Outlook: Phreesia Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 29 Apr 2023 for (n+1 year)
Methodology : Multi-Instance Learning (ML)

## Abstract

Phreesia Inc. Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Sign Test1,2,3,4 and it is concluded that the PHR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

## Key Points

1. Why do we need predictive models?
2. What are the most successful trading algorithms?
3. What is a prediction confidence?

## PHR Target Price Prediction Modeling Methodology

We consider Phreesia Inc. Common Stock Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of PHR 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(Sign Test)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(Multi-Instance Learning (ML)) X S(n):→ (n+1 year) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of PHR 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?

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

Sample Set: Neural Network
Stock/Index: PHR Phreesia Inc. Common Stock
Time series to forecast n: 29 Apr 2023 for (n+1 year)

According to price forecasts for (n+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%

## IFRS Reconciliation Adjustments for Phreesia Inc. Common Stock

1. The accounting for the forward element of forward contracts in accordance with paragraph 6.5.16 applies only to the extent that the forward element relates to the hedged item (aligned forward element). The forward element of a forward contract relates to the hedged item if the critical terms of the forward contract (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the forward contract and the hedged item are not fully aligned, an entity shall determine the aligned forward element, ie how much of the forward element included in the forward contract (actual forward element) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.16). An entity determines the aligned forward element using the valuation of the forward contract that would have critical terms that perfectly match the hedged item.
2. A firm commitment to acquire a business in a business combination cannot be a hedged item, except for foreign currency risk, because the other risks being hedged cannot be specifically identified and measured. Those other risks are general business risks.
3. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
4. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.

*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

Phreesia Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Phreesia Inc. Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Sign Test1,2,3,4 and it is concluded that the PHR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

### PHR Phreesia Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB3Baa2
Balance SheetBaa2Caa2
Leverage RatiosBaa2Baa2
Cash FlowBa3Baa2
Rates of Return and ProfitabilityBaa2Baa2

*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: 82 out of 100 with 696 signals.

## References

1. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
2. ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. How is the price of gold determined? (No. Stock Analysis). AC Investment Research.
3. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
4. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
5. 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.
6. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
7. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
Frequently Asked QuestionsQ: What is the prediction methodology for PHR stock?
A: PHR stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Sign Test
Q: Is PHR stock a buy or sell?
A: The dominant strategy among neural network is to Hold PHR Stock.
Q: Is Phreesia Inc. Common Stock stock a good investment?
A: The consensus rating for Phreesia Inc. Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of PHR stock?
A: The consensus rating for PHR is Hold.
Q: What is the prediction period for PHR stock?
A: The prediction period for PHR is (n+1 year)

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