Outlook: TRxADE HEALTH Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 09 Jun 2023 for 1 Year
Methodology : Supervised Machine Learning (ML)

Abstract

TRxADE HEALTH Inc. Common Stock prediction model is evaluated with Supervised Machine Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the MEDS stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

Key Points

1. Can machine learning predict?
2. Is now good time to invest?
3. Buy, Sell and Hold Signals

MEDS Target Price Prediction Modeling Methodology

We consider TRxADE HEALTH Inc. Common Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of MEDS 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(Ridge Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ 1 Year $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of MEDS stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Supervised Machine Learning (ML)

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

Ridge Regression

Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.

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?

MEDS Stock Forecast (Buy or Sell) for 1 Year

Sample Set: Neural Network
Stock/Index: MEDS TRxADE HEALTH Inc. Common Stock
Time series to forecast n: 09 Jun 2023 for 1 Year

According to price forecasts for 1 Year 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%

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. An embedded prepayment option in an interest-only or principal-only strip is closely related to the host contract provided the host contract (i) initially resulted from separating the right to receive contractual cash flows of a financial instrument that, in and of itself, did not contain an embedded derivative, and (ii) does not contain any terms not present in the original host debt contract.
3. An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
4. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.

*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

TRxADE HEALTH Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. TRxADE HEALTH Inc. Common Stock prediction model is evaluated with Supervised Machine Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the MEDS stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

MEDS TRxADE HEALTH Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2Baa2
Balance SheetBa3Ba2
Leverage RatiosCaa2Ba2
Cash FlowCBaa2
Rates of Return and ProfitabilityB1C

*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: 78 out of 100 with 838 signals.

References

1. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
2. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
3. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
4. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
5. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
6. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
7. Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
Frequently Asked QuestionsQ: What is the prediction methodology for MEDS stock?
A: MEDS stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Ridge Regression
Q: Is MEDS stock a buy or sell?
A: The dominant strategy among neural network is to Hold MEDS Stock.
Q: Is TRxADE HEALTH Inc. Common Stock stock a good investment?
A: The consensus rating for TRxADE HEALTH Inc. Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of MEDS stock?
A: The consensus rating for MEDS is Hold.
Q: What is the prediction period for MEDS stock?
A: The prediction period for MEDS is 1 Year