Jefferies Financial Group Analysts Lower Earnings Estimates for The Procter & Gamble Company (NYSE:PG)

Jefferies Financial Group Analysts Lower Earnings Estimates for The Procter & Gamble Company (NYSE:PG)

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The Procter & Gamble Company (NYSE:PG – Get Rating) – Equities research analysts at Jefferies Financial Group decreased their Q3 2023 EPS estimates for shares of Procter & Gamble in a research note issued on Wednesday, March 1st. Jefferies Financial Group analyst K. Grundy now expects that the company will earn $1.29 per share for the quarter, down from their previous estimate of $1.31. The consensus estimate for Procter & Gamble’s current full-year earnings is $5.84 per share. Jefferies Financial Group also issued estimates for Procter & Gamble’s FY2023 earnings at $5.81 EPS.

PG has been the topic of a number of other research reports. Wolfe Research began coverage on shares of Procter & Gamble in a report on Monday, November 21st. They set an “outperform” rating and a $156.00 price objective on the stock. Deutsche Bank Aktiengesellschaft increased their price objective on shares of Procter & Gamble from $156.00 to $162.00 and gave the stock a “buy” rating in a report on Tuesday, December 6th. Barclays decreased their price objective on shares of Procter & Gamble from $161.00 to $158.00 and set an “overweight” rating on the stock in a report on Monday, January 23rd. Credit Suisse Group decreased their price objective on shares of Procter & Gamble from $140.00 to $130.00 and set a “neutral” rating on the stock in a report on Tuesday, November 15th. Finally, Raymond James increased their price objective on shares of Procter & Gamble from $165.00 to $170.00 and gave the stock an “outperform” rating in a report on Friday, January 13th. Four investment analysts have rated the stock with a hold rating and ten have assigned a buy rating to the stock. Based on data from MarketBeat, Procter & Gamble has a consensus rating of “Moderate Buy” and an average target price of $155.67.

Procter & Gamble Stock Up 1.6 {21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}

PG opened at $139.93 on Friday. Procter & Gamble has a twelve month low of $122.18 and a twelve month high of $164.90. The firm’s 50-day simple moving average is $144.52 and its 200-day simple moving average is $141.21. The company has a debt-to-equity ratio of 0.47, a quick ratio of 0.37 and a current ratio of 0.56. The firm has a market cap of $330.11 billion, a price-to-earnings ratio of 24.55, a PEG ratio of 3.84 and a beta of 0.40.

Procter & Gamble (NYSE:PG – Get Rating) last posted its earnings results on Thursday, January 19th. The company reported $1.59 earnings per share for the quarter, topping the consensus estimate of $1.58 by $0.01. Procter & Gamble had a net margin of 17.79{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} and a return on equity of 32.03{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}. The business had revenue of $20.77 billion for the quarter, compared to analyst estimates of $20.75 billion. During the same quarter in the prior year, the firm earned $1.66 EPS. The firm’s quarterly revenue was down .9{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} compared to the same quarter last year.

Institutional Trading of Procter & Gamble

Several hedge funds have recently bought and sold shares of the stock. EWG Elevate Inc. bought a new position in shares of Procter & Gamble during the 4th quarter worth approximately $26,000. Silicon Valley Capital Partners bought a new position in shares of Procter & Gamble during the 4th quarter worth approximately $28,000. Legend Financial Advisors Inc. bought a new position in shares of Procter & Gamble during the 3rd quarter worth approximately $30,000. Luken Investment Analytics LLC bought a new position in shares of Procter & Gamble during the 4th quarter worth approximately $37,000. Finally, Kepos Capital LP bought a new position in shares of Procter & Gamble during the 4th quarter worth approximately $38,000. 62.13{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} of the stock is currently owned by institutional investors and hedge funds.

Insider Activity at Procter & Gamble

In related news, CFO Andre Schulten sold 1,311 shares of the stock in a transaction that occurred on Wednesday, March 1st. The shares were sold at an average price of $137.34, for a total value of $180,052.74. Following the sale, the chief financial officer now directly owns 35,142 shares in the company, valued at approximately $4,826,402.28. The sale was disclosed in a filing with the Securities & Exchange Commission, which is accessible through this hyperlink. In related news, CEO Jon R. Moeller sold 2,151 shares of the stock in a transaction that occurred on Wednesday, March 1st. The shares were sold at an average price of $137.34, for a total value of $295,418.34. Following the sale, the chief executive officer now directly owns 226,748 shares in the company, valued at approximately $31,141,570.32. The sale was disclosed in a filing with the Securities & Exchange Commission, which is accessible through this hyperlink. Also, CFO Andre Schulten sold 1,311 shares of the firm’s stock in a transaction that occurred on Wednesday, March 1st. The stock was sold at an average price of $137.34, for a total transaction of $180,052.74. Following the sale, the chief financial officer now owns 35,142 shares in the company, valued at $4,826,402.28. The disclosure for this sale can be found here. Insiders have sold 4,766 shares of company stock worth $656,511 in the last ninety days. Company insiders own 0.26{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} of the company’s stock.

Procter & Gamble Dividend Announcement

The firm also recently announced a quarterly dividend, which was paid on Wednesday, February 15th. Shareholders of record on Friday, January 20th were given a dividend of $0.9133 per share. The ex-dividend date was Thursday, January 19th. This represents a $3.65 dividend on an annualized basis and a yield of 2.61{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}. Procter & Gamble’s payout ratio is presently 64.04{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}.

About Procter & Gamble

(Get Rating)

Procter & Gamble Co engages in the provision of branded consumer packaged goods. It operates through the following segments: Beauty, Grooming, Health Care, Fabric & Home Care, and Baby, Feminine & Family Care. The Beauty segment offers hair, skin, and personal care. The Grooming segment consists of shave care like female and male blades and razors, pre and post shave products, and appliances.

