**Outlook:**VOLTA FINANCE LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 24 Jan 2023**for (n+4 weeks)

**Methodology :**Modular Neural Network (DNN Layer)

## Abstract

VOLTA FINANCE LIMITED prediction model is evaluated with Modular Neural Network (DNN Layer) and Polynomial Regression^{1,2,3,4}and it is concluded that the LON:VTA stock is predictable in the short/long term.

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- Stock Forecast Based On a Predictive Algorithm
- What is a prediction confidence?
- What statistical methods are used to analyze data?

## LON:VTA Target Price Prediction Modeling Methodology

We consider VOLTA FINANCE LIMITED Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of LON:VTA 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:VTA 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?

## LON:VTA Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:VTA VOLTA FINANCE LIMITED

**Time series to forecast n: 24 Jan 2023**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) 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%**

## IFRS Reconciliation Adjustments for VOLTA FINANCE LIMITED

- If an entity originates a loan that bears an off-market interest rate (eg 5 per cent when the market rate for similar loans is 8 per cent), and receives an upfront fee as compensation, the entity recognises the loan at its fair value, ie net of the fee it receives.
- To be eligible for designation as a hedged item, a risk component must be a separately identifiable component of the financial or the non-financial item, and the changes in the cash flows or the fair value of the item attributable to changes in that risk component must be reliably measurable.
- If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.
- Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.

*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

VOLTA FINANCE LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. VOLTA FINANCE LIMITED prediction model is evaluated with Modular Neural Network (DNN Layer) and Polynomial Regression^{1,2,3,4} and it is concluded that the LON:VTA stock is predictable in the short/long term. ** According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

### LON:VTA VOLTA FINANCE LIMITED Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | B1 | B3 |

Balance Sheet | Ba3 | Caa2 |

Leverage Ratios | Baa2 | Baa2 |

Cash Flow | Baa2 | C |

Rates of Return and Profitability | B2 | Baa2 |

*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

## References

- S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016

## Frequently Asked Questions

Q: What is the prediction methodology for LON:VTA stock?A: LON:VTA stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Polynomial Regression

Q: Is LON:VTA stock a buy or sell?

A: The dominant strategy among neural network is to Sell LON:VTA Stock.

Q: Is VOLTA FINANCE LIMITED stock a good investment?

A: The consensus rating for VOLTA FINANCE LIMITED is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of LON:VTA stock?

A: The consensus rating for LON:VTA is Sell.

Q: What is the prediction period for LON:VTA stock?

A: The prediction period for LON:VTA is (n+4 weeks)