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Don't be fooled by the paint & wash trading.
04/08/24 10:21:17 0.18 0.164 0.18 5,000
04/08/24 10:21:16 0.18 0.164 0.18 100
04/08/24 10:20:56 0.172 0.164 0.18 500
04/08/24 10:08:45 0.1776 0.164 0.18 200
04/08/24 10:03:15 0.1645 0.164 0.165 9,500
04/08/24 10:03:10 0.1645 0.164 0.165 500
04/08/24 10:02:22 0.164 0.164 0.165 5,500
04/08/24 10:02:17 0.165 0.164 0.165 1,000
04/08/24 10:02:07 0.165 0.00 0.00 1,625
04/08/24 09:59:56 0.164 0.164 0.165 1,900
04/08/24 09:59:34 0.1646 0.164 0.165 500
04/08/24 09:58:41 0.164 0.158 0.164 12,000
04/08/24 09:48:11 0.164 0.158 0.165 10,000
04/08/24 09:38:22 0.1622 0.158 0.165 200
04/08/24 09:30:09 0.16 0.158 0.165 1,000
04/08/24 09:30:03 0.16 0.153 0.16 3,500
04/05/24 15:47:36 0.1565 0.153 0.16 50
04/05/24 15:47:36 0.1565 0.153 0.16 100
04/05/24 15:47:20 0.153 0.153 0.16 29
04/05/24 15:47:20 0.153 0.153 0.16 100
04/05/24 15:47:07 0.153 0.00 0.00 70
04/05/24 14:56:54 0.16 0.153 0.16 82
04/05/24 14:56:54 0.16 0.153 0.16 1,100
04/05/24 14:29:20 0.1565 0.153 0.16 2,500
04/05/24 14:26:54 0.1579 0.153 0.16 2,500
04/05/24 13:34:33 0.1579 0.153 0.16 500
04/05/24 13:34:22 0.1565 0.153 0.16 500
04/05/24 13:34:11 0.1565 0.153 0.16 73
04/05/24 13:34:11 0.1565 0.153 0.16 2,100
04/05/24 13:22:33 0.1579 0.153 0.16 500
04/05/24 13:01:28 0.155 0.155 0.16 400
04/05/24 13:01:19 0.155 0.155 0.16 1,000
04/05/24 12:25:23 0.1575 0.155 0.16 2,000
04/05/24 12:25:17 0.1575 0.155 0.16 500
04/05/24 12:11:46 0.16 0.155 0.16 500
04/05/24 10:48:32 0.155 0.155 0.16 5,000
04/05/24 09:33:46 0.165 0.152 0.165 5,800
04/05/24 09:32:00 0.165 0.152 0.165 20
04/05/24 09:30:28 0.165 0.152 0.165 100
04/05/24 09:30:02 0.152 0.152 0.165 81,300
04/05/24 09:30:02 0.165 0.152 0.165 81,311
04/05/24 09:30:00 0.165 0.152 0.165 100
04/04/24 15:42:15 0.1665 0.1651 0.1697 10,000
04/04/24 15:42:07 0.1665 0.00 0.00 2,500
04/04/24 14:39:18 0.1651 0.1651 0.1697 5,000
04/04/24 14:06:59 0.1685 0.1651 0.1697 2,500
04/04/24 14:06:59 0.1668 0.1651 0.1697 500
04/04/24 13:58:51 0.1651 0.1651 0.1685 6,700
04/04/24 13:58:48 0.1651 0.1651 0.1685 1,000
04/04/24 13:44:25 0.1681 0.1651 0.1697 10,000
04/04/24 13:39:37 0.1697 0.1651 0.1697 2,400
04/04/24 13:39:32 0.1674 0.1651 0.1697 500
04/04/24 13:01:46 0.1697 0.1651 0.1697 1,000
04/04/24 12:58:49 0.1651 0.1651 0.1697 1,500
04/04/24 12:58:29 0.1674 0.1651 0.1697 500
04/04/24 12:23:23 0.1651 0.1651 0.1697 73
04/04/24 12:23:23 0.1651 0.1651 0.1697 3,500
04/04/24 11:55:52 0.1651 0.1651 0.1697 2,800
04/04/24 11:55:49 0.1651 0.1651 0.1697 1,000
04/04/24 11:52:51 0.1681 0.1651 0.1697 7,000
04/04/24 11:50:22 0.1674 0.165 0.1697 28,900
04/04/24 11:50:14 0.1674 0.165 0.1697 500
04/04/24 11:44:22 0.165 0.165 0.1697 2,000
04/04/24 11:44:21 0.1674 0.165 0.1697 500
04/04/24 11:41:06 0.165 0.165 0.1697 3,000
04/04/24 11:37:13 0.1651 0.165 0.1697 7,000
04/04/24 11:36:41 0.1697 0.165 0.1697 1,000
04/04/24 11:24:08 0.168 0.1585 0.1697 50
04/04/24 11:24:08 0.168 0.1585 0.1697 100
04/04/24 11:22:25 0.1585 0.1585 0.1697 10,000
04/04/24 11:03:53 0.1663 0.1585 0.1697 75
04/04/24 10:48:53 0.1663 0.1585 0.1697 1,000
04/04/24 10:46:32 0.1697 0.1585 0.1697 2,000
04/04/24 10:46:24 0.1641 0.1585 0.1697 500
04/04/24 10:23:43 0.165 0.156 0.1697 12,900
04/04/24 10:23:41 0.165 0.156 0.165 8,700
04/04/24 10:23:37 0.164 0.156 0.164 8,400
04/04/24 10:22:51 0.164 0.156 0.164 6,600
04/04/24 10:22:44 0.164 0.156 0.164 8,400
04/04/24 10:05:51 0.1595 0.155 0.164 500
04/04/24 10:02:31 0.1598 0.1598 0.164 5,600
04/04/24 09:59:47 0.1598 0.155 0.1598 5,000
04/04/24 09:59:46 0.1598 0.155 0.1598 5,000
04/04/24 09:31:41 0.1549 0.152 0.1549 5,000
04/04/24 09:31:37 0.155 0.152 0.1549 24,000
04/04/24 09:31:25 0.1538 0.1538 0.1549 8,000
04/04/24 09:30:00 0.159 0.152 0.159 45
04/04/24 09:30:00 0.159 0.152 0.159 600
04/04/24 09:30:00 0.155 0.152 0.159 7,000
LOL!!!!
