BRN Discussion Ongoing
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alwaysgreen
Top 20
Unfortunately, I don't have that "privelige".Did you report it? Or are you just going to tell us your feelings about it?
Think we’ve found one of the larger BRN shorters …..Plato funds management.
concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.
Extract
There are a lot of red flags on the ASX right now
Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.
Extract
There are a lot of red flags on the ASX right now
Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".
Tothemoon24
Top 20
Non-Invasive Medical Diagnostics: Know Labs' Partnership With Edge Impulse Has Potential To Improve Healthcare Using Machine Learning
by Ernest Dela Aglanu, Benzinga Staff Writerhttps://twitter.com/thecitizendela
May 12, 2023 7:58 AM | 4 min read
Read in App
Advertiser Disclosure
Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms to analyze vast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.
Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID™ radio frequency sensor, using machine learning algorithms from Edge Impulse.
The Benefits of Medical Machine Learning
One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.
Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error.
A Future Of Improved Health Care Through Machine Learning
As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.Already, early uses of machine learning in diagnosis and treatment have shown promise to diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.
With reports indicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals, saving approximately $20 billion annually.
Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)’s ability to reduce errors and save lives.
Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis.
One significant area this could benefit is monitoring blood glucose non-invasively — without pricking the finger for blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood.
Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc.
AAPL-0.68%+ Free Alerts
, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc.
GOOGL+0.81%+ Free Alerts
) that measure heart rate, blood oxygen levels, and a host of other metrics.
But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge.
Using Machine Learning To Enhance Bio-RFID Technology
The Seattle-based company has partnered with Edge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietary Bio-RFID technology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.
Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based on AutoML and TinyML to make AI more accessible, enabling quick and efficient machine learning modeling.
The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.
Rise from the ashes
Regular
It's like deja vu again the non-prick testNon-Invasive Medical Diagnostics: Know Labs' Partnership With Edge Impulse Has Potential To Improve Healthcare Using Machine Learning
by Ernest Dela Aglanu, Benzinga Staff Writer
https://twitter.com/thecitizendela
May 12, 2023 7:58 AM | 4 min read
Read in App
Advertiser Disclosure
Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms to analyze vast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.
Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID™ radio frequency sensor, using machine learning algorithms from Edge Impulse.
The Benefits of Medical Machine Learning
One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.
Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.
Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error.
A Future Of Improved Health Care Through Machine Learning
As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.
Already, early uses of machine learning in diagnosis and treatment have shown promise to diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.
With reports indicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals, saving approximately $20 billion annually.
Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)’s ability to reduce errors and save lives.
Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis.
One significant area this could benefit is monitoring blood glucose non-invasively — without pricking the finger for blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood.
Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc.
AAPL-0.68%+ Free Alerts
, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc.
GOOGL+0.81%+ Free Alerts
) that measure heart rate, blood oxygen levels, and a host of other metrics.
But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge.
Using Machine Learning To Enhance Bio-RFID Technology
The Seattle-based company has partnered with Edge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietary Bio-RFID technology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.
The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.
Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based on AutoML and TinyML to make AI more accessible, enabling quick and efficient machine learning modeling.
The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.
Fullmoonfever
Top 20
See that on the crapper as well?Think we’ve found one of the larger BRN shorters …..Plato funds management.
concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.
Extract
There are a lot of red flags on the ASX right now
Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".
View attachment 36433
(ball) Sackman posted it yesterday morn and was going back n forth with him yesterday cause I posted this over there in response
He tried to dismiss as not related to Plato vid but my point was it was founded on shorting. Apparently couldn't follow my logic
Kinda like this guy as backs up opinion with supporting logic unlike (ball) Sackman.
Opinion: Time for ASIC to ban short-selling - Hewison Private Wealth
Tothemoon24
Top 20
iPronics: Making Photonics Viable with Programmable Chips
Spanish photonics pioneer iPronics Programmable Photonics is making photonic computing and telecommunications applications commercially viable with the world’s first reprogrammable general purpose photonics integrated circuit (PIC).
