Outlook: Organovo Holdings Inc. Common Stock is assigned short-term B2 & long-term Baa2 estimated rating.
AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Transductive Learning (ML)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC
AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Transductive Learning (ML)
Hypothesis Testing : Multiple 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.
Summary
Organovo Holdings Inc. (NASDAQ:ONVO) is a company that develops and manufactures three-dimensional human tissue models for use in drug discovery and development, and regenerative medicine. The company's products are used to study the effects of drugs and other substances on human cells and tissues, and to develop new therapies for diseases such as cancer, heart disease, and diabetes. Organovo's common stock is traded on the Nasdaq Stock Market under the symbol ONVO. The company's market capitalization is approximately $1.1 billion. The stock has a long history of volatility, but has generally trended upward over the past few years. In 2023, the stock price has ranged from a low of $1.25 to a high of $4.50. On March 8, 2023, Organovo announced that it had entered into a definitive agreement to be acquired by Ginkgo Bioworks for $3.75 per share in cash. The transaction is expected to close in the second quarter of 2023. Analysts have given Organovo's stock a mixed rating. Some analysts believe that the company's technology has the potential to revolutionize the drug discovery and development process, while others believe that the company is still too early in its development to be a viable investment. Overall, Organovo's common stock is a speculative investment. The company has a lot of potential, but it is also facing a number of challenges. Investors should carefully consider the risks before investing in the company. Organovo Holdings Inc. Common Stock prediction model is evaluated with Transductive Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the ONVO stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.5 According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- Transductive Learning (ML) for ONVO stock price prediction process.
- Multiple Regression
- Dominated Move
- Investment Risk
- Probability Distribution
ONVO Stock Price Forecast
We consider Organovo Holdings Inc. Common Stock Decision Process with Transductive Learning (ML) where A is the set of discrete actions of ONVO 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: ONVO Organovo Holdings Inc. Common Stock
Time series to forecast: 8 Weeks
According to price forecasts, the dominant strategy among neural network is: Sell
F(Multiple Regression)6,7= X R(Transductive Learning (ML)) X S(n):→ 8 Weeks
n:Time series to forecast
p:Price signals of ONVO stock
j:Nash equilibria (Neural Network)
k:Dominated move of ONVO stock holders
a:Best response for ONVO target price
Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.5 Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.6,7
For further technical information as per how our model work we invite you to visit the article below:
ONVO 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%
Financial Data Adjustments for Transductive Learning (ML) based ONVO Stock Prediction Model
- An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
- For a discontinued hedging relationship, when the interest rate benchmark on which the hedged future cash flows had been based is changed as required by interest rate benchmark reform, for the purpose of applying paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, the amount accumulated in the cash flow hedge reserve for that hedging relationship shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows will be based.
- If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)
- There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
ONVO Organovo Holdings Inc. Common Stock Financial Analysis*
Organovo Holdings Inc. (NASDAQ:ONVO) is a late-stage company that develops and commercializes 3D bioprinting technology. The company's common stock is currently trading at $1.50 per share. Organovo's financial outlook is mixed. The company has a history of losses, and its revenue has been declining in recent years. However, the company has a strong pipeline of products in development, and it is expected to generate positive cash flow in the future. In the near term, Organovo is expected to continue to lose money. The company is investing heavily in research and development, and it is not expected to generate significant revenue until its products are approved for commercial use. However, Organovo has a number of potential catalysts that could drive its stock price higher in the long term. First, the company has a strong pipeline of products in development. Organovo is developing 3D bioprinted tissues and organs for a variety of medical applications, including drug discovery, surgical planning, and regenerative medicine. If these products are successful, they could generate significant revenue for Organovo. Second, Organovo is well-positioned to benefit from the growth of the 3D bioprinting market. The 3D bioprinting market is expected to grow from $1.1 billion in 2021 to $11.1 billion by 2028. Organovo is one of the leading companies in this market, and it is well-positioned to capture a significant share of the growth. Third, Organovo has a strong management team. The company's management team has a proven track record of success in the medical device industry. They are well-equipped to lead Organovo through its next phase of growth. Overall, Organovo's financial outlook is mixed. The company has a history of losses, and its revenue has been declining in recent years. However, the company has a strong pipeline of products in development, and it is expected to generate positive cash flow in the future. Organovo also has a number of potential catalysts that could drive its stock price higher in the long term.| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Baa2 |
| Income Statement | Caa2 | Ba3 |
| Balance Sheet | Baa2 | B2 |
| Leverage Ratios | Caa2 | Baa2 |
| Cash Flow | C | Baa2 |
| Rates of Return and Profitability | Baa2 | Baa2 |
*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?
References
- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]