This "update" reads like there is only one person employed or running this scam OTC...
Sorry, obfuscation is altering reality...
I got communication involving images from my sister last night that required more bull shit software to view it.
The B.S. is out of control...
80%+ of all trading volume is not investors voting on the value of the stock. It is traders attempting to gain a profit mechanically. Meaning, primarily, market makers. Many of whom are also hedge funds, separated by a laughable Chinese wall. The most appalling of which is, of course, Citadel. One of the defendants in NWBO’s lawsuit.
What it means is charts and stats are all fake
So AI rearranged my tabs last night....
Since AI is controlling what I see on the internet it seems that the internet is becoming obsolete...
BONAR has been running scam stocks since the '80s.
Basic DD identifies him as a crook.
The scary part is that this crook has been able to exploit a much larger ring of criminal conspiracy.
At this point his pump and dump schemes are laughable and easily discernable to anyone who does basic DD.
If anyone is really looking at this stock for "investment" purposes....
....please be aware the the pustule on your ass is probably an indication that you need to deal with an economic cancer.
WTF is a "Pur moment"....
Anyone who "invests" in a BONAR stock is either involved in criminal conspiracy or a total idiot.
Everything on this photoshopped screen comes up with Zilch...
Nothing or no one referenced can be emailed.
I haven't charged my stance and I am unable to understand how you could interpret my posting history as such.
Your post contains an oxymoron... emphasis on the "moron" part...
No one with half a brain would remain invested in a stock if they believed their CEO was "incompetent".
So I think the "moron" part applies to your post or anyone who takes it seriously.
I didn't bring up the subject, did I ??
Um... do you believe that AVXL science exists independent of literacy and understanding of molecular biology as a whole?
??
??????
From Google AI:
Analytical development in pharmacology is the process of developing and validating analytical methods to measure the properties of pharmaceutical products. These methods are used to identify, separate, and quantify the chemical components of drug products, and to ensure that the products are safe, effective, and of high quality.
Analytical development involves:
--Identifying testing methodologies
--Ensuring adherence to regulatory requirements
--Validating the methods' appropriateness for their intended objectives
--Selecting and optimizing analytical methods
--Creating a systematic approach to evaluating and selecting suitable methods
--Developing analytical methods that are sensitive, specific, and robust
--Developing analytical methods that can measure the target attribute within acceptable limits of accuracy and precision
Analytical procedures are developed to test specific characteristics of the substances against the predefined acceptance criteria for such characteristics. The goal is to create a strategy to guarantee the drug substance or composition's identification, purity, and potency.
Analytical methods can be used to identify, separate, quantify, and learn more about the chemical components in drug products intended for commercial manufacturing.
What is analyte develo
Still ... you have not referenced a single arugmentable issue....
This is a no brainer...
"Anavex need a Director of Analytical Analysis"
Because conventional analysis (plaque attack theory) is a FAIL...
DUH!!!
Bashers can't overcome the reality of it all...
Another cut member it seems...
An Anti-Truth member who projects his beliefs on others:
Do you guys have to wear hooded robes and chant for the Antwerp initiation meeting? Do you have to flash the secret “L” symbol to get in, and then get paddled by JM while asking for another? Maybe you’ll get “Faster, Less Energy” tattooed on your buttocks?!
Link to original:
https://investorshub.advfn.com/boards/read_msg.aspx?message_id=69486867
nodummy
Re: nodummy post# 11543
Thursday, December 01, 2011 3:50:24 AM
Post# 18772 of 220331
STTN some research inspired by the filing of the last 10Q on November 21, 2011
The stock started to plummet the day before the 10Q filing (maybe some insiders with advanced knowledge that the 10Q was going to be brutal decided they better start getting out early?)
-----
So what was in this 10Q?
Before we get to that a quick rundown on the events that have happened over the past few months:
-----
Starting shortly after the former Chief Executive Officer, Chief Financial Officer, Secretary and Treasurer and founder of the company, Perry Law, tendered his resignation from his last remaining position of Director effective immediately on June 3, 2011 things have taken a very bad turn for the worse.
-----
On June 9, 2011, Brian Bonar signed a toxic financing agreement with La Jolla which included a $500,000 debenture agreement and the right for La Jolla to purchase up to $5,000,000 worth of stock at 80% below the market price. To date this agreement has not been executed.
http://www.otcmarkets.com/edgar/GetFilingHtml?FilingID=7997447
On June 15, 2011, Brian Bonar issued himself 21,897,999 shares at no cost. According to the recently filed 10Q those shares were issued for $218,979 in compensation owed. On June 15, 2011, STTN closed at $.071/share making the actual value of those shares $1,554,757.93.
http://www.sec.gov/Archives/edgar/data/947011/000106299311002630/xslF345X03/form4a.xml
On June 17, 2011, Brian Bonar issued to his Director, Owen Naccarato, 3,000,000 shares at no cost for $225,000 in compensation owed.
http://www.sec.gov/Archives/edgar/data/947011/000106299311002672/xslF345X03/form4.xml
On July 29, 2011, Brian Bonar and Owen Naccarato met at the Rancho Bernardo Inn and used their 24,897,999 shares to elect themselves as the new Directors for the STTN shell.
http://www.otcmarkets.com/edgar/GetFilingHtml?FilingID=8069037
On October 17, 2011, Brian Bonar brought his son, Colin Niven Bonar (aka C. Niven Bonar aka C N Bonar), into the picture by purchasing a group of companies which were all wholly owned subsidiaries of American Marine LLC, a company controlled by both Brian Bonar and C. Niven Bonar, for $50,000 and a $500,000 debt Note.
http://www.otcmarkets.com/edgar/GetFilingHtml?FilingID=8194094
Solvis Medical Group consists of three revoked Nevada entities
Solvis Medical Inc and Solvis Medical Staffing Inc and Solvis Physical Therapy Inc all with Eric Gaer and Robert Dietrich listed as officers.
