Outlook: Barrick Gold Corporation assigned short-term Ba2 & long-term Ba1 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 17 Dec 2022 for (n+6 month)
Methodology : Active Learning (ML)

## Abstract

Stock market or Share market is one of the most complicated and sophisticated way to do business. Small ownerships, brokerage corporations, banking sector, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithm to predict the future stock price for exchange by using open source libraries and preexisting algorithms to help make this unpredictable format of business a little more predictable.(Ravikumar, S. and Saraf, P., 2020, June. Prediction of stock prices using machine learning (regression, classification) Algorithms. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE.) We evaluate Barrick Gold Corporation prediction models with Active Learning (ML) and Stepwise Regression1,2,3,4 and conclude that the ABX:TSX stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

## Key Points

1. Can stock prices be predicted?
2. Why do we need predictive models?
3. Probability Distribution

## ABX:TSX Target Price Prediction Modeling Methodology

We consider Barrick Gold Corporation Decision Process with Active Learning (ML) where A is the set of discrete actions of ABX:TSX 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(Active Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ABX:TSX stock

j:Nash equilibria (Neural Network)

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?

## ABX:TSX Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: ABX:TSX Barrick Gold Corporation
Time series to forecast n: 17 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

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 (Grey to Black): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for Barrick Gold Corporation

1. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.
2. Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
3. The credit risk on a financial instrument is considered low for the purposes of paragraph 5.5.10, if the financial instrument has a low risk of default, the borrower has a strong capacity to meet its contractual cash flow obligations in the near term and adverse changes in economic and business conditions in the longer term may, but will not necessarily, reduce the ability of the borrower to fulfil its contractual cash flow obligations. Financial instruments are not considered to have low credit risk when they are regarded as having a low risk of loss simply because of the value of collateral and the financial instrument without that collateral would not be considered low credit risk. Financial instruments are also not considered to have low credit risk simply because they have a lower risk of default than the entity's other financial instruments or relative to the credit risk of the jurisdiction within which an entity operates.
4. For the purpose of applying paragraph 6.5.11, at the point when an entity amends the description of a hedged item as required in paragraph 6.9.1(b), the amount accumulated in the cash flow hedge reserve shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows are determined.

*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

Barrick Gold Corporation assigned short-term Ba2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Stepwise Regression1,2,3,4 and conclude that the ABX:TSX stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

### Financial State Forecast for ABX:TSX Barrick Gold Corporation Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba1
Operational Risk 7579
Market Risk4330
Technical Analysis9073
Fundamental Analysis7786
Risk Unsystematic5980

### Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 572 signals.

## References

1. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
2. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
3. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
4. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
5. 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
6. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
7. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for ABX:TSX stock?
A: ABX:TSX stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Stepwise Regression
Q: Is ABX:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell ABX:TSX Stock.
Q: Is Barrick Gold Corporation stock a good investment?
A: The consensus rating for Barrick Gold Corporation is Sell and assigned short-term Ba2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of ABX:TSX stock?
A: The consensus rating for ABX:TSX is Sell.
Q: What is the prediction period for ABX:TSX stock?
A: The prediction period for ABX:TSX is (n+6 month)