**Outlook:**DILA Capital Acquisition Corp. Warrant assigned short-term B3 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 10 Dec 2022**for (n+16 weeks)

**Methodology :**Inductive Learning (ML)

## Abstract

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators.(Kanade, P.A., Singh, S., Rajoria, S., Veer, P. and Wandile, N., 2020. Machine learning model for stock market prediction. International Journal for Research in Applied Science and Engineering Technology, 8(6), pp.209-216.)** We evaluate DILA Capital Acquisition Corp. Warrant prediction models with Inductive Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the DILAW stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold**

## Key Points

- Market Risk
- Market Outlook
- Reaction Function

## DILAW Target Price Prediction Modeling Methodology

We consider DILA Capital Acquisition Corp. Warrant Decision Process with Inductive Learning (ML) where A is the set of discrete actions of DILAW 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(Lasso 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(Inductive Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of DILAW 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?

## DILAW Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**DILAW DILA Capital Acquisition Corp. Warrant

**Time series to forecast n: 10 Dec 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold**

**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 DILA Capital Acquisition Corp. Warrant

- The definition of a derivative refers to non-financial variables that are not specific to a party to the contract. These include an index of earthquake losses in a particular region and an index of temperatures in a particular city. Non-financial variables specific to a party to the contract include the occurrence or non-occurrence of a fire that damages or destroys an asset of a party to the contract. A change in the fair value of a non-financial asset is specific to the owner if the fair value reflects not only changes in market prices for such assets (a financial variable) but also the condition of the specific non-financial asset held (a non-financial variable). For example, if a guarantee of the residual value of a specific car exposes the guarantor to the risk of changes in the car's physical condition, the change in that residual value is specific to the owner of the car.
- If a call option right retained by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the asset continues to be measured at its fair value. The associated liability is measured at (i) the option exercise price less the time value of the option if the option is in or at the money, or (ii) the fair value of the transferred asset less the time value of the option if the option is out of the money. The adjustment to the measurement of the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the call option right. For example, if the fair value of the underlying asset is CU80, the option exercise price is CU95 and the time value of the option is CU5, the carrying amount of the associated liability is CU75 (CU80 – CU5) and the carrying amount of the transferred asset is CU80 (ie its fair value)
- To the extent that a transfer of a financial asset does not qualify for derecognition, the transferee does not recognise the transferred asset as its asset. The transferee derecognises the cash or other consideration paid and recognises a receivable from the transferor. If the transferor has both a right and an obligation to reacquire control of the entire transferred asset for a fixed amount (such as under a repurchase agreement), the transferee may measure its receivable at amortised cost if it meets the criteria in paragraph 4.1.2.
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*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

DILA Capital Acquisition Corp. Warrant assigned short-term B3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Inductive Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the DILAW stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold**

### Financial State Forecast for DILAW DILA Capital Acquisition Corp. Warrant Options & Futures

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

Outlook* | B3 | B1 |

Operational Risk | 32 | 82 |

Market Risk | 80 | 41 |

Technical Analysis | 46 | 56 |

Fundamental Analysis | 48 | 56 |

Risk Unsystematic | 39 | 53 |

### Prediction Confidence Score

## References

- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
- J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
- 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
- M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982

## Frequently Asked Questions

Q: What is the prediction methodology for DILAW stock?A: DILAW stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Lasso Regression

Q: Is DILAW stock a buy or sell?

A: The dominant strategy among neural network is to Hold DILAW Stock.

Q: Is DILA Capital Acquisition Corp. Warrant stock a good investment?

A: The consensus rating for DILA Capital Acquisition Corp. Warrant is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of DILAW stock?

A: The consensus rating for DILAW is Hold.

Q: What is the prediction period for DILAW stock?

A: The prediction period for DILAW is (n+16 weeks)

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