Former chairman of the Solvis Medical Group is Brian Bonar.
American Marine LLC is controlled by both Brian Bonar and C. Niven Bonar
http://www.corporationwiki.com/California/Escondido/american-marine-llc/47532519.aspx
Owen Naccarato (STTN Director) served as the legal counsel for the signed agreement between father and son.
C. Niven Bonar and Brian Bonar were previously linked with Dalrada Financial Corp (DFCO). Both C. Niven Bonar and Brian Bonar's daughter, Pauline Bonar, were initial shareholders in Dalrada Financial Corp back in 1999 while Brian Bonar was the CEO. Not so coincidentally Owen Naccarato was and still is the legal counsel for Bonar linked Delrada Financial Corp.
http://www.otcmarkets.com/stock/DFCO/company-info
The Bonar, Bonar, Naccarato connections don't end there.
The three can be linked to Allegiant Professional Business Services, Inc. (APRO)
Where daddy Bonar served as a Director and president, son Bonar served as the COO, and Naccarato once against served as legal counsel:
http://www.otcmarkets.com/stock/APRO/company-info
http://investing.businessweek.com/research/stocks/people/people.asp?ticker=APRO:US
APRO was (I used past tense because that company is basically dead now) a PEO company just like STTN is now.
APRO even uses the same address as STTN
11838 Bernardo Plaza Ct.
Suite 240
San Diego, CA 92128
Which is in shouting distance from American Marine LLC
11838 Bernardo Plaza Ct
Suite 210
San Diego, CA 92128
And is within walking distance from Dalada Financial Corp
11956 Bernardo Plaza Drive
#516
San Diego, CA 92128
John Capezzuto who works with Brian Bonar with APRO also worked with Brian Bonar with scam company Warning Management Services Inc. (WNMI) which was revoked by the SEC on May 22, 2009
http://www.sec.gov/litigation/admin/2009/34-59968.pdf
http://investorshub.advfn.com/boards/read_msg.aspx?message_id=64388493
Legal counsel for scam company WNMI was Owen Naccarato.
Are you beginning to wonder if Brian Bonar always uses Owen Naccarato for a reason?
Here is a list of companies for which Owen Naccarato currently provides legal services:
http://www.otcmarkets.com/service-provider/Naccarato-&-Associates?id=2062&b=n&filterOn=3
Allegiant Professional Business Services, Inc. (APRO)
Com-Guard.com, Inc. (CGUD)
Dalrada Financial Corp. (DFCO)
Diverse Media Group, Inc. (DVME)
DPOLLUTION International Inc. (RMGX)
eMamba International Corp. (EMBA)
Family Room Entertainment Corp. (FMYR)
Genco Corp. (GNCC)
Global Digital Solutions, Inc. (GDSI)
Icon Media Holdings, Inc. (ICNM)
ITonis, Inc. (ITNS)
Lexico Resources International, Inc. (LXXI)
Markray Corp. (RVBR)
Quad Energy Corp (CDID)
Ree International, Inc. (REEI)
Smart-Tek Solutions, Inc. (STTN)
South Shore Resources, Inc. (SSHO)
TapSlide, Inc. (TSLI)
Velocity Energy Inc. (VCYE)
This link draws some interesting past connections between Corey Ribotsky and many companies that used Owen Naccarato as legal counsel
http://www.offshorealert.com/WorkArea/threadeddisc/print_thread.aspx?id=60&g=posts&t=37726
---------------------
I got side tracked though back to the 10Q
Then on November 21, 2011, the STTN 10Q for the 3rd quarter came out and it was ugly.
Cash on September 30, 2011 - $270,048
Cash on June 30, 2011 - $882,069
STTN lost $612,021 in cash during the 3rd quarter
Accounts payable and accrued liabilities on September 30, 2011 - $8,384,307
Accounts payable and accrued liabilities on June 30, 2011 - $3,256,689
STTN added $5,127,619 in accounts payable and accrued liabilities during the 3rd quarter
The accounts payable and accrued liabilities for the 2nd quarter was only $82,477
$5,127,619 is $2,129,070 more than STTN had in accounts payable and accrued liabilities for its entire existence from 1995 - through the 2nd quarter of 2011.
Why the $5,127,619 in accounts payable and accrued liabilities all in just a 3 month period? Who is all that money owed to?
Gross profit on September 30, 2011 (for 3rd quarter) - negative $155,177
Gross profit on June 30, 2011 (for 2nd quarter) - $1,685,183
STTN went from a profitable business to a company with a failing business. The cost of revenue for the 3rd quarter of 2011 was higher than the revenues themselves. They would have been better off not doing business in the 3rd quarter.
Subtract away the operation costs/expenses and
Overall operating loss on September 30, 2011 (for 3rd quarter) - $2,963,852
Overall operating loss on June 30, 2011 (for 2nd quarter) - $599,161
STTN's operating losses increased by $2,364,691.
During the 1st quarter of 2011 (the period ending March 31, 2011), STTN didn't have an operation loss. They had an operating gain of $363,354 after subtracting away all the costs of operations from the revenues for the quarter. It is obvious the direction that STTN is headed, and it is not good.
A further break down of the Selling, general and administrative expenses helps partially explain why STTN is headed down the toilet.
Salaries & Related Expense
1st quarter - $311,430
2nd quarter - $582,969
3rd quarter - $553,404
Consulting
1st quarter - $220,366
2nd quarter - $158,823
3rd quarter - $353,624
Commissions
1st quarter - $177,223
2nd quarter - $269,583
3rd quarter - $633,742
Outside Services
1st quarter - $31,688
2nd quarter - $62,403
3rd quarter - $211,287
Overall Selling, General, and Administrative Expenses
1st quarter - $1,272,515
2nd quarter - $1,685,183
3rd quarter - $1,997,439
Since revenues dropped by 28% from the 2nd quarter to the 3rd quarter why did commissions increase by 235% during that same stretch?