See Also

Earnings History and Estimates for Procter & Gamble (NYSE:PG)

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Tesla stock pops after China sales jump year-over-year

Tesla stock pops after China sales jump year-over-year

Tesla (TSLA) inventory jumped Friday after the automaker reported potent China income.

Tesla’s wholesale shipments for February from its China manufacturing facility rose 32{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} from a 12 months ago to 74,402 motor vehicles, in accordance to China’s Passenger Car Association (CPCA). That also determine represents a 13{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} soar thirty day period about thirty day period from January.

Shares of the EV maker were being up practically 3.6{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} Friday.

The increase in February shipments is not stunning supplied that CPCA explained February product sales of new energy vehicles, which involve battery electric and hybrid income, rose by 30{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} overall. And it pointed out very last month that January would be a “weak” thirty day period for in general gross sales in the area because of to the Chinese New Calendar year.

Nevertheless, stronger revenue in February for Tesla is a favourable development as levels of competition rises in the important Chinese EV sector, where Tesla is obtaining an increasing total of its world profits.

“Tesla’s continued growth in China need to occur as no surprise,” Chandan Kumar, head of merchandise at index provider Indxx, mentioned. “As a reasonable consequence of this Tesla, even with what numerous People in america may possibly think, only gets around 31{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} of its total income from the US, with the relaxation fundamentally all from China and Europe.”

A visitor checks a Tesla Model 3 car at a showroom of the U.S. electric vehicle (EV) maker in Beijing, China February 4, 2023. REUTERS/Florence Lo

A customer checks a Tesla Model 3 automobile at a showroom of the U.S. electric car (EV) maker in Beijing, China February 4, 2023. REUTERS/Florence Lo

Current selling price cuts in January of the Chinese-created Product 3 and the Design Y — which were lower by 13.5{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} and 10{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}, respectively — are obviously supplying Tesla a enhance in the area, in spite of rivals like BYD outselling them in February. BYD’s new power vehicle income jumped by above 100{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} to 193,655. In the meantime, Tesla’s share of the new strength marketplace in China slipped to 9{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} from 10{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} when BYD’s share rose to 37{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} from 27{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}, according to the CPCA.

Tom Zhu, Tesla’s head of global producing (and very likely heir clear to CEO Elon Musk), tackled considerations about desire in China before this week at Tesla’s Investor Day.

“As extensive as you offer you a merchandise with benefit at an economical rate, you don’t have to fear about desire,” Zhu said in the course of the Q&A part of the function late Wednesday night. Zhu famous the selling price cuts in China “generated big demand from customers, extra than we can develop, really.” Tesla also cut costs in Australia, Japan, and South Korea in purchase to gin up demand from customers.

Tesla's China website (3/3/2023)

Tesla’s China web page (3/3/2023)

The rate cuts of program ended up cheered by new Tesla prospective buyers in China but were being satisfied with deep resentment and protests by current buyers who had been not given a refund or other sorts of compensation, this kind of as cost-free charging, when the selling price cuts had been declared.

According to Tesla’s China internet site, the existing wait time for all versions of the built-to-purchase Product 3 stands at one particular to four months, and the hold out time for the Design Y SUV (RWD and dual motor) is two to five months.

Pras Subramanian is a reporter for Yahoo Finance. You can abide by him on Twitter and on Instagram.

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Business-immigration group alarmed over DeSantis proposal to repeal in-state tuition for Dreamers

Business-immigration group alarmed over DeSantis proposal to repeal in-state tuition for Dreamers

MIAMI — A group of businesses, college students and local community leaders expressed alarm Thursday about Florida Gov. Ron DeSantis’ proposal to reverse a regulation that enables undocumented immigrants to shell out in-state college or university and university tuition.

DeSantis, who is anticipated to start a bid for president, has proposed reversing the 2014 evaluate as element of a bundle of laws cracking down on unlawful immigration.

“It under no circumstances happened to me in 2014 that we would be convening yet again to deal with the challenge of in-state tuition,” Eduardo Padrón, former president of Miami Dade Faculty, claimed Thursday at a information convention in Miami.

The news convention was structured by the American Business Immigration Coalition, or ABIC, a bipartisan team that advocates for immigration reform.

“This is an concern of fairness and common feeling and it’s excellent for our economic system. If you place roadblocks at a time when there is excellent have to have in fields like engineering, medical professionals, nursing, it’s an unwell-advised and ill-conceived thought,” mentioned Padrón, a former board chair of the Association of American Colleges and Universities.

About 40,000 college students enrolled in bigger education and learning in Florida are deemed undocumented, with about 12,000 suitable for DACA and about 28,000 ineligible, according to the Better Training Immigration Portal. Each yr about 5,000 Florida learners who do not have permanent authorized position graduate from large school in the point out. DACA, or Deferred Motion for Childhood Arrivals, presents young immigrants who ended up introduced to the U.S. as small children temporary security from deportation and permission to legally perform.

The legislation making in-condition tuition out there to Florida pupils who deficiency authorized immigration standing, also known as Dreamers, was signed by then-Gov. Rick Scott, a Republican now in the U.S. Senate. Though it was opposed at the time by conservatives in the Legislature, it was backed by a range of Republicans, such as Lt. Gov. Jeanette Núñez, then in the Florida Home of Representatives.

Although some Republicans who backed the law have been silent on the concern, Scott has criticized DeSantis’ proposal as “unfair.” 

He just lately informed reporters in Tampa that “it’s a bill that I was proud to indication. … It’s a monthly bill I would indication once again nowadays.”

Florida is one of 23 states, alongside with Washington, D.C., that permit college students with no long-lasting lawful status who attended superior faculty in the respective state or Washington, D.C., to pay in-state tuition.

