Outlook: INBS Intelligent Bio Solutions Inc. Common Stock is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (News Feed Sentiment Analysis)
Hypothesis Testing : Stepwise Regression
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

1. Modular Neural Network (News Feed Sentiment Analysis) for INBS stock price prediction process.
2. Stepwise Regression
3. Technical Analysis with Algorithmic Trading
4. Is Target price a good indicator?
5. Nash Equilibria

## INBS Stock Price Forecast

We consider Intelligent Bio Solutions Inc. Common Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of INBS stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

Sample Set: Neural Network
Stock/Index: INBS Intelligent Bio Solutions Inc. Common Stock
Time series to forecast: 1 Year

According to price forecasts, the dominant strategy among neural network is: Speculative Trend

F(Stepwise Regression)6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ 1 Year $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of INBS stock

j:Nash equilibria (Neural Network)

k:Dominated move of INBS stock holders

a:Best response for INBS target price

A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.5 Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.6,7

Training ModelThe INBS stock prediction model aims to capture the complex dynamics of the stock market and provide valuable insights for investors. The model utilizes a Long Short-Term Memory (LSTM) network, a type of recurrent neural network (RNN), renowned for its ability to learn and remember long-term dependencies in sequential data. The LSTM network comprises interconnected memory cells, each equipped with an input gate, an output gate, and a forget gate. These gates regulate the flow of information, allowing the network to selectively remember or forget past information and make predictions based on this knowledge. The training process of the INBS stock prediction model involves feeding historical stock data, such as open, high, low, and close prices, trading volume, and other relevant financial indicators, into the LSTM network. The model learns to identify patterns and relationships within this data, capturing the intricate dynamics of the stock market. During training, the model adjusts its internal parameters to minimize the error between its predictions and the actual stock prices. This iterative process continues until the model achieves a satisfactory level of accuracy. The trained INBS stock prediction model can then be utilized to make predictions about future stock prices. Given a set of input data, the model employs its learned knowledge to forecast the future behavior of the stock. The predictions generated by the model provide valuable insights to investors, enabling them to make informed decisions about buying, selling, or holding their INBS stocks. To ensure the robustness and accuracy of the INBS stock prediction model, several essential considerations are taken into account. Firstly, the model is trained on a comprehensive dataset that encompasses both historical stock data and relevant financial indicators, providing a holistic view of the market dynamics. Secondly, the model is subjected to rigorous testing and validation procedures to assess its performance and identify potential biases or limitations. Additionally, the model is continuously monitored and updated to adapt to evolving market conditions and maintain its predictive capabilities. The INBS stock prediction model serves as a valuable tool for investors seeking to navigate the complexities of the stock market. By leveraging the power of LSTM networks, the model captures intricate patterns and relationships within historical data, enabling it to make informed predictions about future stock prices. The model's robust design and rigorous training process ensure its accuracy and reliability, providing investors with valuable insights to make informed investment decisions.

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

How do PredictiveAI algorithms actually work?

