Latest news with #MicroAlgo
Business Upturn
09-06-2025
- Business
- Business Upturn
MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System
By GlobeNewswire Published on June 9, 2025, 18:30 IST shenzhen, June 09, 2025 (GLOBE NEWSWIRE) — MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System Shenzhen, Jun. 09, 2025––MicroAlgo Inc. (the 'Company' or 'MicroAlgo') (NASDAQ: MLGO), today announced that by integrating the quantum image LSQb algorithm with quantum encryption technology, they have proposed a brand-new information hiding and transmission scheme, aiming to build a more secure and efficient data protection LSQb algorithm, namely the Least Significant Quantum Bit algorithm for quantum images, is an innovative quantum image processing technology. It achieves secure information hiding by embedding secret information into the least significant quantum bits of a quantum image. Building on this foundation, MicroAlgo further integrates relevant theories from quantum information theory and cryptography, comprehensively expanding the application scope and functionality of the LSQb algorithm. This integration not only enhances the security of information hiding but also improves the efficiency and reliability of information transmission in quantum core of MicroAlgo's technological innovation lies in utilizing the Least Significant Quantum Bit (LSQb) algorithm for efficient information encoding and decoding, combined with quantum encryption technologies such as Quantum Key Distribution (QKD) to ensure data security during transmission. The LSQb algorithm can identify and select key quantum bits critical to image representation, reducing the number of quantum gate operations by optimizing the embedding and extraction processes, thereby lowering algorithm complexity. Meanwhile, quantum encryption technology provides unconditional security for information transmission, ensuring that information leakage is prevented even in a quantum computing Image Preprocessing: First, the original image undergoes compressed sensing and sparse representation to extract key features and convert them into quantum bit form. Further analysis is conducted using machine learning or deep learning models to ensure the retention of important visual elements of the image, reduce the amount of encoded information, and lower algorithm Bit Selection and Embedding: An improved Least Significant Quantum Bit (LSQb) algorithm is employed to embed selected key quantum bits into quantum states. Each quantum bit generates a corresponding quantum state and is embedded into a larger quantum state structure through quantum gate operations. Quantum error correction codes and quantum entanglement properties are introduced to enhance the system's robustness and stability, reducing unnecessary quantum gate Key Distribution and Encryption: Quantum Key Distribution (QKD) technology is utilized to generate a shared key, ensuring the security of data transmission. The sender and receiver exchange correlated quantum states to generate the key, and any attempt to read the states will alter them and be detected, preventing information Transmission and Protection: The encrypted quantum state information is transmitted through a quantum channel, and even if eavesdropping occurs, attackers cannot obtain useful information. By combining protocols such as quantum teleportation, the system's security and flexibility are further Decryption and Recovery: The receiver uses the shared key to decrypt the quantum state information and applies inverse quantum gate operations to restore the original quantum bit sequence. Key feature information is extracted through a decoding algorithm and reassembled into a complete image, with error correction mechanisms introduced to ensure high-fidelity recovery. The entire process validates the effectiveness and accuracy of information hiding and transmission, establishing an efficient and secure quantum information processing integrates the Least Significant Quantum Bit (LSQb) algorithm for quantum images with other related theories, such as quantum information theory and cryptography, to further expand its application scope and functionality. Combined with quantum encryption technology, it constructs a more secure quantum information hiding and transmission system, ensuring the secure transmission of information in quantum networks. On one hand, it significantly reduces the demand for quantum resources, minimizing the involvement of unnecessary quantum bits and the number of quantum gate operations, thereby increasing the algorithm's execution speed. On the other hand, leveraging the unconditional security provided by quantum encryption technology ensures a high level of confidentiality for data during transmission. This not only enhances the efficiency of information processing but also greatly improves the system's resilience to interference, maintaining high information fidelity even in noisy environments. Additionally, by simplifying quantum circuit design, it reduces the cost and technical complexity of hardware implementation, making large-scale commercial applications practical applications, MicroAlgo's novel information hiding and transmission system has already been applied in multiple fields. For example, in medical image encryption, patient privacy data receives a higher level of protection; in financial transaction systems, customers' sensitive financial information is similarly safeguarded effectively. Through this approach, not only is information security enhanced, but processing efficiency is also improved, meeting the modern society's demand for high-speed and efficient data processing. In the future, with continuous advancements in quantum computing and quantum encryption technologies, MicroAlgo's novel information hiding and transmission system is expected to expand beyond its current application scenarios to more emerging fields, such as artificial intelligence and big data analysis. For instance, in the field of artificial intelligence, leveraging the advantages of quantum computing can accelerate the training process of machine learning models; in big data analysis, quantum image processing technology can help extract valuable information from massive datasets more quickly. Through ongoing exploration and practice, quantum image processing technology will become more mature and refined, contributing to the construction of a more secure and efficient information society. About MicroAlgo Inc. MicroAlgo Inc. (the 'MicroAlgo'), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development. Forward-Looking Statements This press release contains statements that may constitute 'forward-looking statements.' Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, Words such as 'expect,' 'estimate,' 'project,' 'budget,' 'forecast,' 'anticipate,' 'intend,' 'plan,' 'may,' 'will,' 'could,' 'should,' 'believes,' 'predicts,' 'potential,' 'continue,' and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction. MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law. Contact MicroAlgo Inc. Investor Relations Email: [email protected] Disclaimer: The above press release comes to you under an arrangement with GlobeNewswire. Business Upturn takes no editorial responsibility for the same. GlobeNewswire provides press release distribution services globally, with substantial operations in North America and Europe.