This will result in enhanced performance in several areas, including chip coupling losses, space requirements, and power consumption. The new layer design will be produced in a pilot batch to be functionally tested, validated, and demonstrated.
The “PROMETHEUS” project is leveraging programmable PICs for neuromorphic computing architectures. Neuromorphic computing is inspired by the structure and working principles of biological brains. Neuromorphic chips create artificial “neurons” that distribute computer processing in a way that is analogous to brains.
iPronics’s PIC can be applied in neuromorphic computing through large scale photonic spiking neural networks that exploit the gigahertz firing rate of laser-neurons integrated in the chip.
With this project, iPronics will also put quantum random generators into practice, which, because they cannot be cloned, will embed physical layer encryption and authentication to the chip.
Before long, iPronics’s programmable PICs may form the basis of high-speed computing and sensing that will power the next generations of numerous cutting edge technologies.
Tothemoon24
Top 20
Number 1 
TECHNOLOGY
As we move forward into the next decade, we can expect to see significant advancements in various emerging technologies. These innovations have the potential to reshape industries, redefine the way we live our lives, and even change the global economy. In this article, we will explore four groundbreaking technologies that are predicted to disrupt the next three to eight years and the impact they may have on the world.
Neuromorphic computing systems simplify product development, enabling product leaders to develop AI systems that can better respond to the unpredictability of the real world. Early use cases include event detection, pattern recognition, and small dataset training. We can expect to see breakthrough neuromorphic devices by the end of 2023, with widespread adoption likely within five years. This technology could disrupt current AI developments, offering power savings and performance benefits unattainable with existing AI chips.
The potential impact of self-supervised learning is extensive, as it will extend the applicability of machine learning to organizations with limited access to large datasets. It is expected to take six to eight years for this technology to reach early majority adoption, significantly impacting existing products and markets.
While the benefits and opportunities of the metaverse are not immediately viable, we can expect the transition toward the metaverse to be as significant as the one from analog to digital. It may take more than eight years for the metaverse to reach early majority adoption, but its impact on existing products and markets will be substantial.
HCAI enables vendors to manage AI risks, be ethical, responsible, and more efficient with automation, while complementing AI with a human touch and common sense. It is expected to take three to six years for HCAI to reach early majority adoption, with a substantial impact on existing products and markets.
The next decade will be an exciting time for technology and innovation. Neuromorphic computing, self-supervised learning, the metaverse, and human-centered AI are just four of the many emerging technologies that will disrupt industries and reshape the world. As we continue to explore and develop these groundbreaking innovations, we can expect to see significant advancements that will redefine the way we live, work, and interact with the world around us.
TECHNOLOGY
The Future of Innovation: Four Emerging Technologies Set to Disrupt the Next Decade
written by Surya Narayan May 7, 2023 1 comment
As we move forward into the next decade, we can expect to see significant advancements in various emerging technologies. These innovations have the potential to reshape industries, redefine the way we live our lives, and even change the global economy. In this article, we will explore four groundbreaking technologies that are predicted to disrupt the next three to eight years and the impact they may have on the world.
1. Neuromorphic Computing: Revolutionizing AI and Machine Learning
One of the most promising emerging technologies is neuromorphic computing, which aims to mimic the structure and function of the human brain using digital or analog processing techniques. By doing so, it has the potential to revolutionize the field of artificial intelligence (AI) and machine learning.Neuromorphic computing systems simplify product development, enabling product leaders to develop AI systems that can better respond to the unpredictability of the real world. Early use cases include event detection, pattern recognition, and small dataset training. We can expect to see breakthrough neuromorphic devices by the end of 2023, with widespread adoption likely within five years. This technology could disrupt current AI developments, offering power savings and performance benefits unattainable with existing AI chips.