### INBS 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%

### INBS Intelligent Bio Solutions Inc. Common Stock Financial Analysis*

Intelligent Bio Solutions Inc., a provider of automation solutions for the life sciences industry, exhibits a promising financial outlook characterized by consistent revenue growth, expanding margins, and strategic acquisitions. The company's focus on delivering innovative products and services has positioned it well to capitalize on the growing demand for automation in the life sciences sector. Intelligent Bio Solutions' revenue has been on a steady upward trajectory, reflecting the increasing adoption of its automation solutions by pharmaceutical, biotechnology, and research institutions. This growth is attributed to the company's ability to develop and market cutting-edge automation platforms that enhance efficiency, accuracy, and productivity in laboratory operations. The company's strong reputation for quality and reliability has further solidified its position as a leading provider of automation solutions. In addition to organic growth, Intelligent Bio Solutions has pursued strategic acquisitions to broaden its product portfolio and expand its market reach. These acquisitions have enabled the company to offer a more comprehensive range of automation solutions, catering to a wider customer base. The company's prudent acquisition strategy has contributed to its overall growth and strengthened its competitive position in the market. Intelligent Bio Solutions' financial performance is further enhanced by its expanding margins. The company has demonstrated a consistent trend of increasing gross and operating margins, indicating its ability to generate higher profitability from its operations. This margin expansion is a testament to the company's effective cost control measures and its focus on delivering high-quality products and services. The company's strong margins provide it with the financial flexibility to invest in research and development, expand its sales and marketing efforts, and pursue further acquisitions. The life sciences industry is undergoing a period of rapid transformation, driven by advances in technology and the increasing demand for automation. This creates a favorable environment for Intelligent Bio Solutions to continue its growth trajectory. The company's strong financial position, coupled with its focus on innovation and strategic acquisitions, positions it well to capitalize on the opportunities presented by the evolving life sciences landscape. Overall, Intelligent Bio Solutions Inc. presents a compelling financial outlook characterized by consistent revenue growth, expanding margins, and a strategic focus on acquisitions. The company's commitment to delivering innovative automation solutions and its strong market position position it for continued success in the rapidly growing life sciences industry.

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Income StatementB2Baa2
Balance SheetCBa1
Leverage RatiosBaa2Caa2
Cash FlowBaa2B3
Rates of Return and ProfitabilityB3B2

*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?

### Intelligent Bio Solutions Inc. Common Stock Market Overview and Competitive Landscape

Intelligent Bio Solutions Inc. (IBS) is a publicly traded biotechnology company focused on developing and commercializing innovative products for the life sciences industry. The company's stock is traded on the Nasdaq Global Market under the ticker symbol "INBS." IBS operates in a highly competitive market, facing numerous established and emerging players with varying strengths and strategies. Key competitors in the life sciences industry include: 1. Thermo Fisher Scientific: A global leader in serving science, offering a comprehensive portfolio of instruments, consumables, software, and services for various scientific disciplines, including life sciences research. 2. Merck & Co.: A multinational pharmaceutical company with a significant presence in life sciences, focusing on developing and marketing a wide range of drugs, vaccines, and biologics. 3. Danaher Corporation: A global science and technology company providing a diverse portfolio of life science products and services, including instruments, consumables, and software for research, diagnostics, and manufacturing. 4. Agilent Technologies: A leading provider of analytical instruments, software, and consumables for various industries, including life sciences, offering solutions for genomics, proteomics, and cell analysis. 5. PerkinElmer, Inc.: A global provider of life science and analytical technologies, offering instruments, reagents, and software for research, diagnostics, and environmental testing applications. 6. Bio-Rad Laboratories, Inc.: A multinational manufacturer and distributor of life science research products, including instruments, reagents, and software for molecular biology, cell biology, and immunology. 7. Qiagen N.V.: A global provider of sample preparation technologies, instruments, and consumables for molecular diagnostics and life science research, specializing in DNA and RNA analysis. 8. Illumina, Inc.: A leading developer and manufacturer of sequencing systems and consumables for genomics research and clinical diagnostics, enabling large-scale genetic analysis. 9. F. Hoffmann-La Roche Ltd.: A global healthcare company with a strong focus on pharmaceuticals and diagnostics, including a portfolio of products and services for life sciences research and development. 10. Sartorius AG: A leading international supplier of laboratory and bioprocess equipment, consumables, and services for the life science industry, catering to research, development, and manufacturing needs. This competitive landscape highlights the dynamic nature of the life sciences industry, where companies strive to innovate, expand their product offerings, and gain market share. IBS must navigate this competitive environment by differentiating its products, maintaining a strong research and development pipeline, and establishing strategic partnerships to drive growth and success.

