Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. We evaluate Unum prediction models with Modular Neural Network (Market Direction Analysis) and Pearson Correlation1,2,3,4 and conclude that the UNM stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell UNM stock.

Keywords: UNM, Unum, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

2. Short/Long Term Stocks
3. Stock Rating

## UNM Target Price Prediction Modeling Methodology

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. We consider Unum Stock Decision Process with Pearson Correlation where A is the set of discrete actions of UNM 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(Pearson 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 (Market Direction Analysis)) X S(n):→ (n+6 month) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of UNM stock

j:Nash equilibria

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?

## UNM Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: UNM Unum
Time series to forecast n: 31 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell UNM stock.

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 (Yellow to Green): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for Unum

1. A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
2. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
3. 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.
4. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

Unum assigned short-term Caa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Pearson Correlation1,2,3,4 and conclude that the UNM stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell UNM stock.

### Financial State Forecast for UNM Unum Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2B1
Operational Risk 4277
Market Risk3042
Technical Analysis5263
Fundamental Analysis4134
Risk Unsystematic6062

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 744 signals.

## References

1. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
2. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
3. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
4. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
5. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
6. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
7. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
Frequently Asked QuestionsQ: What is the prediction methodology for UNM stock?
A: UNM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Pearson Correlation
Q: Is UNM stock a buy or sell?
A: The dominant strategy among neural network is to Sell UNM Stock.
Q: Is Unum stock a good investment?
A: The consensus rating for Unum is Sell and assigned short-term Caa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of UNM stock?
A: The consensus rating for UNM is Sell.
Q: What is the prediction period for UNM stock?
A: The prediction period for UNM is (n+6 month)