Outlook: LENSAR Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 23 Feb 2023 for (n+3 month)
Methodology : Inductive Learning (ML)

## Abstract

LENSAR Inc. Common Stock prediction model is evaluated with Inductive Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the LNSR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

## Key Points

1. Probability Distribution
2. How accurate is machine learning in stock market?
3. Operational Risk

## LNSR Target Price Prediction Modeling Methodology

We consider LENSAR Inc. Common Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of LNSR 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(Logistic 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(Inductive Learning (ML)) X S(n):→ (n+3 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LNSR 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?

## LNSR Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LNSR LENSAR Inc. Common Stock
Time series to forecast n: 23 Feb 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

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 LENSAR Inc. Common Stock

1. When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
2. In almost every lending transaction the creditor's instrument is ranked relative to the instruments of the debtor's other creditors. An instrument that is subordinated to other instruments may have contractual cash flows that are payments of principal and interest on the principal amount outstanding if the debtor's non-payment is a breach of contract and the holder has a contractual right to unpaid amounts of principal and interest on the principal amount outstanding even in the event of the debtor's bankruptcy. For example, a trade receivable that ranks its creditor as a general creditor would qualify as having payments of principal and interest on the principal amount outstanding. This is the case even if the debtor issued loans that are collateralised, which in the event of bankruptcy would give that loan holder priority over the claims of the general creditor in respect of the collateral but does not affect the contractual right of the general creditor to unpaid principal and other amounts due.
3. Paragraphs 6.9.7–6.9.13 provide exceptions to the requirements specified in those paragraphs only. An entity shall apply all other hedge accounting requirements in this Standard, including the qualifying criteria in paragraph 6.4.1, to hedging relationships that were directly affected by interest rate benchmark reform.
4. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.

*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

LENSAR Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. LENSAR Inc. Common Stock prediction model is evaluated with Inductive Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the LNSR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

### LNSR LENSAR Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa1Baa2
Balance SheetBaa2Baa2
Leverage RatiosBa1Baa2
Cash FlowBa1B1
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: 91 out of 100 with 614 signals.

## References

1. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
2. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
3. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
4. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
6. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
7. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
Frequently Asked QuestionsQ: What is the prediction methodology for LNSR stock?
A: LNSR stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Logistic Regression
Q: Is LNSR stock a buy or sell?
A: The dominant strategy among neural network is to Buy LNSR Stock.
Q: Is LENSAR Inc. Common Stock stock a good investment?
A: The consensus rating for LENSAR Inc. Common Stock is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LNSR stock?
A: The consensus rating for LNSR is Buy.
Q: What is the prediction period for LNSR stock?
A: The prediction period for LNSR is (n+3 month)