----------------
The most disturbing parts of the recent STTN filings:
#1) Brian Bonar paying himself $1,554,757.93 in shares for a $218,979 balance that was owed to him then writing off the payment in the books as a $218,979 stock expense.
#2) Brian Bonar issuing himself and his son a $500,000 debt Note for a group of revoked business entities.
#3) The $5,127,619 in accounts payable accrued during the 3rd quarter alone. Who is all that money owed to?
#4) STTN went from a positive balance sheet at the end of the 1st quarter to a failing business whose revenues cost more than what they make.
#5) The past connections and histories of the main players involved in STTN.
Why do I post regularly on this board?
Because Brian Bonar got my real identity through fraud and doesn't want me to tell the truth.
I HATE BULLIES !!!
Please elucidate on this point..
"MacFarlane's presentation would have raised red flags. Specifically, for the type of endpoints in those trials, Odds Ratio was a very peculiar choice, especially since there were no numbers reported about how many of the trial subjects crossed the threshold used for calculating the Odds Ratios. The reddest flag of all was the failure to include a comparison of the means for the ADCS-ADL endpoint. I believe these are the reasons Doc328, for example, sold his shares. Furthermore, the December 5 conference call was pathetic and did not confront the doubts raised by critics, including Feuerstein. I think Feuerstein is a schmuck, but not all his criticisms of the top-line results release, IIRC, were bull."
I encourage others to re-read and discuss the points you made in the replied to post. It is balanced, with a lot of food for thought.
I believe you and I would make ideal lead plaintiffs in a securities suit based on the CTAD presentation, but I'm not going to volunteer to do that. I wish the lawsuits would just disappear. (BTW, in contrast, I see no basis for a lawsuit based on the Rett results and PRs.)
I also want to include here, that I think Anavex's shot at approval is better than at any previous time since I've been involved. The combination of "Kun Jin's <0.025 p-value finesse," the recent biomarker results such as regarding brain volume, and the FDA's draft guidance negating the import of the ADCS-ADL endpoint, seems to me to make the P2b/3 trial results an excellent basis for approval. I wish, of course, that I'd sold on December 2 or 5, and that I were doing my AVXL purchasing now. I've sold over half my shares, very belatedly and at a big loss, but I still have a substantial amount. If I didn't already own "enough," I would now, in light of what I said earlier in this paragraph, be buying more.
Gee, if people have an effective treatment for CNS diseases they wont buy as much of the ... (shit that we already control) and we'll loose money.
I disagree...
"With 1.2 million traded per day. someone is picking up cheap shares every day!"
At least one party (more likely multiple colluding parteis) is running an AI algo designed to manipulate the price every day!!!
It will only be productive if they find enough suckers.....
The entire market is manipulated, but the most valuable stocks are manipulated more.
The advantage LWLG has is the huge block of retail that just isn't going to sell, no matter how much manipulation occurs.
Most of my shares are in my IRA and I just leave them alone....
It is common knowledge that AI can (and is IMO) be used to manipulate the market.
I disagree with the author of this paper's initial claim that there is "no evidience yet". I believe the huge pump and dump that occurred during the COVID lock up was a way to get government funds given to people and businesses during that time to gamble in an AI controlled market and channel a lot of those funds back to the Street people. The methods and dangers of AI are well described though.
How AI-powered Collusion in Stock Trading Could Hurt Price Formation
November 10, 2023 • 8 min read
There is no evidence yet of AI collusion hurting the financial markets, but the threat is real, warns a paper co-authored by Wharton’s Winston Wei Dou and Itay Goldstein.
As world leaders last week raised fears over runaway AI on the scale of a nuclear war or a pandemic, a more immediate and tangible frontier may well be the capital markets. The potential for AI technologies in capital markets to cause unintended effects arises when autonomous AI algorithms learn to act in concert automatically, either through a “price-trigger mechanism” that punishes deviations in trading behavior or through homogenized learning biases among algorithms, according to new research by experts at Wharton and elsewhere.
“Informed AI traders can collude and generate substantial profits by strategically manipulating low order flows, even without explicit coordination that violates antitrust regulations,” warned a research paper, titled “AI-Powered Trading, Algorithmic Collusion, and Price Efficiency,” by Wharton finance professors Winston Wei Dou and Itay Goldstein, and Yan Ji, professor of finance at the Hong Kong University of Science and Technology.
Quantitative hedge funds and leading investment firms like BlackRock and J.P. Morgan are already using AI, and that trend is gathering momentum across the financial markets. The SEC has recently given the green light to Nasdaq’s AI trading system, which utilizes reinforcement learning (RL) algorithms for making real-time adjustments. Wall Street has been clear so far of a scandal over AI-powered abuses, but the threat of that is palpable, according to Dou.
Red Flags on AI in Retailing, Manufacturing
Dou pointed to the Federal Trade Commission’s recent lawsuit accusing Amazon of using a secret algorithm to manipulate prices. “AI collusion in retail markets could drive prices to super-competitive levels as the algorithms learn to achieve and maintain coordination without any form of agreement, communication, or even intention,” he said. “Retailers and manufacturers have an incentive to gain market power without improving their product qualities. That’s why antitrust regulators are very nervous about this.”
Dou said their paper addresses those very concerns. “We have seen the rise of adoption of AI trading in financial markets, so naturally we would ask similar questions — whether AI collusion will arise in the financial markets,” he said. “If that’s the case, the question is whether we will see important adverse real consequences. If there’s AI collusion in the financial markets, market liquidity and price informativeness may be hurt.” Put another way, he said the worry is whether the markets will effectively facilitate liquidity and if market prices will reflect “real fundamental information.”
Goldstein noted that the Amazon case apart, there is an increasing worry of AI collusion in several other markets. “Our main question is whether something like that might be happening or could happen in financial markets. Financial markets generate another type of environment with their own nuances and complications.”