Yahoo
09-06-2025
- Business
- Yahoo
MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System
shenzhen, June 09, 2025 (GLOBE NEWSWIRE) -- MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System Shenzhen, Jun. 09, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that by integrating the quantum image LSQb algorithm with quantum encryption technology, they have proposed a brand-new information hiding and transmission scheme, aiming to build a more secure and efficient data protection LSQb algorithm, namely the Least Significant Quantum Bit algorithm for quantum images, is an innovative quantum image processing technology. It achieves secure information hiding by embedding secret information into the least significant quantum bits of a quantum image. Building on this foundation, MicroAlgo further integrates relevant theories from quantum information theory and cryptography, comprehensively expanding the application scope and functionality of the LSQb algorithm. This integration not only enhances the security of information hiding but also improves the efficiency and reliability of information transmission in quantum core of MicroAlgo's technological innovation lies in utilizing the Least Significant Quantum Bit (LSQb) algorithm for efficient information encoding and decoding, combined with quantum encryption technologies such as Quantum Key Distribution (QKD) to ensure data security during transmission. The LSQb algorithm can identify and select key quantum bits critical to image representation, reducing the number of quantum gate operations by optimizing the embedding and extraction processes, thereby lowering algorithm complexity. Meanwhile, quantum encryption technology provides unconditional security for information transmission, ensuring that information leakage is prevented even in a quantum computing Image Preprocessing: First, the original image undergoes compressed sensing and sparse representation to extract key features and convert them into quantum bit form. Further analysis is conducted using machine learning or deep learning models to ensure the retention of important visual elements of the image, reduce the amount of encoded information, and lower algorithm Bit Selection and Embedding: An improved Least Significant Quantum Bit (LSQb) algorithm is employed to embed selected key quantum bits into quantum states. Each quantum bit generates a corresponding quantum state and is embedded into a larger quantum state structure through quantum gate operations. Quantum error correction codes and quantum entanglement properties are introduced to enhance the system's robustness and stability, reducing unnecessary quantum gate Key Distribution and Encryption: Quantum Key Distribution (QKD) technology is utilized to generate a shared key, ensuring the security of data transmission. The sender and receiver exchange correlated quantum states to generate the key, and any attempt to read the states will alter them and be detected, preventing information Transmission and Protection: The encrypted quantum state information is transmitted through a quantum channel, and even if eavesdropping occurs, attackers cannot obtain useful information. By combining protocols such as quantum teleportation, the system's security and flexibility are further Decryption and Recovery: The receiver uses the shared key to decrypt the quantum state information and applies inverse quantum gate operations to restore the original quantum bit sequence. Key feature information is extracted through a decoding algorithm and reassembled into a complete image, with error correction mechanisms introduced to ensure high-fidelity recovery. The entire process validates the effectiveness and accuracy of information hiding and transmission, establishing an efficient and secure quantum information processing integrates the Least Significant Quantum Bit (LSQb) algorithm for quantum images with other related theories, such as quantum information theory and cryptography, to further expand its application scope and functionality. Combined with quantum encryption technology, it constructs a more secure quantum information hiding and transmission system, ensuring the secure transmission of information in quantum networks. On one hand, it significantly reduces the demand for quantum resources, minimizing the involvement of unnecessary quantum bits and the number of quantum gate operations, thereby increasing the algorithm's execution speed. On the other hand, leveraging the unconditional security provided by quantum encryption technology ensures a high level of confidentiality for data during transmission. This not only enhances the efficiency of information processing but also greatly improves the system's resilience to interference, maintaining high information fidelity even in noisy environments. Additionally, by simplifying quantum circuit design, it reduces the cost and technical complexity of hardware implementation, making large-scale commercial applications practical applications, MicroAlgo's novel information hiding and transmission system has already been applied in multiple fields. For example, in medical image encryption, patient privacy data receives a higher level of protection; in financial transaction systems, customers' sensitive financial information is similarly safeguarded effectively. Through this approach, not only is information security enhanced, but processing efficiency is also improved, meeting the modern society's demand for high-speed and efficient data the future, with continuous advancements in quantum computing and quantum encryption technologies, MicroAlgo's novel information hiding and transmission system is expected to expand beyond its current application scenarios to more emerging fields, such as artificial intelligence and big data analysis. For instance, in the field of artificial intelligence, leveraging the advantages of quantum computing can accelerate the training process of machine learning models; in big data analysis, quantum image processing technology can help extract valuable information from massive datasets more quickly. Through ongoing exploration and practice, quantum image processing technology will become more mature and refined, contributing to the construction of a more secure and efficient information society. About MicroAlgo Inc. MicroAlgo Inc. (the 'MicroAlgo'), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development. Forward-Looking Statements This press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, Words such as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend," "plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue," and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction. MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law. Contact MicroAlgo Inc. Investor Relations Email: ir@