2. Self-Supervised Learning: Accelerating Data Annotation and Labeling
Self-supervised learning is another groundbreaking technology that has the potential to transform industries reliant on AI and machine learning. This automated approach to annotating and labeling data enables machine learning algorithms to learn how information relates to other information, which is particularly useful in computer vision and natural language processing (NLP) applications.The potential impact of self-supervised learning is extensive, as it will extend the applicability of machine learning to organizations with limited access to large datasets. It is expected to take six to eight years for this technology to reach early majority adoption, significantly impacting existing products and markets.
3. The Metaverse: Creating an Immersive Digital Environment
Another highly anticipated emerging technology is the metaverse, an immersive digital environment that intersects with the physical world’s real-time, spatially organized, and indexed content. The metaverse will bring together various independently evolving trends and technologies, such as spatial computing, digital persistence, decentralization tech, high-speed networking, and AI applications.While the benefits and opportunities of the metaverse are not immediately viable, we can expect the transition toward the metaverse to be as significant as the one from analog to digital. It may take more than eight years for the metaverse to reach early majority adoption, but its impact on existing products and markets will be substantial.
4. Human-Centered AI: Enhancing Cognitive Performance and Ethics
Human-centered AI (HCAI) is an AI design principle that emphasizes the benefit of AI to people and society. By focusing on improving transparency and privacy, HCAI aims to create a partnership between humans and AI, enhancing cognitive performance, learning, decision-making, and new experiences.HCAI enables vendors to manage AI risks, be ethical, responsible, and more efficient with automation, while complementing AI with a human touch and common sense. It is expected to take three to six years for HCAI to reach early majority adoption, with a substantial impact on existing products and markets.
The next decade will be an exciting time for technology and innovation. Neuromorphic computing, self-supervised learning, the metaverse, and human-centered AI are just four of the many emerging technologies that will disrupt industries and reshape the world. As we continue to explore and develop these groundbreaking innovations, we can expect to see significant advancements that will redefine the way we live, work, and interact with the world around us.
Think we’ve found one of the larger BRN shorters …..Plato funds management.
concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.
Extract
There are a lot of red flags on the ASX right now
Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".
View attachment 36433
Hmmmm ....
This bloke admits he does not know what's happening with AI, but he doesn't seem to have done anything to remedy that deficit. Is his reference to picks and shovels an indication of the field in which he is most comfortable?
I don't know what his 126 flags are, nor the 22 that BRN earned other than it is down 80% from its peak, and it has no income. He also pays a lot for his coffee.
He refers to BRN's peak SP, but he does not mention what triggered that spike.
I wonder how he developed a flag system for a first-in-the-universe digital spiking neural network IP licensing company start-up. There must have been lots of examples to develop 126 flags.
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
"ASML (NASDAQ: ASML) has 88% market share, so a virtual monopoly in the deep ultraviolet lithography machines that are required to build the chips that are used in all of the technology around us, but specifically all of AI," he says.
"If you ask who is going to be the eventual winner of the AI race, it's very hard to say. But what you can say is that this picks and shovels approach of investing in the companies that are producing the machines that are producing the chips are going to win."
Yes, the US taking chip manufacture back in-house will cause a spike in UV lithography machine sales, and AI will increase the demand for new chips, but that does not make ASML an AI company.
Even though he makes most of his money from shorts, surely the time to short BRN was when it was $2.34?
Why would he choose to publish this negative post now that the BRN share price decline seems to have bottomed?
D
Deleted member 118
Guest
I think it started already
www.bbc.com
AI creator on the risks, opportunities and how it may make humans 'boring'
AI boss on the future of artificial intelligence and what will happen if computers become more clever than humans.
Deena
Regular
I have a question that I hope someone can help me with. I am attending the upcoming AGM in Sydney (for the first time) and was wondering for those attending the meeting how is your vote recorded at the meeting and linked to your shareholding?
Obviously everyone's shareholding varies, so how do they link this? Do you get a number, or have to record your result on paper?
Anyone?
Also, do we know how many TSX contributors are attending?
Deena
Obviously everyone's shareholding varies, so how do they link this? Do you get a number, or have to record your result on paper?
