Outlook: PGIM Global High Yield Fund Inc. is assigned short-term B2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Time series to forecast n: for Weeks2
Hypothesis Testing : Logistic Regression
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.

## Abstract

PGIM Global High Yield Fund Inc. prediction model is evaluated with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the GHY stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy ## Key Points

1. Is Target price a good indicator?
2. What is Markov decision process in reinforcement learning?
3. Probability Distribution

## GHY Target Price Prediction Modeling Methodology

We consider PGIM Global High Yield Fund Inc. Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of GHY 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(Multi-Task Learning (ML)) X S(n):→ 6 Month $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of GHY stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

### Logistic Regression

In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.

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?

## GHY Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: GHY PGIM Global High Yield Fund Inc.
Time series to forecast: 6 Month

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 Multi-Task Learning (ML) based GHY Stock Prediction Model

1. The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
2. Measurement of a financial asset or financial liability and classification of recognised changes in its value are determined by the item's classification and whether the item is part of a designated hedging relationship. Those requirements can create a measurement or recognition inconsistency (sometimes referred to as an 'accounting mismatch') when, for example, in the absence of designation as at fair value through profit or loss, a financial asset would be classified as subsequently measured at fair value through profit or loss and a liability the entity considers related would be subsequently measured at amortised cost (with changes in fair value not recognised). In such circumstances, an entity may conclude that its financial statements would provide more relevant information if both the asset and the liability were measured as at fair value through profit or loss.
3. 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.
4. The definition of a derivative refers to non-financial variables that are not specific to a party to the contract. These include an index of earthquake losses in a particular region and an index of temperatures in a particular city. Non-financial variables specific to a party to the contract include the occurrence or non-occurrence of a fire that damages or destroys an asset of a party to the contract. A change in the fair value of a non-financial asset is specific to the owner if the fair value reflects not only changes in market prices for such assets (a financial variable) but also the condition of the specific non-financial asset held (a non-financial variable). For example, if a guarantee of the residual value of a specific car exposes the guarantor to the risk of changes in the car's physical condition, the change in that residual value is specific to the owner of the car.

*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.

### GHY PGIM Global High Yield Fund Inc. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2B1
Income StatementBaa2Caa2
Balance SheetB3B1
Leverage RatiosB1Baa2
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityB2C

*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

PGIM Global High Yield Fund Inc. is assigned short-term B2 & long-term B1 estimated rating. PGIM Global High Yield Fund Inc. prediction model is evaluated with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the GHY stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 498 signals.

## References

1. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
2. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
3. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
4. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
5. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
6. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
7. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
Frequently Asked QuestionsQ: What is the prediction methodology for GHY stock?
A: GHY stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Logistic Regression
Q: Is GHY stock a buy or sell?
A: The dominant strategy among neural network is to Buy GHY Stock.
Q: Is PGIM Global High Yield Fund Inc. stock a good investment?
A: The consensus rating for PGIM Global High Yield Fund Inc. is Buy and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of GHY stock?
A: The consensus rating for GHY is Buy.
Q: What is the prediction period for GHY stock?
A: The prediction period for GHY is 6 Month