The Broad Reach of Financial Markets
Goldstein said he and his co-authors focused on financial markets for potential bad outcomes of AI-powered collusion because of the broader impact they have. “The way prices are formed in financial markets ends up having a real effect,” he said. “Firms rely on financial markets to a large extent (such as to raise capital), and so we need to understand the price formation process.”
Another reason the paper’s authors picked the financial markets is because of a paucity of research on their specific concerns. “There’s no scientific study on the outcomes and how AI trading would affect the market efficiency, including factors like price informativeness, market liquidity, and mispricing,” Dou said. The authors stated: “Our paper is one of the first few that study how the widespread adoption of AI-powered trading strategies would affect capital markets.”
The paper noted that the “integration of algorithmic trading and reinforcement learning, known as AI-powered trading, has significantly impacted capital markets.” Drawing from that observation, the authors created a virtual laboratory where they could study the effects of collusion between autonomous, self-interested AI trading algorithms. They developed “a model of imperfect competition among informed speculators with asymmetric information to explore the implications of AI-powered trading strategies on informed traders’ market power and price informativeness.”
A Lab to Study AI Behavior
Dou said their laboratory captures in a transparent manner the important features of the real financial market such as information asymmetry, price impact, and price efficiency. Within this laboratory, they ran trading algorithms to study their behavior and assess their influence on market liquidity and the informativeness of prices.
According to the paper, algorithmic collusion arises from two mechanisms: collusion through homogenized learning biases and collusion through punishment threat as in a price-trigger strategy. Biased learning, also known as “artificial stupidity,” arises because of insufficient learning about play at off-the-equilibrium-path information sets. Such learning biases are homogenized among AI traders due to the shared foundational models upon which they are developed. The collusion through the threat of punishment occurs to deter members of a cartel from breaking away, or “deviate from tacitly agreed upon behavior,” Dou explained.
In product markets such as the OPEC oil cartel, members can monitor deviations from agreed-upon behavior by tracking prices and volumes, and then hand out punishments to cartel-breakers such as blocking them from profitable deals. But high-frequency trading in the financial markets makes it difficult to monitor cartel-breakers. That is where AI-powered trading algorithms can learn to automatically trigger penalties for deviant behavior that market prices may reveal, Dou said. “Such collusion will incentivize all AI algorithms to stay within well-behaved trading strategies. No one will trade too aggressively relative to others.”
The upshot of that is that price informativeness will be hurt, due to market manipulation. “In a market with prevalent AI-powered trading, price efficiency and informativeness can be compromised due to both artificial intelligence and stupidity,” the paper noted.
Understanding the Psychology of Machines
In the lab they created, the paper’s authors became detectives looking for ways in which AI-powered trading algorithms might learn to collude without being detected. “What we were looking for is implicit collusion that occurs between machines,” Goldstein said. “They come to behave in a way that is difficult to detect. And that’s what we tried to figure out through this paper.” Added Dou: “The collusion automatically happens, even when each machine is 100% autonomous without any communication or intention of coordination.”
The lab studies showed the conditions under which collusion thrives. “Collusion through punishment threat (artificial intelligence) only exists when price efficiency and information asymmetry are not very high. However, collusion through homogenized learning biases (artificial stupidity) exists even when efficient prices prevail or when information asymmetry is severe,” the paper stated.
In order to study the way collusion among AI-powered trading algorithms can occur, the authors have to understand how machines think, so to speak. “Comprehending the dynamics of capital markets with the prevalence of AI-powered trading algorithms requires insights into algorithmic behavior akin to the ‘psychology’ of machines,” the paper stated.
What the Study Found
The study’s main findings included:
Informed AI speculators can collude and achieve supra-competitive profits by strategically manipulating excessively low order flows, even in the absence of agreement or communication that would constitute an antitrust infringement.
In scenarios where so-called “preferred-habitat investors play a substantial role in price formation, resulting in prices that are not highly efficient, tacit collusion among informed AI-powered speculators can be sustained through the use of price-trigger strategies.” (Preferred-habitat investors are typically long-term and insensitive to new short-run information.)
How effective AI collusion is depends on the level of information asymmetry in the market: To maintain collusion via a price-trigger punishment threat mechanism (artificial intelligence), the level of information asymmetry must not be too extreme, and there should not be an excessive number of informed speculators, conditions that mirror real-world scenarios.
In the scenario with high price efficiency or high information asymmetry, tacit collusion between AI-powered speculators can still be achieved through homogenized learning biases, reflecting artificial stupidity.
Regulators on a Vigil
Regulators are on high alert. Security and Exchange Commission (SEC) chair Gary Gensler recently cautioned against “the possibility of AI destabilizing the global financial market if big tech-based trading companies monopolize AI development and applications within the financial sector,” the paper noted. “[Regulators] have repeatedly highlighted the potential for AI to inadvertently amplify biases that could lurk in their designers, further jeopardizing competition and market efficiency.”
The findings of the paper serve as an early warning signal to both investors and regulators who want to prevent price distortions — and the broader implications of such distortions on the capital markets. But more research is required to draw insights that weigh both the good and bad outcomes of AI power, Goldstein said. “If you want to think about whether overall, AI technologies are helping or hurting the discovery of information through prices, broader investigation is needed for that. Our study brings to light one potential adverse effect.”
AI expert warns of algo-based market manipulation Regulators must keep pace with new technology that could be used to control financial markets By Eliot Raman Jones 24 Jan 2024 Tweet Facebook LinkedIn Save this article Send to Print this page “The prospect of supercharged manipulation is likely,” says Michael Wellman, a University of Michigan professor. “We know manipulation is already a very prevalent practice, and so if bodies are doing this, they’re going to try and avail themselves of the latest tools. We would be foolish not to expect to see intentional manipulation enhanced by AI.”
AI expert warns of algo-based market manipulation An artificial intelligence professor warns of machine learning ‘arms race’ for regulators against those seeking to use new technologies to manipulate financial markets. By Eliot Raman Jones 23 Jan 2024 Tweet Facebook LinkedIn Save this article Send to Print this page “The prospect of supercharged manipulation is likely. We know that manipulation is already a very prevalent practice and so if bodies are doing this, they’re going to try and avail themselves of the latest tools. We would be foolish not to expect to see intentional manipulation enhanced by AI.” So says Michael Wellman, a University of Michigan professor who earned his PhD in artificial intelligence from MIT in 1988 and has spent his career researching AI and its applications in economics.
