The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth.** We evaluate Kinross Gold Corporation prediction models with Supervised Machine Learning (ML) and Stepwise Regression ^{1,2,3,4} and conclude that the K 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 Hold K stock.**

**K, Kinross Gold Corporation, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- How accurate is machine learning in stock market?
- Dominated Move
- What are buy sell or hold recommendations?

## K Target Price Prediction Modeling Methodology

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. We consider Kinross Gold Corporation Stock Decision Process with Stepwise Regression where A is the set of discrete actions of K 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of K 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?

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

**Sample Set:**Neural Network

**Stock/Index:**K Kinross Gold Corporation

**Time series to forecast n: 17 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold K 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

Kinross Gold Corporation assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Stepwise Regression ^{1,2,3,4} and conclude that the K 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 Hold K stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | Ba3 |

Operational Risk | 83 | 86 |

Market Risk | 90 | 64 |

Technical Analysis | 52 | 39 |

Fundamental Analysis | 35 | 43 |

Risk Unsystematic | 65 | 88 |

### Prediction Confidence Score

## References

- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.

## Frequently Asked Questions

Q: What is the prediction methodology for K stock?A: K stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Stepwise Regression

Q: Is K stock a buy or sell?

A: The dominant strategy among neural network is to Hold K Stock.

Q: Is Kinross Gold Corporation stock a good investment?

A: The consensus rating for Kinross Gold Corporation is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of K stock?

A: The consensus rating for K is Hold.

Q: What is the prediction period for K stock?

A: The prediction period for K is (n+6 month)