Should You Buy Now or Wait? ALNY Stock Forecast

Outlook: Alnylam Pharmaceuticals Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 17 Jun 2023 for 8 Weeks
Methodology : Active Learning (ML)

Abstract

Alnylam Pharmaceuticals Inc. Common Stock prediction model is evaluated with Active Learning (ML) and Paired T-Test^1,2,3,4 and it is concluded that the ALNY stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. What are the most successful trading algorithms?
2. Can we predict stock market using machine learning?
3. Short/Long Term Stocks

ALNY Target Price Prediction Modeling Methodology

We consider Alnylam Pharmaceuticals Inc. Common Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of ALNY 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(Paired T-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(Active Learning (ML)) X S(n):→ 8 Weeks $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of ALNY stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Active Learning (ML)

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.

Paired T-Test

A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.

 

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?

ALNY Stock Forecast (Buy or Sell) for 8 Weeks

Sample Set: Neural Network
Stock/Index: ALNY Alnylam Pharmaceuticals Inc. Common Stock
Time series to forecast n: 17 Jun 2023 for 8 Weeks

According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

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 Alnylam Pharmaceuticals Inc. Common Stock

1. If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
2. 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.
3. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.
4. At the date of initial application, an entity is permitted to make the designation in paragraph 2.5 for contracts that already exist on the date but only if it designates all similar contracts. The change in the net assets resulting from such designations shall be recognised in retained earnings at the date of initial application.

*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

Alnylam Pharmaceuticals Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Alnylam Pharmaceuticals Inc. Common Stock prediction model is evaluated with Active Learning (ML) and Paired T-Test^1,2,3,4 and it is concluded that the ALNY stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

ALNY Alnylam Pharmaceuticals Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Ba3
Balance Sheet	B1	Caa2
Leverage Ratios	C	B2
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	Baa2	Ba3

*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: 73 out of 100 with 701 signals.

References

1. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
2. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
3. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
4. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
5. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
6. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
7. Harris ZS. 1954. Distributional structure. Word 10:146–62

Frequently Asked Questions

Q: What is the prediction methodology for ALNY stock?
A: ALNY stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Paired T-Test
Q: Is ALNY stock a buy or sell?
A: The dominant strategy among neural network is to Buy ALNY Stock.
Q: Is Alnylam Pharmaceuticals Inc. Common Stock stock a good investment?
A: The consensus rating for Alnylam Pharmaceuticals Inc. Common Stock is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ALNY stock?
A: The consensus rating for ALNY is Buy.
Q: What is the prediction period for ALNY stock?
A: The prediction period for ALNY is 8 Weeks

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