How AI Algorithms Are Changing Trading Forever
The Aite Group in its report "Hedge Fund Survey, 2020: Algorithmic Trading"
argues that the main reason for the growing popularity of algorithms in trading
is to try to reduce the influence of the human factor on the market due to its
high volatility. The economic fallout from COVID-19 has seen a record-breaking
drop in the American, European, and Chinese stock markets. And only a few months
later, measures to stimulate the economy were able to stop the fall and reverse
the downtrend up. Thus, we get the first task of Algorithmic Trading - risk
reduction in a market with high volatility. The second global advantage of
algorithmic trading lies in the ability to analyze the potential impact of trade
on the market. This can be especially useful for Hedge Funds and institutional
investors who handle large sums of money with a visible effect on price
movements. The third fundamental advantage of trading algorithms is protection
from emotions. Traders and investors, like all living people, experience the
emotions of fear, greed, lost profits, and others. These emotions have a
negative impact on performance and results.
How to prevent corporate credentials ending up on the dark web
Employees are the weakest link in any organization’s security posture. A Tessian
report found that 43% of US and UK employees have made mistakes that resulted in
cybersecurity repercussions for their organizations. Phishing scams, including
emails that try to trick employees into sharing corporate login details, are
particularly common. Educating employees on cyber threats and how to spot them
is crucial to mitigating attacks. However, for training to be effective, it
needs to consist of more than just repetitive lectures. In the report mentioned
above, 43% of respondents said a legitimate-looking email was the reason they
fell for a phishing scam, while 41% of employees said they were fooled because
the email looked like it came from higher up. Live-fire security drills can help
employees familiarize themselves with real-world phishing attacks and other
password hacks. Safety awareness training should also teach workers the
importance of good practices like using a virtual private network (VPN) when
working from home and making social media accounts private.
IT leadership: 4 ways to find opportunities for improvement
Technology leaders should regularly use their own technology to better
identify pain points and opportunities for improvements. That means that I
should be teaching and using the same systems that faculty does to understand
their experience through their lens. I should be meeting regularly with them
and generating a Letterman-style Top 10 list of the things I hate most about
my technology experience. This is something to do with the students, too. What
do they hate most about the technology at the university? And how can we
partner with them to address these issues over the next 12 months? Several
years ago, for example, we reexamined our application process. If a
prospective student wanted to submit an application, we required them to
generate a unique username and password. If the one they chose was already
taken, they needed to continue creating alternate versions until they
eventually landed upon one that was available. If someone began the
application process and logged off to complete it later, then forgot their
username and password, they’d have to start all over again. It was absurd.
Data Management In The Age Of AI
AI is increasingly converging the traditional high-performance computing and
high-performance data analytics pipelines, resulting in multi-workload
convergence. Data analytics, training and inference are now being run on the
same accelerated computing platform. Increasingly, the accelerated compute
layer isn’t limited to GPUs—it now involves FPGAs, graph processors and
specialized accelerators. Use cases are moving from computer vision to
multi-modal and conversational AI, and recommendation engines are using deep
learning while low-latency inference is used for personalization on LinkedIn,
translation on Google and video on YouTube. Convolutional neural networks
(CNN) are being used for annotation and labeling to transfer learning. And
learning is moving to federated learning and active learning, while deep
neural networks (DNN) are becoming even more complex with billions of
hyper-parameters. The result of these transitions is different stages within
the AI data pipelines, each with distinct storage and I/O requirements.
SASE: Building a Migration Strategy
Gartner's analysts say that "work from anywhere" and cloud-based computing
have accelerated cloud-delivered SASE offerings to enable anywhere, anytime
secure access from any device. Security and risk management leaders should
build a migration plan from the legacy perimeter and hardware-based offerings
to a SASE model. One hindrance to SASE adoption, some security experts tell
me, is that organizations lack visibility into sensitive data and awareness of
threats. Too many enterprises have separate security and networking teams that
don't share information and lack an all-encompassing security strategy, they
say. "While the vendors are touting SASE as the end-all solution, the key to
success would depend upon how well we define the SASE operating model,
particularly when there are so many vendors coming up with SASE-based
solutions," says Bengaluru-based Sridhar Sidhu, senior vice president and head
of the information security services group at Wells Fargo. Yask Sharma,
CISO of Indian Oil Corp., says that as data centers move to the cloud,
companies need to use SASE to enhance security while controlling costs.
