AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Modular Neural Network (DNN Layer)
Hypothesis Testing : Polynomial 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
Relay Therapeutics Inc. Common Stock prediction model is evaluated with Modular Neural Network (DNN Layer) and Polynomial Regression1,2,3,4 and it is concluded that the RLAY stock is predictable in the short/long term. In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.5 According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell
Key Points
- Modular Neural Network (DNN Layer) for RLAY stock price prediction process.
- Polynomial Regression
- Game Theory
- Trading Signals
- Dominated Move
RLAY Stock Price Forecast
We consider Relay Therapeutics Inc. Common Stock Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of RLAY 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: RLAY Relay Therapeutics Inc. Common Stock
Time series to forecast: 3 Month
According to price forecasts, the dominant strategy among neural network is: Sell
n:Time series to forecast
p:Price signals of RLAY stock
j:Nash equilibria (Neural Network)
k:Dominated move of RLAY stock holders
a:Best response for RLAY target price
In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.5 Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.6,7
For further technical information as per how our model work we invite you to visit the article below:
RLAY 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 Modular Neural Network (DNN Layer) based RLAY Stock Prediction Model
- An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
- When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.
- For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
*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.
RLAY Relay Therapeutics Inc. Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | B2 |
Income Statement | B1 | C |
Balance Sheet | Caa2 | Baa2 |
Leverage Ratios | B1 | B1 |
Cash Flow | Caa2 | C |
Rates of Return and Profitability | Baa2 | B1 |
*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
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked Questions
Q: Is RLAY stock expected to rise?A: RLAY stock prediction model is evaluated with Modular Neural Network (DNN Layer) and Polynomial Regression and it is concluded that dominant strategy for RLAY stock is Sell
Q: Is RLAY stock a buy or sell?
A: The dominant strategy among neural network is to Sell RLAY Stock.
Q: Is Relay Therapeutics Inc. Common Stock stock a good investment?
A: The consensus rating for Relay Therapeutics Inc. Common Stock is Sell and is assigned short-term B2 & long-term B2 estimated rating.
Q: What is the consensus rating of RLAY stock?
A: The consensus rating for RLAY is Sell.
Q: What is the forecast for RLAY stock?
A: RLAY target price forecast: Sell
People also ask
⚐ What are the top stocks to invest in right now?☵ What happens to stocks when they're delisted?
- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research data (API,CSV,JSON)
- Real-time updates
- In-depth research reports (PDF)