In-point out tuition and affordability for Dreamers has been backed by reasonable Republicans and the organization sector, as properly as Democrats and immigrant groups who argue that expanding educational alternatives is superior for the overall financial state.

“Florida would only be handicapping alone by getting away in-condition tuition prices for undocumented youthful folks that the state has previously invested in for their K-12 several years,” Mike Fernandez, chairman of MBF Healthcare Partners and co-chair of ABIC, explained in a information release.

“The whole issue of making postsecondary education obtainable to them, apart from standard fairness and decency, is to facilitate their heading into the fields exactly where Florida most desperately requirements upcoming staff,” Fernandez mentioned. “Not to mention that the more skilled they turn into and the additional they gain, the extra they’ll put into condition and area tax revenues, not to mention the economic system general.”

DeSantis and other Republicans have shifted noticeably on the issue of immigration because Donald Trump was elected president in 2016 on tricky-line immigration positions.

“We operate actually tough to make greater training cost-effective for Floridians, and we’re very pleased of that. We have the most economical better instruction in the country,” DeSantis mentioned at a news meeting previous 7 days. “We have had inflation. The fees have modified. If we want to keep the line on tuition, then you’ve got obtained to say, you require to be a U.S. citizen who life in Florida. Why would we subsidize non-U.S. citizens when we want to make positive we want to keep it affordable for our personal individuals?”

Requested for remark on the criticisms, DeSantis’ workplace referred to the governor’s past remarks. The business did not immediately reply to a ask for for any details or exploration exhibiting the influence of the students’ spending in-state tuition on climbing tuition prices.

Supporters of the 2014 legislation say numerous students who do not have lawful position would not attend at all if they had been not supplied the cost split.

Murilo Alves, 25, is a healthcare faculty university student who arrived from Brazil when he was 3 a long time aged. He is enrolled in DACA, which allows younger persons who qualify to operate and study in the U.S. The authorization is non permanent, has to be renewed each individual two a long time and is staying challenged in court docket by Republicans.

Alves compensated in-state-tuition for his undergraduate diploma at Florida Atlantic College, and is now a first-year health care faculty pupil at Nova Southeastern University.

Alves credits Florida’s current regulation for making it possible for him to pursue bigger education and learning.

“It was extremely difficult to get here, but I’m pretty grateful. The crucial component is I would have not been able to do any of this if it weren’t for in-condition tuition, that was crucial to get to exactly where I am suitable now,” he mentioned.

“I’m particularly grateful that we experienced that benefit. I’m hopeful now that by us sharing our tales that we can stop this legislation that Governor DeSantis is attempting to move,” Alves said.

Adani Bonds Flash Warnings Even as $153 Billion Stock Rout Eases

Adani Bonds Flash Warnings Even as $153 Billion Stock Rout Eases

(Bloomberg) — The historic marketplace meltdown of Adani Team has shown indicators of abating immediately after the Indian conglomerate went on a tour to restore assurance and received a $1.9 billion expense from a large-profile funds manager.

Most Go through from Bloomberg

But a closer glance at billionaire Gautam Adani’s empire displays that even though fears of a personal debt blowup in the up coming three many years have receded, buyers still have doubts about the group’s for a longer time-time period compensation talents. The ports-to-electric power conglomerate’s inventory-industry slump and uncertainties in excess of credit rankings carry on to lover worries about its accessibility to resources next a shorter seller’s allegations of fraud.

These types of worries have lingered even after the Adani Group renewed efforts to appease traders throughout a a few-day roadshow this 7 days in Singapore and Hong Kong, wherever executives claimed the conglomerate has enough money to repay debt because of more than the following three decades. A relatives trust also marketed 154.5 billion rupees ($1.9 billion) of stock in 4 firms to GQG Companions, the US-primarily based revenue supervisor led by Rajiv Jain.

“It’s definitely beneficial he’s running to promote some of his holdings and increase some cash,” claimed Kamil Dimmich of North of South Money. “If we can see that motor resume the place he can accessibility economical marketplaces once more, that could stabilize issues,” he reported, referring to the billionaire.

The Adani Group has also reduce expenditures and manufactured early financial debt reimbursement to ease a rout that has erased $153 billion from its shares because US-based mostly Hindenburg Research’s fraud allegations, which it has denied. The adhering to indicators will probably verify key to income managers’ choices on the conglomerate, as its crisis of self esteem proceeds to unfold.

Bond Possibility

When most of Adani Group’s 15 dollar bonds are off their lows hit ideal following Hindenburg’s Jan. 24 report, all but one are still in the red.

The group’s 4 notes because of by the stop of 2026 are trading involving 84 cents to 94 cents on the dollar, down from 91 cents to 99 cents just before the report, but still indicating comparatively small-payment risk.

It is a diverse image for bonds with maturities additional down the highway. 7 of the group’s 11 notes due in or right after 2027 are buying and selling under or in close proximity to 70 cents on the dollar, a level that defines distress or severe problems about well timed payment.

China’s Mega Restructure Difficulties in India: Credit history Edge Podcast

Three of the 6 namesake businesses have financial debt maturing about the up coming 12 months that exceed funds balances, the team stated in a credit history report last month.

The mismatch is the widest for Adani Energy Ltd., which has 33.5 billion rupees of personal debt obligations in the upcoming 13 months, vs . a cash stability of 19.3 billion rupees as of Dec. 31.

To be positive, the a few corporations may still be equipped to bridge the hole with long run earnings. Adani Power’s hard cash move stood at 118.4 billion rupees this fiscal calendar year, while Adani Ports and Particular Economic Zone Ltd. has 84.3 billion rupees and Adani Complete Gasoline Ltd. has 9.3 billion rupees, the report mentioned.

Inventory Slump

Right after a rout that experienced erased virtually two-thirds of their mixed industry benefit, the group’s 10 stocks staged a collective rebound Wednesday for the initial time due to the fact Hindenburg’s report, led by a in the vicinity of 15{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} surge in flagship Adani Enterprises Ltd.