How to Bridge the Gap between Netops and Secops
If you were designing the managerial structure for a software development firm
from scratch today, it’s very unlikely that you would separate NetOps and
SecOps in the first place. Seen from the perspective of 2021, many of the
monitoring and visibility tools that both teams seek and use seem inherently
similar. Unfortunately, however, the historical development of many firms has
not been that simple. Teams and remits are not designed from the ground up
with rationality in mind – instead they emerge from a complex series of
interactions and ever-changing priorities. This means that different teams
often end up with their own priorities, and can come to believe that they are
more important than those of other parts of your organization. This is seen
very clearly in the distinction between SecOps and NetOps teams in many firms.
At the executive level, your network exists in order to facilitate connections
– between systems and applications but above all between staff members. Yet
for many NetOps teams, the network can come to be seen as an end in itself.
The future of data science and risk management
“Enterprise data is growing nearly exponentially, and it is also increasing in
complexity in terms of data types,” said Morgan. “We have gone way past the
time when humans could sift through this amount of data in order to see
large-scale trends and derive actionable insights. The platforms and best
practices of data science and data analytics incorporate technologies which
automate the analytics workflows to a large extent, making dataset size and
complexity much easier to tackle with far less effort than in years past. “The
second value-add is to leverage machine learning, and ultimately artificial
intelligence, to go beyond historical and near-real-time trend analysis and
‘look into the future’, so to speak. Predictive analysis can unveil new
customer needs for products and services and then forecast consumer reactions
to resultant offers. Equally, predictive analytics can help uncover latent
anomalies that lead to much better predictions about fraud detection and
potentially risky behaviour. “Nothing can foretell the future with 100%
certainty, but the ability of modern data science to provide scary-smart
predictive analysis goes well beyond what an army of humans could do
manually.”
DevOps Observability from Code to Cloud
DevOps has transformed itself in the last few years, completely changing from
what we used to see as siloed tools connected together to highly integrated,
single-pane-of-glass platforms. Collaboration systems like JIRA, Slack, and
Microsoft Teams are connected to your observability tools such as Datadog,
Dynatrace, Splunk, and Elastic. Finally, IT Service management tools like
PagerDuty are also connected in. Tying these high-in-class tools together on one
platform, such as the JFrog Platform, yields high value to the enterprises
looking for observability workflow. The security folks also need better
visibility into an enterprise’s systems, to look for vulnerabilities. A lot of
this information is available in Artifactory and Amazon Web Services‘ Xray, but
how do we leverage this information in other partner systems like JIRA and
Datadog? It all starts with JFrog Xray’s security impact, where we can generate
the alert to Slack and robust security logs to Datadog to be analyzed by your
Site Reliability Engineer. A PagerDuty incident that’s also generated from Xray
can then be used to create a JIRA issue quickly.
6 Global Megatrends That Are Impacting Banking’s Future
The line between digital and physical has blurred, with consumers who once
preferred brick-and-mortar engagements now researching, shopping and buying
using digital channels more than ever. This trend is expected to increase across
all industries. While organizations have enabled improved digital engagement
over the past several months, there are still major pain points, mostly with
speed, simplicity and cross-channel integration during the ‘first mile’ of
establishing a relationship. The retail industry already understands that
consumers are becoming increasingly impatient, wanting the convenience and
transparency of eCommerce and the service and humanization of physical stores.
In banking, consumers are diversifying their financial relationships, moving to
fintech and big tech providers that can open relationships in an instant and
personalize experiences. According to Brett King, founder of Moven and author of
the upcoming book, ‘The Rise of Technosocialism’, “The ability to acquire new
customers at ‘digital scale’ will impact market share and challenge existing
budgets for branches. ..."
Understanding Contextual AI In The Modern Business World
Contextual AI can be divided into three pillars that help make businesses become
more visible to the people they want to reach. In the same sense, when a
business is looking for a partner, it has to be sure that a prospect can offer
the right services to fulfill its goals. Contextual AI aims to deliver that. The
technology allows a brand to enhance its understanding of consumer interests. It
is easy to make assumptions about consumer interests in different sectors, but
difficult to prove them. ... In previous years, contextual AI was seen as an
enhancing technology, but not an essential one. Now, the recognition of
contextual AI as more than simply enhancing is growing. Businesses are
constantly looking for more cost-effective solutions to their problems, and
contextual AI offers one solution to fit that bracket. If you look at a similar
alternative, such as behavioral advertising, it is heavily reliant on data — and
lots of it. The huge amounts of data required to make this a success means that
businesses have to implement a successful collection, analysis and then
reporting solution in order to leverage it effectively. This can be a costly
process if a business does not have large economies of scale.
Quote for the day:
"A true dreamer is one who knows how to
navigate in the dark" -- John Paul Warren
No comments:
Post a Comment