Outlook: JELD-WEN Holding Inc. Common Stock is assigned short-term B1 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (CNN Layer)
Hypothesis Testing : Spearman Correlation
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.

## Summary

JELD-WEN Holding Inc. Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Spearman Correlation1,2,3,4 and it is concluded that the JELD stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

## Key Points

1. What is a prediction confidence?
2. Fundemental Analysis with Algorithmic Trading
3. What is the best way to predict stock prices?

## JELD Target Price Prediction Modeling Methodology

We consider JELD-WEN Holding Inc. Common Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of JELD 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(Spearman Correlation)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(Modular Neural Network (CNN Layer)) X S(n):→ 8 Weeks $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of JELD stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Modular Neural Network (CNN Layer)

CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.

### Spearman Correlation

Spearman correlation is a nonparametric measure of the strength and direction of association between two variables. It is a rank-based correlation, which means that it does not assume that the data is normally distributed. Spearman correlation is calculated by first ranking the data for each variable, and then calculating the Pearson correlation between the ranks.

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?

## JELD Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: JELD JELD-WEN Holding Inc. Common Stock
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: Buy

Strategic Interaction Table Legend:

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%

### Financial Data Adjustments for Modular Neural Network (CNN Layer) based JELD Stock Prediction Model

1. If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
2. The requirements in paragraphs 6.8.4–6.8.8 may cease to apply at different times. Therefore, in applying paragraph 6.9.1, an entity may be required to amend the formal designation of its hedging relationships at different times, or may be required to amend the formal designation of a hedging relationship more than once. When, and only when, such a change is made to the hedge designation, an entity shall apply paragraphs 6.9.7–6.9.12 as applicable. An entity also shall apply paragraph 6.5.8 (for a fair value hedge) or paragraph 6.5.11 (for a cash flow hedge) to account for any changes in the fair value of the hedged item or the hedging instrument.
3. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.
4. For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.

*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.

### JELD JELD-WEN Holding Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1Baa2
Income StatementBa2Baa2
Balance SheetB3Ba3
Leverage RatiosBaa2B3
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityBa3Baa2

*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?

## Conclusions

JELD-WEN Holding Inc. Common Stock is assigned short-term B1 & long-term Baa2 estimated rating. JELD-WEN Holding Inc. Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Spearman Correlation1,2,3,4 and it is concluded that the JELD stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 545 signals.

## References

1. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
2. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
3. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
4. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
5. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
6. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
7. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
Frequently Asked QuestionsQ: What is the prediction methodology for JELD stock?
A: JELD stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Spearman Correlation
Q: Is JELD stock a buy or sell?
A: The dominant strategy among neural network is to Buy JELD Stock.
Q: Is JELD-WEN Holding Inc. Common Stock stock a good investment?
A: The consensus rating for JELD-WEN Holding Inc. Common Stock is Buy and is assigned short-term B1 & long-term Baa2 estimated rating.
Q: What is the consensus rating of JELD stock?
A: The consensus rating for JELD is Buy.
Q: What is the prediction period for JELD stock?
A: The prediction period for JELD is 8 Weeks