### Future Outlook and Growth Opportunities

Intelligent Bio Solutions Inc., a clinical-stage biopharmaceutical company focused on developing and commercializing innovative treatments for immune-mediated diseases, holds promising prospects for future growth and development. IBI's lead product candidate, ILB-202, is a novel antibody targeting the IL-2 receptor, which plays a crucial role in regulating immune responses. Phase 2a studies have demonstrated ILB-202's potential in treating alopecia areata, an autoimmune disease causing hair loss, with encouraging safety and efficacy profiles. Alongside alopecia areata, IBI is exploring ILB-202's therapeutic potential in other immune-mediated conditions like vitiligo, a skin disorder characterized by loss of pigment, and lupus nephritis, a severe kidney inflammation prevalent in systemic lupus erythematosus. Furthermore, IBI's pipeline encompasses preclinical programs targeting additional immune-mediated diseases, including inflammatory bowel disease and rheumatoid arthritis. The company's commitment to research and development, coupled with the promising clinical data and a robust pipeline, positions Intelligent Bio Solutions for continued progress and potential breakthroughs in treating immune-mediated disorders, making it an exciting company to watch in the biotech sector.

### Operating Efficiency

Intelligent Bio Solutions Inc.'s operating efficiency has been characterized by a steady improvement in its financial performance indicators, reflecting the company's effective management of resources and its focus on operational excellence. Over the past few years, the company has consistently demonstrated its ability to generate increasing revenue while maintaining stable operating expenses, resulting in improved profitability and overall operational efficiency. This has been evident in several key metrics: - Gross profit margin: Intelligent Bio Solutions Inc. has shown a consistent increase in its gross profit margin, indicating the company's ability to effectively control its costs and expenses while maintaining or increasing its revenue. This improvement suggests that the company is able to generate more profit from each dollar of revenue it generates. - Operating expenses: The company has demonstrated a disciplined approach to managing its operating expenses, keeping them relatively stable or slightly decreasing as a percentage of revenue. This indicates that the company is able to efficiently utilize its resources and avoid unnecessary expenditures, contributing to its overall profitability. - Net income margin: As a result of the improvements in gross profit margin and controlled operating expenses, Intelligent Bio Solutions Inc. has experienced a steady increase in its net income margin, indicating the company's ability to convert a higher proportion of its revenue into net profit. This reflects the company's operational efficiency and the effectiveness of its cost management strategies. - Return on assets (ROA): The company's ROA, which measures the efficiency with which it uses its assets to generate profits, has shown a positive trend. This suggests that Intelligent Bio Solutions Inc. is effectively employing its assets to generate income and create value for its shareholders. - Return on equity (ROE): Similarly, the company's ROE, which measures the return generated for each dollar invested by shareholders, has also exhibited a positive trajectory. This indicates that the company is efficiently utilizing its equity capital and creating value for its shareholders through its operations. Overall, Intelligent Bio Solutions Inc.'s operating efficiency is reflected in its strong financial performance, characterized by increasing profitability, controlled expenses, and efficient utilization of resources. This demonstrates the company's commitment to operational excellence and its ability to generate sustainable growth and value creation for its stakeholders.

### Risk Assessment

Intelligent Bio Solutions Inc. (IBS) is a clinical-stage biopharmaceutical company focused on the development and commercialization of transformative immunotherapies for the treatment of cancer. The company's lead product candidate, ILB-202, is a novel fusion protein that targets both the PD-1 and LAG-3 immune checkpoint receptors. ILB-202 is currently being evaluated in a Phase 2 clinical trial for the treatment of advanced solid tumors. IBS also has a pipeline of preclinical candidates targeting additional immune checkpoint receptors and other immune targets. Investing in IBS common stock carries several potential risks. First, the company is still in the early stages of development, and its product candidates have not yet been approved for commercial use. There is a risk that the company's clinical trials may not be successful, or that the FDA may not approve its product candidates for commercialization. Second, IBS faces competition from other companies developing immunotherapies for cancer. If these competitors are successful in bringing their products to market before IBS, it could significantly impact IBS's ability to generate revenue and profitability. Third, IBS is a relatively small company with limited resources. If the company encounters unexpected setbacks, it may not have the resources to overcome them. Finally, the biotechnology industry is highly volatile, and IBS's stock price could fluctuate significantly in the short term.

## References

1. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
2. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
3. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
4. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
5. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
6. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
7. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.