy at the Baghdad airport, seen by Tehran as an act of war that risks regional conflagration.

“Not surprising, the Gulf markets are reacting negatively given we are in the middle of all the geopolitics action,” said Vrajesh Bhandari, a senior portfolio manager at Al Mal Capital.

“We fear this can be an overhang over the next few months and not just a one day or week thing.”

Saudi Arabia’s benchmark index lost 2.4 percent, weighed down by a 2.1 percent drop in Al-Rajhi Bank and a 1.7 percent fall in Saudi Aramco to SR34.6 ($9.2), which hit its lowest intraday level since last month’s market debut.

Egypt’s blue-chip index dived 4.4 percent, touching its lowest since September 2019. The country’s largest lender Commercial International Bank closed down 1.9 percent and Eastern Company dived 4.9 percent.

In Kuwait, the index plunged 4.1 percent with all stocks in the red including Kuwait Finance House, down 5.1 percent, and National Bank of Kuwait, off 2.8 percent.

The Dubai index tumbled 3.1 percent, hurt by a 3.1 percent slide in its largest lender Emirates NBD and a 3.7 percent decline in Emaar Properties.

Abu Dhabi’s index lost 1.4 percent, with the UAE’s largest lender First Abu Dhabi Bank retreating 1.2 percent, while Abu Dhabi Commercial Bank was down 3.3 percent.


• Saudi Arabia’s benchmark index lost 2.4 percent.

• The Dubai index tumbled 3.1 percent.

• Abu Dhabi’s index lost 1.4 percent.

• The small bourse of Oman dropped by just 0.3 percent.

The decline in Gulf shares comes despite a surge in oil prices, on which all six GCC nations rely heavily for public revenues.

“It’s certainly due to fears of a possible US-Iranian conflict breaking out in the Gulf,” said Mohammed Zidan, market strategist at Thinkmarket in Dubai.

“I think the decline will continue for some time and especially as long as tensions and the threat of an armed conflict continue,” Zidan told AFP.

The Qatar index eased 2.1 percent with all its 20 stocks closing lower. Lender Masraf Al Rayan fell 2.7 percent and Qatar National Bank declined 1.4 percent.

The normally dormant bourse of Bahrain, home to the US 5th fleet, fell 2.3 percent.

The small bourse of Oman dropped by just 0.3 percent.

Man vs. machine in bid to beat virus

Updated 20 February 2020

Man vs. machine in bid to beat virus

  • Human and artificial intelligence are racing ahead to detect and control outbreaks of infectious disease

BOSTON: Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments to save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It is difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The first public alert outside China about the novel coronavirus came on Dec. 30 from the automated HealthMap system at Boston Children’s Hospital. At 11:12 p.m. local time, HealthMap sent an alert about unidentified pneumonia cases in the Chinese city of Wuhan. The system, which scans online news and social media reports, ranked the alert’s seriousness as only 3 out of 5. It took days for HealthMap researchers to recognize its importance.

Four hours before the HealthMap notice, New York epidemiologist Marjorie Pollack had already started working on her own public alert, spurred by a growing sense of dread after reading a personal email she received that evening.

“This is being passed around the internet here,” wrote her contact, who linked to a post on the Chinese social media forum Pincong. The post discussed a Wuhan health agency notice and read in part: “Unexplained pneumonia???”

Pollack, deputy editor of the volunteer-led Program for Monitoring Emerging Diseases, known as ProMed, quickly mobilized a team to look into it. ProMed’s more detailed report went out about 30 minutes after the terse HealthMap alert.

Early warning systems that scansocial media, online news articles and government reports for signs of infectious disease outbreaks help inform global agencies such as the World Health Organization — giving international experts a head start when local bureaucratic hurdles and language barriers might otherwise get in the way.

Some systems, including ProMed, rely on human expertise. Others are partly or completely automated.

“These tools can help hold feet to the fire for government agencies,” said John Brownstein, who runs the HealthMap system as chief innovation officer at Boston Children’s Hospital. “It forces people to be more open.”

The last 48 hours of 2019 were a critical time for understanding the new virus and its significance. Earlier on Dec. 30, Wuhan Central Hospital doctor Li Wenliang warned his former classmates about the virus in a social media group — a move that led local authorities to summon him for questioning several hours later.

Li, who died Feb. 7 after contracting the virus, told The New York Times that it would have been better if officials had disclosed information about the epidemic earlier. “There should be more openness and transparency,” he said.

ProMed reports are often incorporated into other outbreak warning systems. including those run by the World Health Organization, the Canadian government and the Toronto startup BlueDot. WHO also pools data from HealthMap and other sources.

Computer systems that scan online reports for information about disease outbreaks rely on natural language processing, the same branch of artificial intelligence that helps answer questions posed to a search engine or digital voice assistant.

But the algorithms can only be as effective as the data they are scouring, said Nita Madhav, CEO of San Francisco-based disease monitoring firm Metabiota, which first
notified its clients about the outbreak in early January.

Madhav said that inconsistency in how different agencies report medical data can stymie algorithms. The text-scanning programs extract keywords from online text, but may fumble when organizations variously report new virus cases, cumulative virus cases, or new cases in a given time interval. The potential for confusion means there is almost always still a person involved in reviewing the data.

“There’s still a bit of human in the loop,” Madhav said.

Andrew Beam, a Harvard University epidemiologist, said that scanning online reports for key words can help reveal trends, but the accuracy depends on the quality of the data. He also notes that these techniques are not so novel.

“There is an art to intelligently scraping web sites,” Beam said. “But it’s also Google’s core technology since the 1990s.”

Google itself started its own Flu Trends service to detect outbreaks in 2008 by looking for patterns in search queries about flu symptoms. Experts criticized it for overestimating flu prevalence. Google shut down the website in 2015 and handed its technology to nonprofit organizations such as HealthMap to use Google data to build their own models.

Google is now working with Brownstein’s team on a similar web-based approach for tracking the geographical spread of the tick-borne Lyme disease.

Scientists are also using big data to model possible routes of early disease transmission.