Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. We evaluate Metlife prediction models with Transfer Learning (ML) and Polynomial Regression1,2,3,4 and conclude that the MET stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy MET stock.

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

## Key Points

1. How do you decide buy or sell a stock?
2. How do you know when a stock will go up or down?
3. Stock Rating

## MET Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider Metlife Stock Decision Process with Polynomial Regression where A is the set of discrete actions of MET 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(Polynomial 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(Transfer Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of MET 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?

## MET Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: MET Metlife
Time series to forecast n: 14 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy MET 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%

## Conclusions

Metlife assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Polynomial Regression1,2,3,4 and conclude that the MET stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy MET stock.

### Financial State Forecast for MET Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 4163
Market Risk7043
Technical Analysis5545
Fundamental Analysis7873
Risk Unsystematic5253

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 711 signals.

## References

1. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
2. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
3. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
4. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
5. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
6. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
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 MET stock?
A: MET stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Polynomial Regression
Q: Is MET stock a buy or sell?
A: The dominant strategy among neural network is to Buy MET Stock.
Q: Is Metlife stock a good investment?
A: The consensus rating for Metlife is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of MET stock?
A: The consensus rating for MET is Buy.
Q: What is the prediction period for MET stock?
A: The prediction period for MET is (n+1 year)