SPNE Stock: A Risky Investment, But One with a lot of Potential

Outlook: SeaSpine Holdings Corporation Common Stock is assigned short-term B1 & long-term B2 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 23 Jun 2023 for 1 Year
Methodology : Multi-Instance Learning (ML)

Summary

SeaSpine Holdings Corporation Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the SPNE stock is predictable in the short/long term. Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Key Points

1. What are the most successful trading algorithms?
2. What is statistical models in machine learning?
3. What is Markov decision process in reinforcement learning?

SPNE Target Price Prediction Modeling Methodology

We consider SeaSpine Holdings Corporation Common Stock Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of SPNE 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

F(Wilcoxon Sign-Rank Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ 1 Year $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of SPNE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Instance Learning (ML)

Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance.

Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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

How do AC Investment Research machine learning (predictive) algorithms actually work?

SPNE Stock Forecast (Buy or Sell) for 1 Year

Sample Set: Neural Network
Stock/Index: SPNE SeaSpine Holdings Corporation Common Stock
Time series to forecast n: 23 Jun 2023 for 1 Year

According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

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%

IFRS Reconciliation Adjustments for SeaSpine Holdings Corporation Common Stock

1. A firm commitment to acquire a business in a business combination cannot be a hedged item, except for foreign currency risk, because the other risks being hedged cannot be specifically identified and measured. Those other risks are general business risks.
2. For example, Entity A, whose functional currency is its local currency, has a firm commitment to pay FC150,000 for advertising expenses in nine months' time and a firm commitment to sell finished goods for FC150,000 in 15 months' time. Entity A enters into a foreign currency derivative that settles in nine months' time under which it receives FC100 and pays CU70. Entity A has no other exposures to FC. Entity A does not manage foreign currency risk on a net basis. Hence, Entity A cannot apply hedge accounting for a hedging relationship between the foreign currency derivative and a net position of FC100 (consisting of FC150,000 of the firm purchase commitment—ie advertising services—and FC149,900 (of the FC150,000) of the firm sale commitment) for a nine-month period.
3. When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.
4. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income subsequent changes in the fair value of particular investments in equity instruments. Such an investment is not a monetary item. Accordingly, the gain or loss that is presented in other comprehensive income in accordance with paragraph 5.7.5 includes any related foreign exchange component.

*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.

Conclusions

SeaSpine Holdings Corporation Common Stock is assigned short-term B1 & long-term B2 estimated rating. SeaSpine Holdings Corporation Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the SPNE stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

SPNE SeaSpine Holdings Corporation Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B2
Income Statement	B1	C
Balance Sheet	Caa2	Ba3
Leverage Ratios	Baa2	Caa2
Cash Flow	B2	B1
Rates of Return and Profitability	Caa2	Caa2

*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?

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 674 signals.

References

1. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
2. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
3. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
4. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
5. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
6. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
7. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.

Frequently Asked Questions

Q: What is the prediction methodology for SPNE stock?
A: SPNE stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is SPNE stock a buy or sell?
A: The dominant strategy among neural network is to Sell SPNE Stock.
Q: Is SeaSpine Holdings Corporation Common Stock stock a good investment?
A: The consensus rating for SeaSpine Holdings Corporation Common Stock is Sell and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of SPNE stock?
A: The consensus rating for SPNE is Sell.
Q: What is the prediction period for SPNE stock?
A: The prediction period for SPNE is 1 Year

People also ask

⚐ What are the top stocks to invest in right now?
☵ What happens to stocks when they're delisted?