Cleveland-Cliffs assigned short-term B1 & long-term Ba1 forecasted stock rating.

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. We evaluate Cleveland-Cliffs prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Linear Regression^1,2,3,4 and conclude that the CLF 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 Sell CLF stock.

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

Key Points

Probability Distribution
Market Outlook
Understanding Buy, Sell, and Hold Ratings

CLF Target Price Prediction Modeling Methodology

Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We consider Cleveland-Cliffs Stock Decision Process with Linear Regression where A is the set of discrete actions of CLF 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(Linear Regression)^5,6,7=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+1 year)

\sum_{i = 1}^{n} (a_{i})

n:Time series to forecast

p:Price signals of CLF 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?

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

Sample Set: Neural Network
Stock/Index: CLF Cleveland-Cliffs
Time series to forecast n: 13 Nov 2022 for (n+1 year)

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

Adjusted IFRS* Prediction Methods for Cleveland-Cliffs

The decision of an entity to designate a financial asset or financial liability as at fair value through profit or loss is similar to an accounting policy choice (although, unlike an accounting policy choice, it is not required to be applied consistently to all similar transactions). When an entity has such a choice, paragraph 14(b) of IAS 8 requires the chosen policy to result in the financial statements providing reliable and more relevant information about the effects of transactions, other events and conditions on the entity's financial position, financial performance or cash flows. For example, in the case of designation of a financial liability as at fair value through profit or loss, paragraph 4.2.2 sets out the two circumstances when the requirement for more relevant information will be met. Accordingly, to choose such designation in accordance with paragraph 4.2.2, the entity needs to demonstrate that it falls within one (or both) of these two circumstances.
An entity that first applies these amendments at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.31–7.2.34.
If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.

*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

Cleveland-Cliffs assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Linear Regression^1,2,3,4 and conclude that the CLF 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 Sell CLF stock.

Financial State Forecast for CLF Cleveland-Cliffs Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba1
Operational Risk	77	90
Market Risk	41	81
Technical Analysis	30	71
Fundamental Analysis	88	68
Risk Unsystematic	59	47

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 815 signals.

References

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Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
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Frequently Asked Questions

Q: What is the prediction methodology for CLF stock?
A: CLF stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Linear Regression
Q: Is CLF stock a buy or sell?
A: The dominant strategy among neural network is to Sell CLF Stock.
Q: Is Cleveland-Cliffs stock a good investment?
A: The consensus rating for Cleveland-Cliffs is Sell and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of CLF stock?
A: The consensus rating for CLF is Sell.
Q: What is the prediction period for CLF stock?
A: The prediction period for CLF is (n+1 year)