Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed. We evaluate Carrier Global prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the CARR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell CARR stock.

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

## Key Points

1. What are buy sell or hold recommendations?
2. Can we predict stock market using machine learning?
3. What statistical methods are used to analyze data?

## CARR Target Price Prediction Modeling Methodology

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We consider Carrier Global Stock Decision Process with Stepwise Regression where A is the set of discrete actions of CARR 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(Stepwise 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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+16 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of CARR 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?

## CARR Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: CARR Carrier Global
Time series to forecast n: 11 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell CARR 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

Carrier Global assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the CARR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell CARR stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B2
Operational Risk 3939
Market Risk4944
Technical Analysis5935
Fundamental Analysis5559
Risk Unsystematic5684

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 774 signals.

## References

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2. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
3. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
4. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
5. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
6. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
7. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
Frequently Asked QuestionsQ: What is the prediction methodology for CARR stock?
A: CARR stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression
Q: Is CARR stock a buy or sell?
A: The dominant strategy among neural network is to Sell CARR Stock.
Q: Is Carrier Global stock a good investment?
A: The consensus rating for Carrier Global is Sell and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of CARR stock?
A: The consensus rating for CARR is Sell.
Q: What is the prediction period for CARR stock?
A: The prediction period for CARR is (n+16 weeks)