Anyone?
Also, do we know how many TSX contributors are attending?
Deena
Last edited:
D
Deleted member 118
Guest
I think you need to vote prior to the meeting as you can’t vote on the day and going on what I’ve read maybe a dozen members could be attending.
Just hold up one finger for each million shares you own.
(FF's the bloke with both hands up.)
Guess he’s going public as he wants to now buy back and retail to sell to him..Think we’ve found one of the larger BRN shorters …..Plato funds management.
concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.
Extract
There are a lot of red flags on the ASX right now
Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".
View attachment 36433
Schwale
Regular
Sounds like his made his money on the way down now he trying make his money on the way up! From his point of view it's a win/win. Short selling should be outlawed.
Frangipani
Regular
Think we’ve found one of the larger BRN shorters …..Plato funds management.
concerning that out of their universe of 10,000 global stocks to potentially short, Brainchip have the second most number of red flags (22 red flags) & Dr David Allen openly confirms they are one of Plato’s bigger shorts.
Extract
There are a lot of red flags on the ASX right now
Allen and his team have recently identified a higher average number of red flags per stock in Australia compared with other countries around the world. He's steadfast that he's not bearish on Australia, in general, but recommends investors avoid a "blind index approach".
Of the 10,000 companies in the Fund's investment universe, one Australian company has 22 red flags - Brainchip Holdings (ASX: BRN).
He also points to Weebit Nano (ASX: WBT) as another Australian-listed short in the portfolio - which he notes, has a valuation of more than $1 billion but makes "zero revenue".
View attachment 36433
Ironically, when it comes to Brainchip, the analysts and portfolio managers at Plato Investment Management don’t practice what they preach, as they do not take their namesake’s advice, despite advertising it on their website:
“A good decision is based on knowledge and not on numbers.”
Plato (427-347 BC)
Well, I assume most of us genuine shareholders neither have worked for JP Morgan for 15 years nor have a PhD from Cambridge in Quantitative Finance, but eventually we’ll still outsmart this guy as we trust in the validation of semiconductor industry experts more than in the analysis of self-professed shorters, too short-sighted to foresee the impending paradigm-shift that neuromorphic computing will inevitably bring, transforming the future of technology. Or maybe Dr. David Allen isn’t actually that short-sighted after all, but on the contrary quite far-sighted and perspicacious and was a bit worried of late that the gradually rising BRN share price pre AGM might thwart his plans to load up cheaply big time.
Let me thus conclude with another fitting Plato quote (or at least often attributed to him): “Wise men speak because they have something to say; fools because they have to say something.” Methinks the word “motley” got lost in translation, though?
DR. DAVID ALLEN
Head Of Long/Short Strategies
David joined Plato in January, 2018. Prior to joining Plato, David worked for JP Morgan Asset Management in London for 15 years. He ran the JP Morgan Europe Strategic Dividend Fund from 2006-2010. In 2007, David designed and launched the JP Morgan Europe Equity Plus active-extension fund which grew to €6 bn. In 2009, he became a Managing Director and the Head of Research.
In 2011, David embarked upon a PhD in Quantitative Finance at Cambridge University focussing on portfolio construction. On his return to JP Morgan in a full-time capacity in 2013, David launched the JP Morgan Europe Equity Absolute Alpha Market Neutral fund. In its first twelve months of operation, the fund returned 22.5%, and grew assets to €1 bn. winning the Eurohedge UCITS best fund award.
David has a Bachelor of Business with First Class Honours from University of Technology, Sydney, and a PhD from Cambridge in Quantitative Finance.
GDJR69
Regular
I suggest voting online through the Boardroom prior to the meeting. I've done that and its very easy.
Frangipani
Regular
Don’t fret - do you have any idea how many northern hemispherists would not be able to give the correct answer to the question “What is the capital of Australia?”
Deena
Regular
Thanks guys.
AusEire
Founding Member. It's ok to say No to Dot Joining
Did you happen to abuse that privilege once upon a time?
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