Outlook: CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 is assigned short-term B1 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Hypothesis Testing : Wilcoxon Sign-Rank Test
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

CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 prediction model is evaluated with Multi-Task Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the CNO^A 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 4 Weeks period, the dominant strategy among neural network is: Hold

## Key Points

1. What is a prediction confidence?
2. What is the best way to predict stock prices?
3. Buy, Sell and Hold Signals

## CNO^A Target Price Prediction Modeling Methodology

We consider CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of CNO^A 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(Wilcoxon Sign-Rank 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-Task Learning (ML)) X S(n):→ 4 Weeks $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of CNO^A 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.

### Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

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?

## CNO^A Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: CNO^A CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060
Time series to forecast: 4 Weeks

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

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 CNO^A Stock Prediction Model

1. The characteristics of the hedged item, including how and when the hedged item affects profit or loss, also affect the period over which the forward element of a forward contract that hedges a time-period related hedged item is amortised, which is over the period to which the forward element relates. For example, if a forward contract hedges the exposure to variability in threemonth interest rates for a three-month period that starts in six months' time, the forward element is amortised during the period that spans months seven to nine.
2. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
3. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
4. A layer component that includes a prepayment option is not eligible to be designated as a hedged item in a fair value hedge if the prepayment option's fair value is affected by changes in the hedged risk, unless the designated layer includes the effect of the related prepayment option when determining the change in the fair value of 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.

### CNO^A CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1Baa2
Income StatementCaa2Baa2
Balance SheetBaa2Baa2
Leverage RatiosBa1Baa2
Cash FlowBa1B2
Rates of Return and ProfitabilityCaa2Ba1

*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

CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 is assigned short-term B1 & long-term Baa2 estimated rating. CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 prediction model is evaluated with Multi-Task Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the CNO^A stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 707 signals.

## References

1. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
2. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
3. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
4. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
5. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
6. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
7. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
Frequently Asked QuestionsQ: What is the prediction methodology for CNO^A stock?
A: CNO^A stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is CNO^A stock a buy or sell?
A: The dominant strategy among neural network is to Hold CNO^A Stock.
Q: Is CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 stock a good investment?
A: The consensus rating for CNO Financial Group Inc. 5.125% Subordinated Debentures due 2060 is Hold and is assigned short-term B1 & long-term Baa2 estimated rating.
Q: What is the consensus rating of CNO^A stock?
A: The consensus rating for CNO^A is Hold.
Q: What is the prediction period for CNO^A stock?
A: The prediction period for CNO^A is 4 Weeks