The most recent gains have served reduce the conglomerate’s industry wipeout to about $140 billion from a peak of $153 billion.

But, it’s nonetheless early times. Adani Whole Gas, Adani Transmission Ltd. and Adani Green Strength Ltd., which racked up the major losses, stay 70{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} to 80{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} decreased from their Jan. 24 amounts. Hindenburg reported in its report that Adani’s 7 key namesake stocks had sky-large valuations and faced draw back of 85{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996}.

Shaky Scores

Moody’s Buyers Services, which slice its outlook for Adani Inexperienced and 3 other companies to adverse from stable past thirty day period, claimed that refinancing maturing financial debt, adjustments to capital-investing programs, and cash-raising initiatives are crucial variables to look at.

Even more scores steps could adhere to if the firms’ capacity to raise cash is appreciably curtailed, there is a important boost in borrowing costs or a deterioration of fundamentals.

Equally, S&P Worldwide Ratings also downgraded the Adani Group’s outlook to detrimental in February. It said that Indian banks will most likely charge higher chance rates and become excess careful in the aftermath of the crisis.

Financial investment Phone calls

Of the 7 critical companies, 5 have minuscule analyst coverage and even for the other two with far more pursuing, the brutal selloff appears to have made a restricted effect on the brokerages’ perceptions.

Flagship Adani Enterprises., which is tracked by only two brokerages, is split involving a acquire and a keep recommendation, in accordance to Bloomberg-compiled data. Adani Ports, the crown jewel and a component of India’s benchmark NSE Nifty 50 Index, is the most adopted and has elevated its tally of acquire phone calls to 21 from 20 ahead of the disaster.

“What is lacking below, what no person talked about, was these are phenomenal, irreplaceable assets,” Jain, chairman of GQG reported. “You have to be greedy when people today are fearful.”

But some aren’t convinced. “Investors should still keep away from these shares since they are remarkably unstable,” said Karthick Jonagadla, main govt of Mumbai-dependent Quantace Investigate & Funds Pvt. “If any trader marketed these shares a several weeks ago simply because of a whistleblower report and wants to get now since they are low-cost, such trades are mere speculation and lack fundamentals.”

ESG Retreat

The disaster also has spilled more than into the ESG market place, prompting the asset administration unit of JPMorgan Chase & Co. to wipe its applicable portfolios thoroughly clean of publicity to the Adani empire.

India’s best court docket explained Thursday it has established up a panel to probe allegations against Adani Group. It also requested the area markets regulator to look into any manipulation in the group’s shares and notify about its findings in two months.

–With help from Ishika Mookerjee, Divya Patil and Bhuma Shrivastava.

(Provides dollars balances specifics in ninth to 11th paragraphs, and chart.)

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RIA Roundup: Lazard Acquires Truvvo, Creates $8B Family Office

RIA Roundup: Lazard Acquires Truvvo, Creates $8B Family Office

Registered investment advisors announced more than $18.8 billion in transacted assets this week, an indication that M&A in the space has not slowed as much as some predicted.

Lazard Asset Management and Truvvo Partners combined to create Lazard Family Office Partners, while Stratos Wealth Partners took ownership of First Wealth Financial Group in the wake of the sudden death of its CEO.

Meanwhile, Beacon Pointe announced it completed five acquisitions over the past three months, Pathstone is set to acquire $1.5 billion in assets and Merchant-backed Legacy Capital added $365 million in Arkansas. At the same time, Hightower facilitated the first tuck-in for partner firm Schultz Collins, while Snowden Lane lured another Morgan Stanley advisor.

In stories published earlier this week, Integrated Partners and Falcon Wealth Planning each announced their first acquisitions ever, Americana Partners added a $6 billion Houston RIA and Clearstead purchased its second trust business.

Lazard Asset Management Acquires Truvvo Partners, Creating Family Office

Lazard Asset Management, which manages about $216 billion in assets, announced it acquired Truvvo Partners, a New York City-based RIA with $3.8 billion in assets that provides strategic advice, wealth planning and investment management to families.

Together, the firms have formed Lazard Family Office Partners to manage approximately $8 billion in assets—including Lazard’s existing U.S. private client business—and provide advice and investment solutions across public and private markets. The family office will integrate investment management, risk management and family office services into one offering.

As a result of the deal, Lazard’s global wealth management division now oversees approximately $22 billion in client assets, including a European wealth management business.

“Demand for sophisticated and innovative wealth management solutions is increasing as family offices navigate the ever-changing markets and economic environment,” Lazard CEO Evan Russo said in a statement.

“We believe leveraging Lazard’s expertise, infrastructure and resources will strengthen our platform and enable a holistic approach, allowing us to better serve our clients,” added Truvvo CEO and CIO Casey Whalen.

Lazard’s global investment franchise is expected to complement Truvvo’s open-architecture platform and expertise in private markets, according to the announcement. The family office unit will provide investment management, as well as expertise in wealth transfer, tax planning, philanthropy, operational solutions, cash flow and liquidity planning.

The Truvvo team, which will be based in Lazard’s New York office, includes Whalen, Jerome Antenen, Alison Rosenzweig, Caitlin Reynolds and Danielle Roseman.

One of the world’s largest asset management firms, Lazard currently operates out of 26 countries on five continents, providing a wide range of financial advice and management to corporations, partnerships, institutions, governments and individuals.

The firm celebrates its 175th anniversary this year.

Stratos Wealth Partners Expands Ownership Stake in First Wealth

Stratos Wealth Partners, an RIA of Stratos Wealth Holdings, expanded its ownership in First Wealth Financial Group to a majority stake, following the unexpected passing of Founder and CEO Breton Williams.