Yahoo
08-05-2025
- Business
- Yahoo
We Don't Think MicroAlgo's (NASDAQ:MLGO) Earnings Should Make Shareholders Too Comfortable
Investors appear disappointed with MicroAlgo Inc.'s (NASDAQ:MLGO) recent earnings, despite the decent statutory profit number. We did some digging and found some worrying factors that they might be paying attention to. Our free stock report includes 4 warning signs investors should be aware of before investing in MicroAlgo. Read for free now. As finance nerds would already know, the accrual ratio from cashflow is a key measure for assessing how well a company's free cash flow (FCF) matches its profit. In plain english, this ratio subtracts FCF from net profit, and divides that number by the company's average operating assets over that period. You could think of the accrual ratio from cashflow as the 'non-FCF profit ratio'. Therefore, it's actually considered a good thing when a company has a negative accrual ratio, but a bad thing if its accrual ratio is positive. While it's not a problem to have a positive accrual ratio, indicating a certain level of non-cash profits, a high accrual ratio is arguably a bad thing, because it indicates paper profits are not matched by cash flow. To quote a 2014 paper by Lewellen and Resutek, "firms with higher accruals tend to be less profitable in the future". For the year to December 2024, MicroAlgo had an accrual ratio of 0.45. That means it didn't generate anywhere near enough free cash flow to match its profit. Statistically speaking, that's a real negative for future earnings. In fact, it had free cash flow of CN¥29m in the last year, which was a lot less than its statutory profit of CN¥38.6m. Notably, MicroAlgo had negative free cash flow last year, so the CN¥29m it produced this year was a welcome improvement. Unfortunately for shareholders, the company has also been issuing new shares, diluting their share of future earnings. The good news for shareholders is that MicroAlgo's accrual ratio was much better last year, so this year's poor reading might simply be a case of a short term mismatch between profit and FCF. As a result, some shareholders may be looking for stronger cash conversion in the current year. Note: we always recommend investors check balance sheet strength. Click here to be taken to our balance sheet analysis of MicroAlgo. To understand the value of a company's earnings growth, it is imperative to consider any dilution of shareholders' interests. MicroAlgo expanded the number of shares on issue by 5,977% over the last year. That means its earnings are split among a greater number of shares. To talk about net income, without noticing earnings per share, is to be distracted by the big numbers while ignoring the smaller numbers that talk to per share value. You can see a chart of MicroAlgo's EPS by clicking here. Unfortunately, we don't have any visibility into its profits three years back, because we lack the data. Zooming in to the last year, we still can't talk about growth rates coherently, since it made a loss last year. But mathematics aside, it is always good to see when a formerly unprofitable business come good (though we accept profit would have been higher if dilution had not been required). And so, you can see quite clearly that dilution is having a rather significant impact on shareholders. In the long term, if MicroAlgo's earnings per share can increase, then the share price should too. But on the other hand, we'd be far less excited to learn profit (but not EPS) was improving. For the ordinary retail shareholder, EPS is a great measure to check your hypothetical "share" of the company's profit. As it turns out, MicroAlgo couldn't match its profit with cashflow and its dilution means that shareholders own less of the company than the did before (unless they bought more shares). For all the reasons mentioned above, we think that, at a glance, MicroAlgo's statutory profits could be considered to be low quality, because they are likely to give investors an overly positive impression of the company. If you'd like to know more about MicroAlgo as a business, it's important to be aware of any risks it's facing. Case in point: We've spotted 4 warning signs for MicroAlgo you should be mindful of and 3 of these shouldn't be ignored. Our examination of MicroAlgo has focussed on certain factors that can make its earnings look better than they are. And, on that basis, we are somewhat skeptical. But there is always more to discover if you are capable of focussing your mind on minutiae. For example, many people consider a high return on equity as an indication of favorable business economics, while others like to 'follow the money' and search out stocks that insiders are buying. So you may wish to see this free collection of companies boasting high return on equity, or this list of stocks with high insider ownership. Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Associated Press
02-05-2025
- Business
- Associated Press
MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum Machine Learning
SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the 'Company' or 'MicroAlgo') (NASDAQ: MLGO) 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'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's practicality. MicroAlgo'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'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'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'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. Enhanced Noise Robustness for Real Quantum Computing Environments: Given that current Noisy Intermediate-Scale Quantum (NISQ) devices still exhibit significant noise levels, a model's noise resilience is critical. To improve the classifier's robustness, MicroAlgo proposes a technique based on Variational Quantum Error Correction (VQEC). This method actively learns noise patterns during training and adjusts circuit parameters to mitigate noise effects. This strategy markedly enhances the classifier's stability in noisy environments, making its performance on real quantum devices more reliable. MicroAlgo's classifier auto-optimization technology, based on variational quantum algorithms, successfully reduces the computational complexity of parameter updates through deep optimization of the core circuit and the introduction of novel regularization methods. This approach significantly boosts training speed and generalization capability. This breakthrough technology not only demonstrates its effectiveness in theory but also exhibits superior performance in simulation experiments, laying a crucial foundation for the advancement of quantum machine learning. As quantum computing hardware continues to advance, this technology will further expand its application domains in the future, accelerating the practical implementation of quantum intelligent computing and propelling quantum computing into a new stage of real-world utility. In an era where quantum computing and artificial intelligence converge, this innovation will undoubtedly serve as a significant milestone in advancing the frontiers of technology. About MicroAlgo Inc. MicroAlgo Inc. (the 'MicroAlgo'), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development. Forward-Looking Statements This press release contains statements that may constitute 'forward-looking statements.' Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, Words such as 'expect,' 'estimate,' 'project,' 'budget,' 'forecast,' 'anticipate,' 'intend,' 'plan,' 'may,' 'will,' 'could,' 'should,' 'believes,' 'predicts,' 'potential,' 'continue,' and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction. MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law. View original content: SOURCE
Yahoo
30-04-2025
- Business
- Yahoo
Here's What's Concerning About MicroAlgo's (NASDAQ:MLGO) Returns On Capital
If you're not sure where to start when looking for the next multi-bagger, there are a few key trends you should keep an eye out for. One common approach is to try and find a company with returns on capital employed (ROCE) that are increasing, in conjunction with a growing amount of capital employed. If you see this, it typically means it's a company with a great business model and plenty of profitable reinvestment opportunities. However, after briefly looking over the numbers, we don't think MicroAlgo (NASDAQ:MLGO) has the makings of a multi-bagger going forward, but let's have a look at why that may be. We've found 21 US stocks that are forecast to pay a dividend yield of over 6% next year. See the full list for free. For those that aren't sure what ROCE is, it measures the amount of pre-tax profits a company can generate from the capital employed in its business. To calculate this metric for MicroAlgo, this is the formula: Return on Capital Employed = Earnings Before Interest and Tax (EBIT) ÷ (Total Assets - Current Liabilities) 0.019 = CN¥20m ÷ (CN¥1.3b - CN¥207m) (Based on the trailing twelve months to December 2024). Thus, MicroAlgo has an ROCE of 1.9%. In absolute terms, that's a low return and it also under-performs the IT industry average of 9.4%. Check out our latest analysis for MicroAlgo Historical performance is a great place to start when researching a stock so above you can see the gauge for MicroAlgo's ROCE against it's prior returns. If you'd like to look at how MicroAlgo has performed in the past in other metrics, you can view this free graph of MicroAlgo's past earnings, revenue and cash flow. Unfortunately, the trend isn't great with ROCE falling from 24% five years ago, while capital employed has grown 379%. That being said, MicroAlgo raised some capital prior to their latest results being released, so that could partly explain the increase in capital employed. It's unlikely that all of the funds raised have been put to work yet, so as a consequence MicroAlgo might not have received a full period of earnings contribution from it. While on the subject, we noticed that the ratio of current liabilities to total assets has risen to 16%, which has impacted the ROCE. If current liabilities hadn't increased as much as they did, the ROCE could actually be even lower. Keep an eye on this ratio, because the business could encounter some new risks if this metric gets too high. Bringing it all together, while we're somewhat encouraged by MicroAlgo's reinvestment in its own business, we're aware that returns are shrinking. And investors may be expecting the fundamentals to get a lot worse because the stock has crashed 100% over the last three years. In any case, the stock doesn't have these traits of a multi-bagger discussed above, so if that's what you're looking for, we think you'd have more luck elsewhere. MicroAlgo does have some risks, we noticed 4 warning signs (and 3 which can't be ignored) we think you should know about. While MicroAlgo may not currently earn the highest returns, we've compiled a list of companies that currently earn more than 25% return on equity. Check out this free list here. Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned. Sign in to access your portfolio
