Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We evaluate First Quantum Minerals Ltd. prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Statistical Hypothesis Testing1,2,3,4 and conclude that the FM 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 Hold FM stock.

Keywords: FM, First Quantum Minerals Ltd., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Understanding Buy, Sell, and Hold Ratings
2. Stock Rating
3. How accurate is machine learning in stock market?

## FM Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider First Quantum Minerals Ltd. Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of FM 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(Statistical Hypothesis Testing)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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+16 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of FM 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?

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

Sample Set: Neural Network
Stock/Index: FM First Quantum Minerals Ltd.
Time series to forecast n: 09 Sep 2022 for (n+16 weeks)

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

First Quantum Minerals Ltd. assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Statistical Hypothesis Testing1,2,3,4 and conclude that the FM 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 Hold FM stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba3
Operational Risk 8649
Market Risk7444
Technical Analysis3569
Fundamental Analysis8885
Risk Unsystematic6385

### Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 692 signals.

## References

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2. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
3. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
4. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
5. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
6. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
7. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
Frequently Asked QuestionsQ: What is the prediction methodology for FM stock?
A: FM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Statistical Hypothesis Testing
Q: Is FM stock a buy or sell?
A: The dominant strategy among neural network is to Hold FM Stock.
Q: Is First Quantum Minerals Ltd. stock a good investment?
A: The consensus rating for First Quantum Minerals Ltd. is Hold and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of FM stock?
A: The consensus rating for FM is Hold.
Q: What is the prediction period for FM stock?
A: The prediction period for FM is (n+16 weeks)