LON:SIS Stock: A Sinking Ship?

Outlook: SCIENCE IN SPORT PLC is assigned short-term B3 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Pearson Correlation
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.

Abstract

SCIENCE IN SPORT PLC prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation^1,2,3,4 and it is concluded that the LON:SIS stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial 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 financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Key Points

1. Why do we need predictive models?
2. Nash Equilibria
3. Understanding Buy, Sell, and Hold Ratings

LON:SIS Target Price Prediction Modeling Methodology

We consider SCIENCE IN SPORT PLC Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of LON:SIS 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(Pearson Correlation)^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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ 4 Weeks $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:SIS stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Financial Sentiment Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial 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 financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.

Pearson Correlation

Pearson correlation, also known as Pearson's product-moment correlation, is a measure of the linear relationship between two variables. It is a statistical measure that assesses the strength and direction of a linear relationship between two variables. The sign of the correlation coefficient indicates the direction of the relationship, while the magnitude of the correlation coefficient indicates the strength of the relationship. A correlation coefficient of 0.9 indicates a strong positive correlation, while a correlation coefficient of 0.2 indicates a weak positive correlation.

 

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?

LON:SIS Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:SIS SCIENCE IN SPORT PLC
Time series to forecast: 4 Weeks

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

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 Modular Neural Network (Financial Sentiment Analysis) based LON:SIS Stock Prediction Model

1. When designating a risk component as a hedged item, the hedge accounting requirements apply to that risk component in the same way as they apply to other hedged items that are not risk components. For example, the qualifying criteria apply, including that the hedging relationship must meet the hedge effectiveness requirements, and any hedge ineffectiveness must be measured and recognised.
2. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.
3. Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
4. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).

*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.

LON:SIS SCIENCE IN SPORT PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba2
Income Statement	C	Baa2
Balance Sheet	Caa2	B1
Leverage Ratios	B1	C
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	Ba3	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?

Conclusions

SCIENCE IN SPORT PLC is assigned short-term B3 & long-term Ba2 estimated rating. SCIENCE IN SPORT PLC prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation^1,2,3,4 and it is concluded that the LON:SIS stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 613 signals.

References

1. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
2. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
3. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
4. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
5. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
6. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
7. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000

Frequently Asked Questions

Q: What is the prediction methodology for LON:SIS stock?
A: LON:SIS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation
Q: Is LON:SIS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SIS Stock.
Q: Is SCIENCE IN SPORT PLC stock a good investment?
A: The consensus rating for SCIENCE IN SPORT PLC is Hold and is assigned short-term B3 & long-term Ba2 estimated rating.
Q: What is the consensus rating of LON:SIS stock?
A: The consensus rating for LON:SIS is Hold.
Q: What is the prediction period for LON:SIS stock?
A: The prediction period for LON:SIS is 4 Weeks

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