Outlook: RICHMOND VANADIUM TECHNOLOGY LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 23 Mar 2023 for (n+1 year)
Methodology : Modular Neural Network (Market Direction Analysis)

## Abstract

RICHMOND VANADIUM TECHNOLOGY LIMITED prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the RVT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

## Key Points

1. Can machine learning predict?
2. Decision Making
3. How do you know when a stock will go up or down?

## RVT Target Price Prediction Modeling Methodology

We consider RICHMOND VANADIUM TECHNOLOGY LIMITED Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of RVT 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(Polynomial 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 (Market Direction Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of RVT 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?

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

Sample Set: Neural Network
Stock/Index: RVT RICHMOND VANADIUM TECHNOLOGY LIMITED
Time series to forecast n: 23 Mar 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

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%

1. If a collar, in the form of a purchased call and written put, prevents a transferred asset from being derecognised and the entity measures the asset at fair value, it continues to measure the asset at fair value. The associated liability is measured at (i) the sum of the call exercise price and fair value of the put option less the time value of the call option, if the call option is in or at the money, or (ii) the sum of the fair value of the asset and the fair value of the put option less the time value of the call option if the call option is out of the money. The adjustment to the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the options held and written by the entity. For example, assume an entity transfers a financial asset that is measured at fair value while simultaneously purchasing a call with an exercise price of CU120 and writing a put with an exercise price of CU80. Assume also that the fair value of the asset is CU100 at the date of the transfer. The time value of the put and call are CU1 and CU5 respectively. In this case, the entity recognises an asset of CU100 (the fair value of the asset) and a liability of CU96 [(CU100 + CU1) – CU5]. This gives a net asset value of CU4, which is the fair value of the options held and written by the entity.
2. An example of a fair value hedge is a hedge of exposure to changes in the fair value of a fixed-rate debt instrument arising from changes in interest rates. Such a hedge could be entered into by the issuer or by the holder.
3. At the date of initial application, an entity is permitted to make the designation in paragraph 2.5 for contracts that already exist on the date but only if it designates all similar contracts. The change in the net assets resulting from such designations shall be recognised in retained earnings at the date of initial application.
4. The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

## Conclusions

RICHMOND VANADIUM TECHNOLOGY LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. RICHMOND VANADIUM TECHNOLOGY LIMITED prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the RVT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

### RVT RICHMOND VANADIUM TECHNOLOGY LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Caa2
Balance SheetB3C
Leverage RatiosBa3B2
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityB2Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

### Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 544 signals. ## References

1. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
2. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
3. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
4. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
5. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
6. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
7. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
Frequently Asked QuestionsQ: What is the prediction methodology for RVT stock?
A: RVT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Polynomial Regression
Q: Is RVT stock a buy or sell?
A: The dominant strategy among neural network is to Buy RVT Stock.
Q: Is RICHMOND VANADIUM TECHNOLOGY LIMITED stock a good investment?
A: The consensus rating for RICHMOND VANADIUM TECHNOLOGY LIMITED is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of RVT stock?
A: The consensus rating for RVT is Buy.
Q: What is the prediction period for RVT stock?
A: The prediction period for RVT is (n+1 year)