Modelling A.I. in Economics

OLMA Olema Pharmaceuticals Inc. Common Stock

Outlook: Olema Pharmaceuticals Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 05 Jun 2023 for (n+4 weeks)
Methodology : Modular Neural Network (DNN Layer)

Abstract

Olema Pharmaceuticals Inc. Common Stock prediction model is evaluated with Modular Neural Network (DNN Layer) and ElasticNet Regression1,2,3,4 and it is concluded that the OLMA stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Hold

Key Points

  1. How do predictive algorithms actually work?
  2. Market Risk
  3. Trading Interaction

OLMA Target Price Prediction Modeling Methodology

We consider Olema Pharmaceuticals Inc. Common Stock Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of OLMA 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(ElasticNet Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+4 weeks) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of OLMA stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

OLMA Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: OLMA Olema Pharmaceuticals Inc. Common Stock
Time series to forecast n: 05 Jun 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Hold

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 Olema Pharmaceuticals Inc. Common Stock

  1. For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness
  2. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
  3. For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
  4. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.

*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

Olema Pharmaceuticals Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Olema Pharmaceuticals Inc. Common Stock prediction model is evaluated with Modular Neural Network (DNN Layer) and ElasticNet Regression1,2,3,4 and it is concluded that the OLMA stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Hold

OLMA Olema Pharmaceuticals Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2Baa2
Balance SheetCB1
Leverage RatiosBaa2B3
Cash FlowBaa2B2
Rates of Return and ProfitabilityCaa2Baa2

*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: 90 out of 100 with 605 signals.

References

  1. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
  2. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
  3. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
  4. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
  5. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
  6. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
  7. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
Frequently Asked QuestionsQ: What is the prediction methodology for OLMA stock?
A: OLMA stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and ElasticNet Regression
Q: Is OLMA stock a buy or sell?
A: The dominant strategy among neural network is to Hold OLMA Stock.
Q: Is Olema Pharmaceuticals Inc. Common Stock stock a good investment?
A: The consensus rating for Olema Pharmaceuticals Inc. Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OLMA stock?
A: The consensus rating for OLMA is Hold.
Q: What is the prediction period for OLMA stock?
A: The prediction period for OLMA is (n+4 weeks)

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