This Article offers a novel perspective on the implications of increasingly autonomous and “black box” algorithms, within the ramification of algorithmic trading, for the integrity of capital markets. Artificial intelligence (AI) and particularly its subfield of machine learning (ML) methods have gained immense popularity among the great public and achieved tremendous success in many real-life applications by leading to vast efficiency gains. In the financial trading domain, ML can augment human capabilities in price prediction, dynamic portfolio optimization, and other financial decision-making tasks. However, thanks to constant
progress in the ML technology, the prospect of increasingly capable and autonomous agents to delegate operational tasks and even decision-making is now beyond mere imagination, thus opening up the possibility for approximating (truly) autonomous trading agents anytime soon.
Given these spectacular developments, this Article argues that such autonomous algorithmic traders may involve significant risks to market integrity, independent from their human experts, thanks to self-learning capabilities offered by state-of-the-art and innovative ML methods. Using the proprietary trading industry as a case study, we explore emerging threats to the application of established market abuse laws in the event of algorithmic market abuse, by taking an interdisciplinary stance between financial regulation, law and economics, and computational finance. Specifically, our analysis focuses on two emerging market abuse risks by autonomous algorithms: market manipulation and “tacit” collusion. We explore their likelihood to arise in global capital markets and evaluate related social harm as forms of market failures.
With these new risks in mind, this Article questions the adequacy of existing regulatory frameworks and enforcement mechanisms, as well as current legal rules on the governance of algorithmic trading, to cope with increasingly autonomous and ubiquitous algorithmic trading systems. We demonstrate how the “black box” nature of specific ML-powered algorithmic trading strategies can subvert existing market abuse laws, which are based upon traditional liability concepts and tests (such as “intent” and “causation”). We conclude by addressing the shortcomings of the present legal framework and develop a number of guiding principles to assist legal and policy reform in the spirit of promoting and safeguarding market integrity and safety.
BINGO !!!!
This was my perception...
Thanks for the confirmation.
The only reason I can think of is that EMA approval is more available by statute....
OK - correct me if I'm wrong...
The Aussies have data on Parkinson's and Alzheimer's.
There is Rett Syndrome data from the U.S. and Australia, but they are applying for EMA approval based on unique criteria in that jurisdiction..
https://www.nasdaq.com/articles/anavex-avxl-up-12-on-regulatory-update-for-alzheimers-drug
PLEASE DON'T DISS this post. Correct and add DD, but all purely negative criticism will be ignored.
I am looking to build a correct and succinct statement of Blarcamesine' s position in the regulatory matrix.
I believe in the AVXL science...
But as a stockholder I was seriously offended by the presentation of this page.
https://www.anavex.com/newsroom
Anyone got Missling's email address?
I disagree with your statement...
"The only answer we get is they are getting positive feed back from Teir1s but no one seems to be happy enough to pull the trigger on a deal."
IMO a much more likely answer is the deals were made, signed, sealed, and delivered...
But until LWLG clients have finished their development process and are ready to market, distribute, and go into their sell cycle....
.... you will continue to hear nothing.
This is as to be expected.
Maybe you should define and link to the definition of your acronym.
Your post is gibberish.
DFCO won't sell a single heat pump without a spec sheet...
Actually from what I've been reading today - if the Brits approve one indication and the Aussies approve another, they will both be approved in both jurisdictions.
Logic says that would also include Canada, anyone care to comment on this?
An unsupported claim is not "real due diligence"..
It's just the usual BONAR B.S.
The project is on - but DFCO isn't part of it.
DFCO has no IP
Meanwhile -check this out:
WHO
Daikin, Mitsubishi, Viessmann
WHEN
Now
We’ve entered the era of the heat pump.
Heat pumps are appliances that can cool and heat spaces using electricity. Many buildings today are still heated with fossil fuels, specifically natural gas. Switching to electric heat pumps that run on renewable energy could help homes, offices, and even manufacturing facilities cut their emissions dramatically.
While heat pumps have been used in buildings since the mid-20th century, the technology is breaking through in a new way. Global sales of heat pumps grew by 11% in 2022, the second consecutive year of double-digit growth, though that rate may have slowed in 2023. Europe saw the most dramatic shift, with a 40% growth in heat pump installations through 2022, largely driven by the energy crisis stemming from the Russia-Ukraine war and by efforts to move away from natural gas.
Related Story
Bosch employee opening the housing of a heat pump in a cold chamber
Everything you need to know about the wild world of heat pumps
Heat pumps could help address climate change and save you money. Here’s how they work.
Asia is another hot spot, with China leading global installations and China and Japan together accounting for more than half of new patents filed on heat pump technology since 2010. New approaches are enabling heat pumps to reach higher temperatures, which could allow the technology to help clean up industrial manufacturing by supplying power to generate steam used in food processing and paper making.
In total, heat pumps have the potential to cut global emissions by 500 million tons in 2030—as much as pulling all cars in Europe today off the roads. That would require the total number of heat pumps installed to reach about 600 million by the end of the decade. (That’s about 20% of the heating needs for all the world’s buildings.)
There are still big challenges ahead for heat pumps, including ramping production to meet rising demand and ensuring that the electrical grid is robust enough to supply electricity to these and other climate-focused technologies. But all signs indicate that heat pumps are entering their heyday.
2024
First they have to have one model that works...
No spec sheets tells me they don't have that.
They let go the only guy that knew what he was doing.
This is nonsense:
"in two trading days involving involving 75% of outstanding common shares, that is clearly a massive dump of the stock by shareholders"
The fact is that the MM's and Hedge funds can sell the same shares over and over while longs don't sell anything.
It's the AI manipulation of the market that allows this.
The entire market is manipulated, but the most valuable stocks are manipulated more.
