最新ニュース ESAB (NYSE:ESAB) 株価情報

ESAB

ESAB, spun off from Colfax in 2022, is a leading manufacturer of equipment and consumables used in welding, cutting, and joining applications. Alongside competitors Lincoln Electric and ITW's Miller brand, ESAB is one of the top three players in the welding space. ESAB generated roughly $2.7 billion in revenue in 2024.

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GOOGL

Watching Alphabet; Reuters Reports Court Comments Saying U.S. Judge Sets Remedies Trial In Google Ad Tech Antitrust Case For September 22; U.S. DOJ To Seek Divestiture Of Google Advertising Technology Assets; Google Says Structural Remedies Are Not Appropriate

Fartcoin (CRYPTO: FARTCOIN) is turning heads with a <a href="https://www.benzinga.com/money/fartcoin-price-prediction">10% rally over the past week</a>, leaving meme coin heavyweights Dogecoin (CRYPTO: <a class="ticker" href="https://www.benzinga.com/quote/doge/usd">DOGE</a>) and Shiba Inu (CRYPTO: <a class="ticker" href="https://www.benzinga.com/quote/shib/usd">SHIB</a>) trailing behind.
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<td>Cryptocurrency</td>
<td>Price&nbsp;&nbsp;&nbsp;</td>
<td>Market Cap</td>
<td>24-Hour Trend</td>
<td>7-Day Trend</td>
</tr>
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<td>Fartcoin</td>
<td>&nbsp;$1.21</td>
<td>$1.21 billion</td>
<td>-3.2%&nbsp;</td>
<td>+9.2%&nbsp;</td>
</tr>
<tr>
<td>Dogecoin </td>
<td>&nbsp;$0.1822</td>
<td>$27.2 billion</td>
<td>+0.8%&nbsp;</td>
<td>-1.5%&nbsp;</td>
</tr>
<tr>
<td>Shiba Inu </td>
<td>&nbsp;$0.00001358</td>
<td>$8 billion</td>
<td>+0.2%&nbsp;</td>
<td>-4%&nbsp;</td>
</tr>
</tbody>
</table>
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Trader Notes: The surge follows a wave of bearish sentiment from high-profile accounts, a setup that prominent trader Unipcs says mirrors conditions ahead of Fartcoin's last major rally.
Unipcs <a href="https://x.com/theunipcs/status/1918206886656217478">argues</a> that Fartcoin remains at the center of meme coin mindshare, especially with Bitcoin nearing new all-time highs. 
After enduring a brutal 92% correction, he believes a full collapse is unlikely, and that skeptics may be spreading fear to buy lower.
Trader Cold Blooded Shiller highlighted that $1.20 is acting as a sticky resistance, but signs of strength are returning on lower timeframes.
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<blockquote class="twitter-tweet" data-width="500" data-dnt="true">Starting to get that LTF strength through here on FARTCOIN. $1.20 being sticky. <a href="https://t.co/9sXadA9IK4">https://t.co/9sXadA9IK4</a> <a href="https://t.co/ylFxFHNcdy">pic.twitter.com/ylFxFHNcdy</a>
&mdash; Cold Blooded Shiller (@ColdBloodShill) <a href="https://twitter.com/ColdBloodShill/status/1918265238278824105?ref_src=twsrc%5Etfw">May 2, 2025</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script></div>
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Artchick <a href="https://x.com/digitalartchick/status/1918110570126934390">pointed</a> out that Fartcoin just posted its highest monthly close and has only closed higher on the weekly chart twice before. 
He argues the market is too focused on noise and that we could be witnessing the start of a historic, organic rally.
Kriesz, dubbing himself the "Chief Farting Officer," <a href="https://x.com/_Kriesz_/status/1917678542894293489">sees</a> bullish technicals forming, including an ascending triangle and cup-and-handle pattern. His target: $1.66–$1.67, citing measured moves from past setups and liquidity zones.
Statistics: &nbsp;Stalkchain <a href="https://x.com/StalkHQ/status/1918077979374305618">shows</a> Fartcoin was the most purchased token by smart money wallets in the last 24 hours, a bullish signal as momentum builds.
Read Next:&nbsp;
<ul>
<li><a href="https://www.benzinga.com/markets/cryptocurrency/25/05/45134223/dogecoin-millionaire-shares-his-top-5-plays-doge-shib-pepe-and-two-surprises">&#8216;Dogecoin Millionaire&#8217; Shares His Top 5 Plays: DOGE, SHIB, PEPE And Two Surprises</a></li>
</ul>
Image: Shutterstock

Coverage of Bitcoin, Etherum, Ripple and all the latest digital currencies.