The owner and leadership transitions are effective immediately, according to Thursday’s announcement, “with no impact to the firm’s operations.” As a part of the transition, minority owner Andrew Meyers has been named president of First Wealth.

“As we continue processing the loss of our friend and colleague, we are grateful that Breton had such a detailed business continuity plan in place,” Meyers said in a statement. “I want to assure our clients that First Wealth’s team of advisors and staff is committed to providing the valued investment advice and financial planning care they have become accustomed to. Our strengthened partnership with Stratos will allow us to build an even greater business and provide additional services to these loyal clients.”

Established in Clinton, Iowa, in 1987, First Wealth oversees more than $348 million in combined brokerage and advisory assets. The firm provides investment management and retirement, estate, pension and tax-favored planning. Stratos has been a non-ownership partner in the firm for eight years, supporting growth as it expanded to six advisors in four locations.

“Breton was a well-respected member of the wealth management community in Iowa, who cared deeply about the well-being of his clients and community, and will be sorely missed,” said Charles Shapiro, founding partner and Chief Development Officer at Stratos. “On behalf of Stratos, I extend my condolences to the Williams family, staff of First Wealth and clients whose lives Breton improved over the years. We are honored to build on his legacy alongside Andrew and the First Wealth team, providing an exceptional client experience and growing the firm.”

Meyers, an advisor with First Wealth since 2011, recently stepped into a leadership role as part of the planned succession. Working with Senior Client Service Representative Cari Bush, Meyers began implementing the plan established by the late Williams to “ensure a seamless transition for clients.”

Stratos Wealth Partners manages more than $9.6 billion in advisory assets and advises on more than $6.9 billion in brokerage and third-party assets held away at LPL Financial. The platform offers infrastructure and operational, strategic and revenue-generating resources to growth-minded firms. Since its founding, Stratos has grown to 275 independent advisors, with more than 60 home office staff and more than 87 locations nationwide.

Beacon Pointe Adds Five RIAs in Three Months

Newport Beach, Calif.-based Beacon Pointe Advisors completed five RIA acquisitions over the last three months, according to an announcement, with three deals closing at the end of 2022 and two closing earlier this year.

Midwest Financial Advisor Group, Nexus Wealth Advisors, Pinnacle Wealth Management, Ailsa Capital and Bennicas & Associates have become Beacon Pointe regional offices in new and existing markets and extend the firm’s footprint to additional states, including Illinois, Michigan and Utah.

They added a combined $1.5 billion in assets under management, bringing Beacon Pointe to approximately $25 billion in AUM and 46 offices nationwide.

“Coming off of a busy year of M&A activity in 2021, it was great to keep that same momentum in 2022,” Beacon Pointe President Matt Cooper said in a statement. “Not only did we expand into several new territories, including three new offices in the Midwest, but we added further density in existing markets that we have been pursuing for quite some time.”

With office locations in Skokie, Ill., and Bloomfield Hills, Mich., Midwest Financial Advisor Group brings Beacon Pointe to both states for the first time. Serving clients in the greater Chicago region with around $300 million in assets, husband and wife founders Heather O’Neill Fairbanks and Isamu Fairbanks lead the five-person team.

“A big part of what we were looking for when searching for the right partner was a firm that could provide us the back-office support and resources we needed while still fostering a sense of community and culture that we aligned with,” said O’Neill Fairbanks. “Those elements paired with initiatives of Beacon Pointe’s Women’s Advisory Institute is what truly drew us into the firm.”

Pinnacle Wealth Management joins Beacon Pointe with $155 million in assets under management and expands the firm’s presence in the Denver region. Joined by a team of six, President Tom Stefaniak is taking on the role of managing director at Beacon Pointe.

“I was fortunate to have heard about Beacon Pointe through an existing partner at the firm,” Stefaniak said. “We’re excited to begin leveraging the robust platform and technology Beacon Pointe has cultivated over the years.”

Ailsa Capital will become Beacon Pointe’s first office in the state of Utah, with around $210 million in client assets. John Martindale is joining as managing director and bringing a team of three.

“The depth of Beacon Pointe’s service offerings, particularly from a client standpoint, was what truly drew us into the firm from the outset,” said Martindale. “That, paired with established back-office services that would enable us to spend more time with our clients, was one of the main drivers of our decision to partner with Beacon Pointe.”

With $240 million in assets under management, Bennicas & Associates is located in Portola Valley, Calif., and will be joining one of Beacon Pointe’s existing Bay Area office locations. Founder Georgia Bennicas is joining as partner and senior wealth advisor, along with advisor Michael Dunn and two staff members.

Nexus Wealth Advisors, located in Santa Cruz, Calif., is an extension of Beacon Pointe’s existing Bay Area office in Campbell. Nexus founder Lance Wexler and his team will continue serving clients in the Santa Cruz County and South Bay Area.

Financial terms of the deals were not disclosed.

Pathstone Will Acquire Rex Capital Advisors

Pathstone, a partner-owned and private equity-backed RIA serving families, family offices, foundations and endowments, entered into an agreement to acquire Rex Capital Advisors. Based in Providence, R.I., Rex provides investment advisory and family office services to ultra-high-net-worth families and related entities.

Founded in 2002 by Arthur Duffy, Rex Capital originally served as a single-family office. Working with Michael Chase, Matthew Thibault and Timothy Devlin, Rex has grown to advise 12 client families across the U.S., representing approximately $1.5 billion in assets. In addition to customized family office solutions, the Rex team brings private equity and venture capital expertise.

Once the deal has closed, the Rex team will have access to Pathstone’s infrastructure, expanded services and talent to accelerate growth.

“From the first conversation with Arthur and his team, we saw alignment in the way we approach client service, embrace innovation, and view the future of the family office business model,” said Pathstone CEO Matt Fleissig. “We’re thrilled to partner with such a culturally aligned group and to continue strengthening our presence in New England, in line with our goal of growing within our existing regional offices.”

