**Outlook:**SPI Energy Co. Ltd. Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 25 Apr 2023**for (n+1 year)

**Methodology :**Statistical Inference (ML)

## Abstract

SPI Energy Co. Ltd. Ordinary Shares prediction model is evaluated with Statistical Inference (ML) and Chi-Square^{1,2,3,4}and it is concluded that the SPI stock is predictable in the short/long term.

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

## Key Points

- What are buy sell or hold recommendations?
- How can neural networks improve predictions?
- What is the use of Markov decision process?

## SPI Target Price Prediction Modeling Methodology

We consider SPI Energy Co. Ltd. Ordinary Shares Decision Process with Statistical Inference (ML) where A is the set of discrete actions of SPI 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(Chi-Square)

^{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(Statistical Inference (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of SPI 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?

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

**Sample Set:**Neural Network

**Stock/Index:**SPI SPI Energy Co. Ltd. Ordinary Shares

**Time series to forecast n: 25 Apr 2023**for (n+1 year)

**According to price forecasts for (n+1 year) 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 SPI Energy Co. Ltd. Ordinary Shares

- When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
- Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
- The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
- Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.

*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

SPI Energy Co. Ltd. Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. SPI Energy Co. Ltd. Ordinary Shares prediction model is evaluated with Statistical Inference (ML) and Chi-Square^{1,2,3,4} and it is concluded that the SPI stock is predictable in the short/long term. ** According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

### SPI SPI Energy Co. Ltd. Ordinary Shares Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | B3 | Baa2 |

Balance Sheet | B1 | C |

Leverage Ratios | Baa2 | Caa2 |

Cash Flow | B3 | Baa2 |

Rates of Return and Profitability | Baa2 | C |

*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. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- 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
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.

## Frequently Asked Questions

Q: What is the prediction methodology for SPI stock?A: SPI stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Chi-Square

Q: Is SPI stock a buy or sell?

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

Q: Is SPI Energy Co. Ltd. Ordinary Shares stock a good investment?

A: The consensus rating for SPI Energy Co. Ltd. Ordinary Shares is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of SPI stock?

A: The consensus rating for SPI is Sell.

Q: What is the prediction period for SPI stock?

A: The prediction period for SPI is (n+1 year)