Cryptocurrency

$DOGE

$SHIB

Fartcoin Soars 10% In A Week, Outshining Dogecoin, Shiba Inu

Piper Sandler analyst Thomas Champion reiterates Amazon.com (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/AMZN#NASDAQ">AMZN</a>) with a Overweight and lowers the price target from $215 to $212.

Price Target

Reiteration

Analyst Ratings

AMZN

Piper Sandler Reiterates Overweight on Amazon.com, Lowers Price Target to $212

Barrington Research analyst Gary Prestopino maintains Standex International (NYSE:<a class="ticker" href="https://www.benzinga.com/stock/SXI#NYSE">SXI</a>) with a Outperform and maintains $220 price target.

Barrington Research Maintains Outperform on Standex International, Maintains $220 Price Target

Piper Sandler analyst Matt O'Brien maintains DexCom (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/DXCM#NASDAQ">DXCM</a>) with a Overweight and lowers the price target from $100 to $90.

DXCM

Piper Sandler Maintains Overweight on DexCom, Lowers Price Target to $90

Keybanc analyst Jeffrey Hammond maintains Mastercard (NYSE:<a class="ticker" href="https://www.benzinga.com/stock/MA#NYSE">MA</a>) with a Overweight and raises the price target from $630 to $635.

Keybanc Maintains Overweight on Mastercard, Raises Price Target to $635

Celsius Holdings Inc (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/CELH#NASDAQ">CELH</a>) is scheduled to report its first-quarter results on Tuesday, May 6, <a href="https://www.benzinga.com/insights/short-sellers/25/04/44634247/how-is-the-market-feeling-about-celsius-holdings">before market open</a>.
According to JPMorgan, material negative earnings revisions seem to be in the company&#8217;s rearview.
The Celsius Holdings Thesis: <a href="https://www.benzinga.com/analyst-stock-ratings/analyst/5e5520feece4e60001201237/andrea-teixeira">Analyst Andrea Teixeira</a> maintained an Overweight rating, while raising the price target from $39 to $44.
The Celsius Holdings Thesis: The company is likely to report sales and earnings at $338 million and 19 cents per share, short of consensus of $343 million and 20 cents per share, Teixeira said in the note.
Check out <a href="https://www.benzinga.com/analyst-stock-ratings">other analyst stock ratings</a>.
Celsius Holdings' sales trends are recovering, "inflecting back to positive growth in some recent periods," she added.
The company is witnessing energy category acceleration, while most staples categories are seeing softening consumption trends, the analyst stated.
The acquisition of Alani Nu, which closed on April 1, 2025, is expected to become accretive from the second quarter, Teixeira said. "With that, our 2025E revenue/EPS/EBITDA move to $2.2B (+64% YOY), $0.95 (+37.7% YOY), and $453M (+77.2% YOY) and our 2026E revenue/EPS/EBITDA move to $2.8B (+27.7% YOY), $1.29 (+35.7% YOY), and $600M (+32.5% YOY)," she further wrote.
CELH Price Action: Shares of Celsius Holdings had risen by 1.72% to $35.47 at the time of publication on Friday.
Read More: 
<ul>
<li><a href="https://in.benzinga.com/content/44890152/j-b-hunt-transport-overcorrects-into-a-buying-opportunity">Short Sellers Gave Up on These 3 Names Recently</a></li>
</ul>
Image: Shutterstock

Analyst Color

Previews

Movers

CELH

Celsius Holdings Q1 Preview: Inflecting To Growth, Negative Earnings Revisions Unlikely, Says Analyst

Piper Sandler analyst Matt O'Brien reiterates Alphatec Holdings (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/ATEC#NASDAQ">ATEC</a>) with a Overweight and raises the price target from $13 to $15.

ATEC

Piper Sandler Reiterates Overweight on Alphatec Holdings, Raises Price Target to $15

Macquarie analyst Steve Koenig maintains Atlassian (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/TEAM#NASDAQ">TEAM</a>) with a Outperform and lowers the price target from $270 to $250.