Based in Englewood, N.J., the acquisition will bring Pathstone’s total client assets to almost $80 billion, with 17 office locations and nearly 350 team members­—more than 175 of whom are shareholders of the firm.

Merchant-backed Legacy Capital Recruits $650M Arkansas Team

Legacy Capital, a Little Rock, Ark.-based RIA and wealth management firm backed by Merchant Investment Management, is opening an office in Northwest Arkansas with the addition of Brian Wood, Michael Peebles and DeAnn Gann. The team of advisors were most recently with Arvest Bank’s wealth management division.

The deal will expand Legacy’s geographic footprint and strengthen its position as one of the largest independent wealth management firms in Arkansas, according to the announcement, including more than $1 billion in client assets and more than $2.5 billion of in-force life insurance.

The former Arvest Bank team will provide everything from asset management and investments to financial and estate planning, banking and trust services, and insurance solutions to high-net-worth and ultra-high-net-worth families.

Legacy has served individuals and families since 1977 with financial planning, asset management, legacy and estate planning, and insurance solutions. Backed by Merchant since 2018, Legacy has doubled AUM since a 2020 merger with Trent Capital and now serves 400 households with a staff of 20.

“Matt and the team at Legacy Capital were one of Merchant’s first partners,” said Merchant co-founder and Managing Partner Tim Bello. “It’s been remarkable working with them and growing the firm.”

Hightower Supports 1st Acquisition for Partner Firm Schultz Collins

Schultz Collins Investment Counsel, a Hightower firm in California’s San Francisco Bay area, completed its first acquisition with support from its parent platform.

DHR Investment Counsel in Oakland, Calif., a $385 million firm led by husband-and-wife team Davis Riemer and Louise Rothman-Riemer, is joining Schultz Collins and bringing the firm’s assets under supervision to more than $1.3 billion.

Founded in 1987, DHR Investment Counsel “pioneered the implementation of the fiduciary standard of practice among investment advisory firms,” according to the announcement, and is among the industry’s first fee-only firms.

Founded in 1995, Schultz Collins serves individual investors, retirement plan sponsors and institutions. The firm joined Hightower in January 2020.

“Together, Schultz Collins and DHR Investment Counsel serve a highly attractive, complementary clientele,” said Hightower Chairman and CEO Bob Oros. “This acquisition will go a long way in supporting the firm’s ambitious growth plans and helping empower their next generations of advisors.”

Hightower has a dedicated M&A team to help its partner firms execute mergers and sub-acquisitions by providing sourcing, valuation, deal structuring, due diligence, legal and regulatory and pre- and post-close integration services, as well as the capital resources needed for transactions.

The growing platform of independent advisors supports 131 firms in 34 states and the District of Columbia with a range of services designed to catalyze and accelerate growth. At the end of 2022, the firm managed $113.7 billion in client assets, up from $106.1 billion just three months earlier, and $144.3 billion in assets under administration.

Snowden Lane Partners Adds Morgan Stanley Advisor in Miami

Eduardo Alvarez Andreu, a Miami-based advisor managing $132 million in client assets, left Morgan Stanley to join Snowden Lane Partners, a hybrid RIA based in New York.

Working out of Snowden Lane’s Coral Gables, Fla. office, Alvarez Andreu will serve as partner and managing director. He brings nearly two decades of experience in financial services, with expertise in international wealth management and alternative investments.

Prior to Snowden Lane, Alvarez Andreu held the roles of first vice president, international client advisor, alternative investments director and portfolio manager at Morgan Stanley in Miami. He joined the wirehouse as a team research analyst and fixed income trader in 2010.

He has also worked as senior sales associate and trading specialist at Barclays and as a private wealth management certified sales assistant at Lehman Brothers. He’s fluent in English, Spanish and Portuguese.

“It’s always humbling to receive interest from advisors as qualified as Eduardo,” said Snowden Managing Director Doug Flaherty. “His experience working with clients both domestically and internationally will be invaluable, and his attention to detail for each of his clients is a true differentiator.”

Since its founding in 2011, Snowden Lane has grown rapidly by recruiting advisors from Morgan Stanley, Merrill Lynch, UBS, JP Morgan, Raymond James, Wells Fargo and Fieldpoint Private, among others.

Today, the firm employs 136 professionals, 75 of whom are client-facing advisors, across 13 offices around the country.

Machine Learning In Asset Pricing Explained

Machine Learning In Asset Pricing Explained

It’s really important to explore the potential of machine learning in asset pricing. In the fast-paced world of finance, accurate and timely asset pricing is essential for making informed investment decisions. Traditional asset pricing models have been widely used for many years, but they have several limitations, including the assumption of linearity and the reliance on simplifying assumptions that may not hold in the real world. In recent years, machine learning has emerged as a promising tool for improving asset pricing models in finance.

This cutting-edge technology allows financial analysts to develop more accurate and robust models that take into account a wider range of factors, including macroeconomic data, company fundamentals, and even news sentiment.

As machine learning algorithms continue to evolve, financial institutions will be able to develop even more accurate and sophisticated asset pricing models, giving them a competitive edge in the market. In this article, we have explored the background of asset pricing, the benefits and challenges of using machine learning in asset pricing, and some examples of how machine learning is being used in asset pricing today.

What is asset pricing?

Asset pricing refers to the process of determining the theoretical value or price of an asset, such as stocks, bonds, or real estate. It involves evaluating a range of factors that can influence an asset’s worth, including market conditions, economic trends, company performance, and more. Investors and financial analysts use asset pricing models to estimate the fair value of an asset, which helps them make informed decisions about buying, selling, or holding investments.

