BIOC Biocept Inc. Common Stock

Outlook: Biocept Inc. Common Stock assigned short-term Baa2 & long-term B2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 09 Dec 2022 for (n+4 weeks)
Methodology : Inductive Learning (ML)

Abstract

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. (Song, Y., 2018. Stock trend prediction: Based on machine learning methods (Doctoral dissertation, UCLA).) We evaluate Biocept Inc. Common Stock prediction models with Inductive Learning (ML) and Paired T-Test^1,2,3,4 and conclude that the BIOC stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Trading Interaction
2. Investment Risk
3. What are main components of Markov decision process?

BIOC Target Price Prediction Modeling Methodology

We consider Biocept Inc. Common Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of BIOC 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(Inductive Learning (ML)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of BIOC 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?

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

Sample Set: Neural Network
Stock/Index: BIOC Biocept Inc. Common Stock
Time series to forecast n: 09 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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%

Adjusted IFRS* Prediction Methods for Biocept Inc. Common Stock

1. The business model may be to hold assets to collect contractual cash flows even if the entity sells financial assets when there is an increase in the assets' credit risk. To determine whether there has been an increase in the assets' credit risk, the entity considers reasonable and supportable information, including forward looking information. Irrespective of their frequency and value, sales due to an increase in the assets' credit risk are not inconsistent with a business model whose objective is to hold financial assets to collect contractual cash flows because the credit quality of financial assets is relevant to the entity's ability to collect contractual cash flows. Credit risk management activities that are aimed at minimising potential credit losses due to credit deterioration are integral to such a business model. Selling a financial asset because it no longer meets the credit criteria specified in the entity's documented investment policy is an example of a sale that has occurred due to an increase in credit risk. However, in the absence of such a policy, the entity may demonstrate in other ways that the sale occurred due to an increase in credit risk.
2. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
3. Sales that occur for other reasons, such as sales made to manage credit concentration risk (without an increase in the assets' credit risk), may also be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows. In particular, such sales may be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows if those sales are infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). If more than an infrequent number of such sales are made out of a portfolio and those sales are more than insignificant in value (either individually or in aggregate), the entity needs to assess whether and how such sales are consistent with an objective of collecting contractual cash flows. Whether a third party imposes the requirement to sell the financial assets, or that activity is at the entity's discretion, is not relevant to this assessment. An increase in the frequency or value of sales in a particular period is not necessarily inconsistent with an objective to hold financial assets in order to collect contractual cash flows, if an entity can explain the reasons for those sales and demonstrate why those sales do not reflect a change in the entity's business model. In addition, sales may be consistent with the objective of holding financial assets in order to collect contractual cash flows if the sales are made close to the maturity of the financial assets and the proceeds from the sales approximate the collection of the remaining contractual cash flows.
4. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Biocept Inc. Common Stock assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Paired T-Test^1,2,3,4 and conclude that the BIOC stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Financial State Forecast for BIOC Biocept Inc. Common Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	B2
Operational Risk	79	55
Market Risk	69	45
Technical Analysis	71	56
Fundamental Analysis	73	36
Risk Unsystematic	70	77

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 635 signals.

References

1. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
2. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
3. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
4. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
5. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
6. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
7. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.

Frequently Asked Questions

Q: What is the prediction methodology for BIOC stock?
A: BIOC stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Paired T-Test
Q: Is BIOC stock a buy or sell?
A: The dominant strategy among neural network is to Sell BIOC Stock.
Q: Is Biocept Inc. Common Stock stock a good investment?
A: The consensus rating for Biocept Inc. Common Stock is Sell and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of BIOC stock?
A: The consensus rating for BIOC is Sell.
Q: What is the prediction period for BIOC stock?
A: The prediction period for BIOC is (n+4 weeks)

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