I disagree with the author of this paper's initial claim that there is "no evidience yet". I believe the huge pump and dump that occurred during the COVID lock up was a way to get government funds given to people and businesses during that time to gamble in an AI controlled market and channel a lot of those funds back to the Street people. The methods and dangers of AI are well described though.
How AI-powered Collusion in Stock Trading Could Hurt Price Formation
November 10, 2023 • 8 min read
There is no evidence yet of AI collusion hurting the financial markets, but the threat is real, warns a paper co-authored by Wharton’s Winston Wei Dou and Itay Goldstein.
As world leaders last week raised fears over runaway AI on the scale of a nuclear war or a pandemic, a more immediate and tangible frontier may well be the capital markets. The potential for AI technologies in capital markets to cause unintended effects arises when autonomous AI algorithms learn to act in concert automatically, either through a “price-trigger mechanism” that punishes deviations in trading behavior or through homogenized learning biases among algorithms, according to new research by experts at Wharton and elsewhere.
“Informed AI traders can collude and generate substantial profits by strategically manipulating low order flows, even without explicit coordination that violates antitrust regulations,” warned a research paper, titled “AI-Powered Trading, Algorithmic Collusion, and Price Efficiency,” by Wharton finance professors Winston Wei Dou and Itay Goldstein, and Yan Ji, professor of finance at the Hong Kong University of Science and Technology.
Quantitative hedge funds and leading investment firms like BlackRock and J.P. Morgan are already using AI, and that trend is gathering momentum across the financial markets. The SEC has recently given the green light to Nasdaq’s AI trading system, which utilizes reinforcement learning (RL) algorithms for making real-time adjustments. Wall Street has been clear so far of a scandal over AI-powered abuses, but the threat of that is palpable, according to Dou.
Red Flags on AI in Retailing, Manufacturing
Dou pointed to the Federal Trade Commission’s recent lawsuit accusing Amazon of using a secret algorithm to manipulate prices. “AI collusion in retail markets could drive prices to super-competitive levels as the algorithms learn to achieve and maintain coordination without any form of agreement, communication, or even intention,” he said. “Retailers and manufacturers have an incentive to gain market power without improving their product qualities. That’s why antitrust regulators are very nervous about this.”
Dou said their paper addresses those very concerns. “We have seen the rise of adoption of AI trading in financial markets, so naturally we would ask similar questions — whether AI collusion will arise in the financial markets,” he said. “If that’s the case, the question is whether we will see important adverse real consequences. If there’s AI collusion in the financial markets, market liquidity and price informativeness may be hurt.” Put another way, he said the worry is whether the markets will effectively facilitate liquidity and if market prices will reflect “real fundamental information.”
Goldstein noted that the Amazon case apart, there is an increasing worry of AI collusion in several other markets. “Our main question is whether something like that might be happening or could happen in financial markets. Financial markets generate another type of environment with their own nuances and complications.”
The Broad Reach of Financial Markets
Goldstein said he and his co-authors focused on financial markets for potential bad outcomes of AI-powered collusion because of the broader impact they have. “The way prices are formed in financial markets ends up having a real effect,” he said. “Firms rely on financial markets to a large extent (such as to raise capital), and so we need to understand the price formation process.”
Another reason the paper’s authors picked the financial markets is because of a paucity of research on their specific concerns. “There’s no scientific study on the outcomes and how AI trading would affect the market efficiency, including factors like price informativeness, market liquidity, and mispricing,” Dou said. The authors stated: “Our paper is one of the first few that study how the widespread adoption of AI-powered trading strategies would affect capital markets.”
The paper noted that the “integration of algorithmic trading and reinforcement learning, known as AI-powered trading, has significantly impacted capital markets.” Drawing from that observation, the authors created a virtual laboratory where they could study the effects of collusion between autonomous, self-interested AI trading algorithms. They developed “a model of imperfect competition among informed speculators with asymmetric information to explore the implications of AI-powered trading strategies on informed traders’ market power and price informativeness.”
A Lab to Study AI Behavior
Dou said their laboratory captures in a transparent manner the important features of the real financial market such as information asymmetry, price impact, and price efficiency. Within this laboratory, they ran trading algorithms to study their behavior and assess their influence on market liquidity and the informativeness of prices.
According to the paper, algorithmic collusion arises from two mechanisms: collusion through homogenized learning biases and collusion through punishment threat as in a price-trigger strategy. Biased learning, also known as “artificial stupidity,” arises because of insufficient learning about play at off-the-equilibrium-path information sets. Such learning biases are homogenized among AI traders due to the shared foundational models upon which they are developed. The collusion through the threat of punishment occurs to deter members of a cartel from breaking away, or “deviate from tacitly agreed upon behavior,” Dou explained.
In product markets such as the OPEC oil cartel, members can monitor deviations from agreed-upon behavior by tracking prices and volumes, and then hand out punishments to cartel-breakers such as blocking them from profitable deals. But high-frequency trading in the financial markets makes it difficult to monitor cartel-breakers. That is where AI-powered trading algorithms can learn to automatically trigger penalties for deviant behavior that market prices may reveal, Dou said. “Such collusion will incentivize all AI algorithms to stay within well-behaved trading strategies. No one will trade too aggressively relative to others.”
The upshot of that is that price informativeness will be hurt, due to market manipulation. “In a market with prevalent AI-powered trading, price efficiency and informativeness can be compromised due to both artificial intelligence and stupidity,” the paper noted.
Understanding the Psychology of Machines
In the lab they created, the paper’s authors became detectives looking for ways in which AI-powered trading algorithms might learn to collude without being detected. “What we were looking for is implicit collusion that occurs between machines,” Goldstein said. “They come to behave in a way that is difficult to detect. And that’s what we tried to figure out through this paper.” Added Dou: “The collusion automatically happens, even when each machine is 100% autonomous without any communication or intention of coordination.”
The lab studies showed the conditions under which collusion thrives. “Collusion through punishment threat (artificial intelligence) only exists when price efficiency and information asymmetry are not very high. However, collusion through homogenized learning biases (artificial stupidity) exists even when efficient prices prevail or when information asymmetry is severe,” the paper stated.