Understanding the background of asset pricing

Asset pricing is a fundamental concept in finance that involves determining the value of assets, such as stocks, bonds, and real estate. Traditional asset pricing models, such as the Capital Asset Pricing Model (CAPM), have been widely used in the finance industry for many years.

However, these models have several limitations, including the assumption of linearity and the reliance on simplifying assumptions. As a result, financial analysts have turned to machine learning as a promising tool for improving asset pricing models.

Machine learning algorithms can handle complex data structures, analyze vast amounts of data to identify patterns and relationships, and develop more accurate and robust asset pricing models that take into account a wider range of factors, such as macroeconomic data, company fundamentals, and even news sentiment.

Overall, understanding the background of asset pricing is crucial for anyone interested in investing or working in finance, as it provides the foundation for developing accurate and robust asset pricing models.

Machine learning in asset pricing explained
Machine learning in asset pricing is transforming the way financial analysts analyze data


  • Machine learning in asset pricing is a powerful tool that allows financial analysts to develop more accurate and robust asset pricing models.
  • By leveraging machine learning algorithms, financial institutions can analyze large amounts of financial data and identify patterns and relationships that traditional asset pricing models might miss.
  • Machine learning in asset pricing has many advantages, including improved accuracy, increased speed, better risk management, and the ability to handle complex data.

Traditional asset pricing models

For many years, traditional asset pricing models have been used in the finance industry to estimate the value of assets. The most common model is the Capital Asset Pricing Model (CAPM), which uses a linear regression of an asset’s returns against the returns of the market as a whole, as well as the risk-free rate of return, to estimate the asset’s expected return. Other traditional models include the Arbitrage Pricing Theory (APT) and the Fama-French Three Factor Model.

Limitations of traditional models

While traditional asset pricing models have been widely used, they have several limitations. One of the biggest issues with these models is their assumption of linearity, which can be problematic in situations where the relationship between an asset’s returns and market returns is nonlinear. Additionally, traditional models often rely on simplifying assumptions, such as normality of returns, that may not hold in the real world. These limitations can lead to inaccurate asset valuations and investment decisions.


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Emergence of machine learning in finance

In recent years, machine learning has emerged as a promising tool for improving asset pricing models. Machine learning algorithms can handle nonlinear relationships and complex data structures, making them well-suited for analyzing large, complex financial datasets. By using machine learning, financial analysts can develop more accurate and robust asset pricing models that take into account a wider range of factors, including macroeconomic data, company fundamentals, and even news sentiment. As a result, machine learning is quickly becoming an essential tool for investors and financial institutions seeking to gain a competitive edge in the market.

Benefits of using machine learning in asset pricing

Machine learning has emerged as a powerful tool for improving asset pricing models in finance. By using machine learning algorithms, financial analysts can develop more accurate and robust models that take into account a wider range of factors, including macroeconomic data, company fundamentals, and even news sentiment. Some of the benefits of using machine learning in asset pricing are explained below.

Improved accuracy

Machine learning algorithms can analyze vast amounts of data to identify patterns and relationships that traditional asset pricing models might miss. This can lead to more accurate valuations of assets, which in turn can help investors make better-informed decisions about buying, selling, or holding investments.

Increased speed

Machine learning algorithms can process and analyze large datasets in a matter of seconds, significantly reducing the time and effort required for financial analysts to develop asset pricing models. This increased speed can help financial institutions stay ahead of the competition and make more timely investment decisions.

Machine learning in asset pricing explained
Machine learning in asset pricing is an innovative approach that uses advanced algorithms to develop more accurate and robust asset pricing models

Better risk management

Machine learning can help financial institutions better manage risk by identifying potential risks and predicting market trends. By analyzing large datasets and identifying patterns, machine learning algorithms can help financial analysts develop more accurate risk models, which in turn can help institutions make better-informed decisions about risk management.

  • Ability to handle complex data: Machine learning algorithms can handle complex data structures, such as unstructured text data, which traditional asset pricing models cannot. This allows financial analysts to incorporate a wider range of data sources into their models, including news sentiment, social media data, and other unstructured data sources.
  • Cost savings: By using machine learning algorithms, financial institutions can significantly reduce the costs associated with asset pricing. Machine learning algorithms can automate many of the processes involved in asset pricing, reducing the need for manual labor and saving financial institutions time and money.

How machine learning is ssed in asset pricing?

Machine learning algorithms are used in asset pricing to analyze large amounts of financial data, identify patterns and relationships, and develop more accurate and robust asset pricing models. Financial analysts use machine learning algorithms to analyze a range of data sources, including macroeconomic data, company fundamentals, news sentiment, and social media data, to develop models that can accurately value assets.

Types of machine learning algorithms used in asset pricing

  • Supervised learning: Supervised learning algorithms are used in asset pricing to predict the value of assets based on historical data. These algorithms use labeled data to learn patterns and relationships between variables and then use that learning to make predictions about future asset values.
  • Unsupervised learning: Unsupervised learning algorithms are used in asset pricing to analyze large, complex datasets and identify patterns and relationships that might be difficult for human analysts to identify. These algorithms do not rely on labeled data and can uncover previously unknown patterns in data.
  • Reinforcement learning: Reinforcement learning algorithms are used in asset pricing to optimize investment strategies by learning from historical data and adjusting investment decisions accordingly. These algorithms can identify optimal investment strategies based on past performance and market conditions.

