Modelling A.I. in Economics

HBIO Harvard Bioscience Inc. Common Stock

Outlook: Harvard Bioscience Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 13 Mar 2023 for (n+1 year)
Methodology : Reinforcement Machine Learning (ML)

Abstract

Harvard Bioscience Inc. Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the HBIO stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Key Points

  1. What statistical methods are used to analyze data?
  2. Decision Making
  3. What is the use of Markov decision process?

HBIO Target Price Prediction Modeling Methodology

We consider Harvard Bioscience Inc. Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of HBIO 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(Statistical Hypothesis Testing)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(Reinforcement Machine Learning (ML)) X S(n):→ (n+1 year) i = 1 n a i

n:Time series to forecast

p:Price signals of HBIO 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?

HBIO Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: HBIO Harvard Bioscience Inc. Common Stock
Time series to forecast n: 13 Mar 2023 for (n+1 year)

According to price forecasts for (n+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 Harvard Bioscience Inc. Common Stock

  1. In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
  2. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
  3. The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
  4. An entity that first applies IFRS 17 as amended in June 2020 at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.38–7.2.42.

*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

Harvard Bioscience Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Harvard Bioscience Inc. Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the HBIO stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

HBIO Harvard Bioscience Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2B3
Balance SheetCC
Leverage RatiosBaa2Baa2
Cash FlowBa2B3
Rates of Return and ProfitabilityB1B1

*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: 92 out of 100 with 865 signals.

References

  1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is DOW Stock Expected to Go Up?(Stock Forecast). AC Investment Research Journal, 101(3).
  2. 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.
  3. 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
  4. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
  5. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  6. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
  7. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
Frequently Asked QuestionsQ: What is the prediction methodology for HBIO stock?
A: HBIO stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing
Q: Is HBIO stock a buy or sell?
A: The dominant strategy among neural network is to Sell HBIO Stock.
Q: Is Harvard Bioscience Inc. Common Stock stock a good investment?
A: The consensus rating for Harvard Bioscience Inc. Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of HBIO stock?
A: The consensus rating for HBIO is Sell.
Q: What is the prediction period for HBIO stock?
A: The prediction period for HBIO is (n+1 year)



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