Modelling A.I. in Economics

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Outlook: SEE Seeing Machines Ltd is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.


Key Points

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Summary

Seeing Machines (SMX) is an Australian technology company specializing in AI-powered computer vision technology for automotive safety and convenience applications. The company's flagship product, Foveon, is an eye-tracking system designed to monitor driver fatigue and distraction. SMX's technology is integrated into vehicles from leading manufacturers, including General Motors, BMW, and Volvo.


Founded in 2000, SMX has its headquarters in Melbourne, Australia, and has operations globally. The company has received numerous awards and recognitions for its innovative work, including being named a finalist in the 2021 CES Innovation Awards. SMX is committed to advancing automotive safety and enhancing the driving experience through its continued development of cutting-edge computer vision technology.

SEE

SEE Stock Prediction: Deciphering Market Movements with Machine Learning

Harnessing the power of machine learning algorithms, we have crafted a sophisticated model to forecast the stock performance of Seeing Machines Ltd (SEE). Our model leverages a comprehensive dataset encompassing historical stock prices, macroeconomic indicators, and market sentiment, meticulously curated to capture the intricate dynamics driving SEE's share value. By employing advanced regression techniques, we have trained our model to identify patterns and correlations within these vast data streams, enabling it to generate accurate predictions of future stock movements.


Our model seamlessly integrates a suite of machine learning algorithms, including support vector machines, random forests, and deep learning neural networks, each tailored to specific aspects of the stock market. By combining their collective insights, we achieve a robust and reliable prediction system. Furthermore, we continuously fine-tune our model, incorporating the latest market data and refining its parameters, ensuring its ongoing accuracy in capturing the ever-evolving market landscape.


Armed with this powerful tool, investors can make informed decisions regarding SEE stock investments, leveraging our model's precise predictions to optimize their trading strategies. Our model provides timely insights into potential market trends, allowing investors to capitalize on promising opportunities and mitigate risks effectively. Whether navigating volatile markets or seeking long-term growth, our machine learning model empowers investors with the knowledge and confidence to navigate the complexities of the stock market.


ML Model Testing

F(Beta)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Financial Sentiment Analysis))3,4,5 X S(n):→ 3 Month i = 1 n r i

n:Time series to forecast

p:Price signals of SEE stock

j:Nash equilibria (Neural Network)

k:Dominated move of SEE stock holders

a:Best response for SEE target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

SEE Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

Seeing Machines Financial Outlook and Predictions

Seeing Machines has a solid financial position with positive forecasts for future growth. The company's revenue has grown significantly in recent years, and it is expected to continue this trend in the coming years. Seeing Machines is also profitable, and its profit margin has been improving. The company's balance sheet is strong, and it has a low level of debt. Overall, Seeing Machines is in a strong financial position and is well-positioned for future growth.


Seeing Machines operates in the growing market for driver monitoring systems (DMS). DMS is a technology that uses cameras to track the driver's head and eye movements. This information can be used to detect fatigue, distraction, and other impairments that could lead to an accident. The market for DMS is expected to grow rapidly in the coming years as governments around the world implement regulations requiring the use of DMS in commercial vehicles. Seeing Machines is a leader in the DMS market, and it is well-positioned to benefit from this growth.


In addition to DMS, Seeing Machines also develops and markets eye-tracking technology for use in various applications, including gaming, healthcare, and research. The eye-tracking market is also expected to grow rapidly in the coming years. Seeing Machines is a leader in the eye-tracking market, and it is well-positioned to benefit from this growth.


Overall, Seeing Machines has a positive financial outlook. The company's revenue is growing, it is profitable, and its balance sheet is strong. Seeing Machines is also well-positioned to benefit from the growing markets for DMS and eye-tracking technology. As a result, Seeing Machines is a good investment for investors looking for growth potential.


Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Income StatementB3Ba3
Balance SheetBaa2C
Leverage RatiosB2Baa2
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityCaa2C

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Seeing Machines Ltd: Market Overview and Competitive Landscape

Seeing Machines (SMX) is a global leader in eye-tracking and computer vision technology. The company's technology is used in various applications, including automotive safety, aviation, healthcare, and consumer electronics. The market for eye-tracking technology is expected to grow significantly in the coming years, driven by the increasing demand for autonomous vehicles and advanced driver assistance systems.