TEAM

Macquarie Maintains Outperform on Atlassian, Lowers Price Target to $250

<a href="https://www.whitehouse.gov/wp-content/uploads/2025/05/Fiscal-Year-2026-Discretionary-Budget-Request.pdf" title="https://www.whitehouse.gov/wp-content/uploads/2025/05/Fiscal-Year-2026-Discretionary-Budget-Request.pdf">https://www.whitehouse.gov/wp-content/uploads/2025/05/Fiscal-Year-2026-Discretionary-Budget-Request.pdf</a>

Macro Economic Events

Politics

Economics

General

White House Budget Proposal Would Cut Non-Defense FY26 Spending By $163B; Would Cancel Over $15B In Infrastructure Investment And Jobs Act Funds For Department Of Energy;  Would Cancel $5.7B In Funds For Electric Vehicle Charger Grant Programs; Would Allot Over $7B To NASA For Lunar Exploration, $1B For Investments On Mars-Focused Programs; Would Expand U.S. International Development Finance Corporation, Including New $3B Revolving Fund

Piper Sandler analyst Edward Tenthoff reiterates Arvinas (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/ARVN#NASDAQ">ARVN</a>) with a Overweight and lowers the price target from $24 to $14.

ARVN

Piper Sandler Reiterates Overweight on Arvinas, Lowers Price Target to $14

Piper Sandler analyst Nathan Race reiterates UMB Financial (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/UMBF#NASDAQ">UMBF</a>) with a Overweight and raises the price target from $132 to $137.

UMBF

Piper Sandler Reiterates Overweight on UMB Financial, Raises Price Target to $137

Macquarie analyst Chad Beynon maintains Red Rock Resorts (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/RRR#NASDAQ">RRR</a>) with a Outperform and maintains $58 price target.

Macquarie Maintains Outperform on Red Rock Resorts, Maintains $58 Price Target

Piper Sandler analyst John Barnidge maintains Prudential Financial (NYSE:<a class="ticker" href="https://www.benzinga.com/stock/PRU#NYSE">PRU</a>) with a Neutral and lowers the price target from $110 to $109.

Piper Sandler Maintains Neutral on Prudential Financial, Lowers Price Target to $109

The Hartford Insurance Gr (NYSE:<a class="ticker" href="https://www.benzinga.com/stock/HIG#NYSE">HIG</a>) has outperformed the market over the past 5 years by 14.16% on an annualized basis producing an average annual return of 28.76%. Currently, The Hartford Insurance Gr has a market capitalization of $34.92 billion. 
Buying $100 In HIG: If an investor had bought $100 of HIG stock 5 years ago, it would be worth $351.65 today based on a price of $122.93 for HIG at the time of writing.
<h3>The Hartford Insurance Gr's Performance Over Last 5 Years</h3>
<img src="https://www.benzinga.com/files/images/story/2025/1746198941_0.png" alt="comp_fig" />
Finally -- what's the point of all this? The key insight to take from this article is to note how much of a difference compounded returns can make in your cash growth over a period of time.
This article was generated by Benzinga's automated content engine and reviewed by an editor.

If You Invested $100 In This Stock 5 Years Ago, You Would Have This Much Today

WIIM

PDEX

Pro-Dex shares are trading lower after the company reported worse-than-expected Q3 sales results.

Macquarie analyst Chad Beynon maintains Rush Street Interactive (NYSE:<a class="ticker" href="https://www.benzinga.com/stock/RSI#NYSE">RSI</a>) with a Outperform and maintains $16 price target.

Macquarie Maintains Outperform on Rush Street Interactive, Maintains $16 Price Target

Keybanc analyst Eric Sheridan maintains LXP Industrial Tr (NYSE:<a class="ticker" href="https://www.benzinga.com/stock/LXP#NYSE">LXP</a>) with a Overweight and lowers the price target from $11 to $10.