Advantages of using machine learning in asset pricing

  • Improved accuracy: Machine learning algorithms can identify patterns and relationships in large, complex datasets that traditional asset pricing models might miss. This leads to more accurate valuations of assets and better-informed investment decisions.
  • Increased speed: Machine learning algorithms can process large amounts of data in seconds, significantly reducing the time and effort required for financial analysts to develop asset pricing models.
  • Better risk management: Machine learning algorithms can identify potential risks and predict market trends, helping financial institutions better manage risk and make more informed investment decisions.
  • Ability to handle complex data: Machine learning algorithms can handle complex data structures, such as unstructured text data, allowing financial analysts to incorporate a wider range of data sources into their models.
Machine learning in asset pricing explained
The application of machine learning in asset pricing is becoming increasingly popular in the finance industry, as it allows for more accurate valuations and informed investment decisions

Challenges in implementing machine learning in asset pricing

  • Data quality: Machine learning algorithms rely on high-quality data to make accurate predictions. Poor data quality can lead to inaccurate models and investment decisions.
  • Interpretability: Machine learning algorithms can be difficult to interpret, making it challenging for financial analysts to understand how the model arrived at its predictions.
  • Implementation costs: Implementing machine learning algorithms can be expensive, requiring significant investments in hardware, software, and personnel.
  • Ethical concerns: There are ethical concerns surrounding the use of machine learning in asset pricing, including the potential for bias and discrimination in the model’s predictions.

So, the use of machine learning in asset pricing has many advantages, including improved accuracy, increased speed, better risk management, and the ability to handle complex data. However, there are also significant challenges to implementing machine learning in finance, including data quality, interpretability, implementation costs, and ethical concerns.

Examples of machine learning in asset pricing

Machine learning algorithms are being used in various ways to improve asset pricing models in finance. Here are some examples of how machine learning is being used in asset pricing:

Predicting stock prices using machine learning algorithms

Machine learning algorithms are being used to predict the future prices of stocks based on historical price data and other factors. Some real-life examples of this include:

  • Google’s DeepMind has developed a machine learning algorithm that can predict the price movements of a stock up to one day in advance with 86{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} accuracy.
  • BlackRock, the world’s largest asset manager, is using machine learning algorithms to analyze financial data and make investment decisions.
  • Bridgewater Associates, one of the world’s largest hedge funds, uses machine learning algorithms to analyze large datasets and identify market trends.

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Portfolio optimization using machine learning

Machine learning algorithms are being used to optimize investment portfolios by identifying the optimal allocation of assets based on historical data and market conditions. Some real-life examples of this include:

  • JPMorgan Chase uses machine learning algorithms to optimize its investment portfolios, resulting in a 15{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} improvement in investment returns.
  • Goldman Sachs uses machine learning algorithms to analyze large datasets and identify market trends to optimize its investment portfolios

Credit risk assessment using machine learning

Machine learning algorithms are being used to assess credit risk by analyzing large amounts of data and identifying patterns that can predict creditworthiness. Some real-life examples of this include:

  • LendingClub uses machine learning algorithms to assess credit risk and make lending decisions, resulting in a 40{21df340e03e388cc75c411746d1a214f72c176b221768b7ada42b4d751988996} improvement in loan approval rates.
  • ZestFinance uses machine learning algorithms to assess credit risk for lenders, resulting in more accurate and fair lending decisions.
Machine learning in asset pricing explained
By leveraging machine learning in asset pricing, financial institutions can analyze large datasets and identify patterns and relationships that traditional asset pricing models might miss

What’s the future of machine learning in asset pricing?

The future of machine learning in asset pricing is promising. As machine learning algorithms continue to evolve, financial institutions will be able to develop even more accurate and robust asset pricing models. Some of the possibilities for the future of machine learning in asset pricing include:

  • Increased use of unstructured data: Machine learning algorithms will become more capable of handling unstructured data, such as news sentiment, social media data, and other data sources. This will allow financial analysts to incorporate a wider range of data sources into their asset pricing models, resulting in more accurate valuations.
  • Greater adoption of deep learning: Deep learning algorithms, which are capable of learning from unstructured data, will become more widely used in asset pricing. This will allow financial analysts to develop more accurate models that take into account a wider range of factors.
  • Increased use of reinforcement learning: Reinforcement learning algorithms will become more widely used in asset pricing to optimize investment strategies. Financial institutions will be able to use these algorithms to identify optimal investment strategies based on past performance and market conditions.
  • Improved interpretability: Machine learning algorithms will become more interpretable, allowing financial analysts to better understand how the model arrived at its predictions. This will increase trust in machine learning models and allow financial institutions to make more informed investment decisions.
  • Greater adoption of explainable AI: Explainable AI, which is designed to produce models that are transparent and easy to understand, will become more widely used in asset pricing. This will help financial institutions comply with regulations and improve trust in machine learning models.
Category Future possibilities
Handling Unstructured Data Incorporating news sentiment, social media data, and other unstructured data sources
Deep Learning More accurate models that take into account a wider range of factors
Reinforcement Learning Identifying optimal investment strategies based on past performance and market conditions
Improved Interpretability Increased understanding of how the model arrived at its predictions
Explainable AI Producing models that are transparent and easy to understand, improving trust in AI models

Final words

In conclusion, the use of machine learning in asset pricing is an exciting and rapidly evolving field in finance. By using machine learning algorithms, financial analysts can develop more accurate and robust models that take into account a wider range of factors, resulting in better-informed investment decisions and a competitive edge in the market. However, there are also significant challenges to implementing machine learning in finance, including data quality, interpretability, implementation costs, and ethical concerns. As machine learning continues to evolve, it is likely to become an even more essential tool for financial institutions seeking to stay ahead in the competitive financial landscape.

Key takeaways

  • There are various types of machine learning algorithms used in asset pricing, such as supervised learning, unsupervised learning, and reinforcement learning, each with its own strengths and limitations.
  • Some of the key applications of machine learning in asset pricing include predicting stock prices, optimizing investment portfolios, and assessing credit risk.
  • Despite the many advantages of machine learning in asset pricing, there are also significant challenges to implementing machine learning algorithms in finance, including data quality, interpretability, and ethical concerns.
  • The future of machine learning in asset pricing is promising, and financial institutions that invest in this technology are likely to gain a competitive edge in the market.