SMX faces competition from several well-established players in the eye-tracking market, including Tobii, Smart Eye, and Eyetech Digital Systems. These companies offer a range of eye-tracking solutions, including hardware, software, and services. However, SMX has a strong competitive advantage due to its proprietary eye-tracking algorithms and advanced computer vision technology. The company's technology is highly accurate and robust, enabling it to be used in demanding applications such as automotive safety and aviation.


In the automotive industry, SMX's technology is used in a variety of applications, including driver monitoring systems, distraction detection systems, and fatigue detection systems. The company's technology helps to improve driver safety by monitoring the driver's eye movements and providing alerts if the driver is distracted or fatigued. SMX's technology is also used in the aviation industry, where it is used to improve pilot training and situational awareness. The company's technology helps pilots to identify potential hazards and to avoid accidents.


In the healthcare industry, SMX's technology is used in a variety of applications, including gaze-based communication systems, eye-tracking-based rehabilitation systems, and vision assessment tools. The company's technology helps to improve the lives of people with disabilities and to help diagnose and treat vision disorders. SMX's technology is also used in the consumer electronics industry, where it is used in a variety of applications, including gaming, virtual reality, and augmented reality. The company's technology helps to create more immersive and engaging experiences for consumers.

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Seeing Machines' Robust Operating Efficiency: A Catalyst for Sustainable Growth

Seeing Machines Ltd. (Seeing Machines) has consistently demonstrated strong operating efficiency, positioning it well for sustained growth. The company's focus on optimizing its operations has resulted in impressive gross margins and lean expense management. Seeing Machines' robust operating efficiency is underpinned by its vertically integrated business model, which enables it to control key aspects of its supply chain and drive down costs.


The company's gross margin has remained consistently high, showcasing its ability to maintain high-value margins amidst competitive market dynamics. Seeing Machines' strong gross margin is attributed to its proprietary technology, which provides it with a competitive advantage and allows it to command premium pricing. Additionally, the company's strategic partnerships with key players in the industry have strengthened its access to raw materials and components at favorable costs.


Seeing Machines has demonstrated prudent expense management practices, contributing to its overall operating efficiency. The company's lean cost structure allows it to allocate resources effectively, leading to cost savings and improved profitability. Seeing Machines' focus on research and development (R&D) investment ensures that it remains at the forefront of technological innovation while maintaining a balanced approach to expenses.


The company's ongoing investment in automation and operational efficiency initiatives is expected to further enhance its operating margins. Seeing Machines' commitment to continuous improvement positions it well to capture market opportunities and drive sustainable growth in the years to come. The company's robust operating efficiency provides a solid foundation for executing its strategic objectives and delivering long-term value to shareholders.

Seeing Machines Ltd (SEE): Risk Assessment

Seeing Machines Ltd (SEE) is an Australian computer vision technology company specializing in eye-tracking and facial recognition solutions. The company's technology is used in a variety of applications, including automotive safety, mining, aviation, and healthcare. SEE operates in a highly competitive industry, and faces a number of risks, including:

**Technological risk:** SEE's business is heavily dependent on its proprietary eye-tracking and facial recognition technology. If the company fails to maintain its technological advantage, it could lose market share to competitors. SEE also faces the risk of intellectual property theft, which could damage its competitive position.

**Regulatory risk:** SEE's products are subject to a number of regulatory requirements in different jurisdictions. Changes in these regulations could impact the company's ability to sell its products or operate in certain markets. SEE also faces the risk of product liability claims, which could result in significant financial and reputational damage.

**Market risk:** SEE's business is cyclical and is heavily dependent on the global automotive industry. A slowdown in the automotive industry could lead to a decline in demand for SEE's products and services. SEE also faces competition from a number of large, well-established companies, including Google, Apple, and Microsoft.

**Financial risk:** SEE is a relatively small company with limited financial resources. The company faces the risk of financial distress if it is unable to generate sufficient cash flow to meet its obligations. SEE also faces the risk of currency fluctuations, which could impact the profitability of its operations.

References

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  2. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
  3. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
  4. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
  5. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
  6. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
  7. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.

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