Keybanc Maintains Overweight on LXP Industrial Tr, Lowers Price Target to $10

Microalgo Inc. (The &quot;Company&quot; or &quot;Microalgo&quot;) (NASDAQ:<a class="ticker" href="https://www.benzinga.com/stock/MLGO#NASDAQ">MLGO</a>) Announced Today the Launch of Their Latest Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms (Vqa). This Technology Significantly Reduces the Complexity of Parameter Updates During Training Through Deep Optimization of the Core Circuit, Markedly Improving Computational Efficiency. Compared to Other Quantum Classifiers, This Optimized Model Has Lower Complexity and Incorporates Advanced Regularization Techniques, Effectively Preventing Model Overfitting and Enhancing the Classifier&#39;s Generalization Capability. the Introduction of This Technology Marks a Significant Step Forward in the Practical Application of Quantum Machine Learning.
Traditional quantum classifiers can theoretically leverage the advantages of quantum computing to accelerate machine learning tasks, but they still face numerous challenges in practical applications. Firstly, current mainstream quantum classifiers often require deep quantum circuits to achieve efficient feature mapping, which results in high optimization complexity for quantum parameters during training. Additionally, as the volume of training data increases, the computational load for parameter updates grows rapidly, leading to prolonged training times and impacting the model&#39;s practicality.
MicroAlgo&#39;s classifier auto-optimization technology significantly reduces computational complexity through deep optimization of the core circuit. This approach improves upon two key aspects: circuit design and optimization algorithms. In terms of circuit design, the technology adopts a streamlined quantum circuit structure, reducing the number of quantum gates and thereby lowering the consumption of computational resources. On the optimization algorithm front, this classifier auto-optimization model employs an innovative parameter update strategy, making parameter adjustments more efficient and substantially accelerating training speed.
In the training process of classifiers based on variational quantum algorithms (VQA), parameter optimization is one of the most critical steps. Generally, VQA classifiers rely on Parameterized Quantum Circuits (PQC), where updating each parameter requires computing gradients to adjust the circuit structure and minimize the loss function. However, the deeper the quantum circuit, the more complex the parameter space becomes, requiring optimization algorithms to perform more iterations to achieve convergence. Furthermore, uncertainties and noise in quantum measurements can also affect the training process, making it difficult for the model to optimize stably.
Traditional optimization methods often employ strategies such as Stochastic Gradient Descent (SGD) or Variational Quantum Natural Gradient (VQNG) to find optimal parameters. However, these methods still face challenges such as high computational complexity, slow convergence rates, and a tendency to get trapped in local optima. Therefore, reducing the computational burden of parameter updates and improving training stability have become key factors in enhancing the performance of VQA classifiers.
MicroAlgo&#39;s classifier auto-optimization technology, based on variational quantum algorithms, significantly reduces the computational complexity of parameter updates through deep optimization of the core circuit. It also incorporates innovative regularization techniques to enhance the stability and generalization capability of the training process. The core breakthroughs of this technology include the following aspects:
Depth Optimization of Quantum Circuits to Reduce Computational Complexity: In traditional VQA classifier designs, the number of layers in the quantum circuit directly impacts computational complexity. To lower computational costs, MicroAlgo employs an Adaptive Circuit Pruning (ACP) method during optimization. This approach dynamically adjusts the circuit structure, eliminating redundant parameters while preserving the classifier&#39;s expressive power. As a result, the number of parameters required during training is significantly reduced, leading to a substantial decrease in computational complexity.
Hamiltonian Transformation Optimization (HTO): Additionally, MicroAlgo introduces an optimization method based on Hamiltonian transformations. By altering the Hamiltonian representation of the variational quantum circuit, this technique shortens the search path within the parameter space, thereby improving optimization efficiency. Experimental results demonstrate that this method can reduce computational complexity by at least an order of magnitude while maintaining classification accuracy.
Novel Regularization Strategy to Enhance Training Stability and Generalization Capability: In classical machine learning, regularization methods are widely used to prevent model overfitting. In the realm of quantum machine learning, MicroAlgo introduces a novel quantum regularization strategy called Quantum Entanglement Regularization (QER). This method dynamically adjusts the strength of quantum entanglement during training, preventing the model from overfitting the training data and thereby improving the classifier&#39;s generalization ability on unseen data.
Additionally, an optimization strategy based on the Energy Landscape is incorporated, which adjusts the shape of the loss function during training. This enables the optimization algorithm to more quickly identify the global optimum, reducing the impact of local optima.

MLGO

MicroAlgo Launches Their Latest Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms

<a href="https://www.bloomberg.com/news/articles/2025-05-02/trump-administration-proposes-cutting-6-billion-from-nasa-s-budget?embedded-checkout=true">https://www.bloomberg.com/news/articles/2025-05-02/trump-administration-proposes-cutting-6-billion-from-nasa-s-budget?embedded-checkout=true</a>

BlackSky Technology Inc. Class A Common Stock

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