In order to study the way collusion among AI-powered trading algorithms can occur, the authors have to understand how machines think, so to speak. “Comprehending the dynamics of capital markets with the prevalence of AI-powered trading algorithms requires insights into algorithmic behavior akin to the ‘psychology’ of machines,” the paper stated.
What the Study Found
The study’s main findings included:
Informed AI speculators can collude and achieve supra-competitive profits by strategically manipulating excessively low order flows, even in the absence of agreement or communication that would constitute an antitrust infringement.
In scenarios where so-called “preferred-habitat investors play a substantial role in price formation, resulting in prices that are not highly efficient, tacit collusion among informed AI-powered speculators can be sustained through the use of price-trigger strategies.” (Preferred-habitat investors are typically long-term and insensitive to new short-run information.)
How effective AI collusion is depends on the level of information asymmetry in the market: To maintain collusion via a price-trigger punishment threat mechanism (artificial intelligence), the level of information asymmetry must not be too extreme, and there should not be an excessive number of informed speculators, conditions that mirror real-world scenarios.
In the scenario with high price efficiency or high information asymmetry, tacit collusion between AI-powered speculators can still be achieved through homogenized learning biases, reflecting artificial stupidity.
Regulators on a Vigil
Regulators are on high alert. Security and Exchange Commission (SEC) chair Gary Gensler recently cautioned against “the possibility of AI destabilizing the global financial market if big tech-based trading companies monopolize AI development and applications within the financial sector,” the paper noted. “[Regulators] have repeatedly highlighted the potential for AI to inadvertently amplify biases that could lurk in their designers, further jeopardizing competition and market efficiency.”
The findings of the paper serve as an early warning signal to both investors and regulators who want to prevent price distortions — and the broader implications of such distortions on the capital markets. But more research is required to draw insights that weigh both the good and bad outcomes of AI power, Goldstein said. “If you want to think about whether overall, AI technologies are helping or hurting the discovery of information through prices, broader investigation is needed for that. Our study brings to light one potential adverse effect.”
AI expert warns of algo-based market manipulation Regulators must keep pace with new technology that could be used to control financial markets By Eliot Raman Jones 24 Jan 2024 Tweet Facebook LinkedIn Save this article Send to Print this page “The prospect of supercharged manipulation is likely,” says Michael Wellman, a University of Michigan professor. “We know manipulation is already a very prevalent practice, and so if bodies are doing this, they’re going to try and avail themselves of the latest tools. We would be foolish not to expect to see intentional manipulation enhanced by AI.”
AI expert warns of algo-based market manipulation An artificial intelligence professor warns of machine learning ‘arms race’ for regulators against those seeking to use new technologies to manipulate financial markets. By Eliot Raman Jones 23 Jan 2024 Tweet Facebook LinkedIn Save this article Send to Print this page “The prospect of supercharged manipulation is likely. We know that manipulation is already a very prevalent practice and so if bodies are doing this, they’re going to try and avail themselves of the latest tools. We would be foolish not to expect to see intentional manipulation enhanced by AI.” So says Michael Wellman, a University of Michigan professor who earned his PhD in artificial intelligence from MIT in 1988 and has spent his career researching AI and its applications in economics.
This Article offers a novel perspective on the implications of increasingly autonomous and “black box” algorithms, within the ramification of algorithmic trading, for the integrity of capital markets. Artificial intelligence (AI) and particularly its subfield of machine learning (ML) methods have gained immense popularity among the great public and achieved tremendous success in many real-life applications by leading to vast efficiency gains. In the financial trading domain, ML can augment human capabilities in price prediction, dynamic portfolio optimization, and other financial decision-making tasks. However, thanks to constant
progress in the ML technology, the prospect of increasingly capable and autonomous agents to delegate operational tasks and even decision-making is now beyond mere imagination, thus opening up the possibility for approximating (truly) autonomous trading agents anytime soon.
Given these spectacular developments, this Article argues that such autonomous algorithmic traders may involve significant risks to market integrity, independent from their human experts, thanks to self-learning capabilities offered by state-of-the-art and innovative ML methods. Using the proprietary trading industry as a case study, we explore emerging threats to the application of established market abuse laws in the event of algorithmic market abuse, by taking an interdisciplinary stance between financial regulation, law and economics, and computational finance. Specifically, our analysis focuses on two emerging market abuse risks by autonomous algorithms: market manipulation and “tacit” collusion. We explore their likelihood to arise in global capital markets and evaluate related social harm as forms of market failures.
With these new risks in mind, this Article questions the adequacy of existing regulatory frameworks and enforcement mechanisms, as well as current legal rules on the governance of algorithmic trading, to cope with increasingly autonomous and ubiquitous algorithmic trading systems. We demonstrate how the “black box” nature of specific ML-powered algorithmic trading strategies can subvert existing market abuse laws, which are based upon traditional liability concepts and tests (such as “intent” and “causation”). We conclude by addressing the shortcomings of the present legal framework and develop a number of guiding principles to assist legal and policy reform in the spirit of promoting and safeguarding market integrity and safety.
I know someone who either doesn't understand the science or is pretending to.
Neither of those events warranted that sort of a sell off.
You made my point.
Trane has a dozen home heat pumps to choose from, and more in the commercial line:
https://www.trane.com/residential/en/products/heat-pumps/
https://www.trane.com/commercial/north-america/us/en/products-systems/heat-pumps.html
The "WGT fringe group" is a figment of your imagination.
Your post is self contradictory....
Probably 70% of all trades are driven by machines following sophisticated algorithms benefitting shorts, market makers, and other nefarious players. That accounts for most of the daily trading volatility and can result in prolonged periods of stock price downturns. But as demonstrated above, it has been AVXL news events that have destroyed AVXL's stock price. AVXL remains a highly damaged stock that remains in a downtrend in the absence of good news. It's pretty simple.
There is no "informative and detailed web page" and there is no evidence that DFCO is still working with Oak Ridge National Laboratory or any branch of the Federal government.
LOL!!!!!
Your search - dalrada site:ornl.gov - did not match any documents.