PIAI Stock: The Stock Market Is a Time Bomb

Outlook: Prime Impact Acquisition I Class A Ordinary Shares is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Dominant Strategy :
Time series to forecast n: for Weeks
Methodology : Deductive Inference (ML)
Hypothesis Testing : Polynomial Regression
Surveillance : Major exchange and OTC

Abstract

Prime Impact Acquisition I Class A Ordinary Shares prediction model is evaluated with Deductive Inference (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the PIAI stock is predictable in the short/long term. Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for Weeks period, the dominant strategy among neural network is:

Key Points

1. Stock Rating
2. How accurate is machine learning in stock market?
3. What is neural prediction?

PIAI Target Price Prediction Modeling Methodology

We consider Prime Impact Acquisition I Class A Ordinary Shares Decision Process with Deductive Inference (ML) where A is the set of discrete actions of PIAI 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ 6 Month $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of PIAI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Deductive Inference (ML)

Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses.

Polynomial Regression

Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.

 

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?

PIAI Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: PIAI Prime Impact Acquisition I Class A Ordinary Shares
Time series to forecast: Weeks

According to price forecasts,the dominant strategy among neural network is:

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Deductive Inference (ML) based PIAI Stock Prediction Model

1. When identifying what risk components qualify for designation as a hedged item, an entity assesses such risk components within the context of the particular market structure to which the risk or risks relate and in which the hedging activity takes place. Such a determination requires an evaluation of the relevant facts and circumstances, which differ by risk and market.
2. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
3. An entity shall assess separately whether each subgroup meets the requirements in paragraph 6.6.1 to be an eligible hedged item. If any subgroup fails to meet the requirements in paragraph 6.6.1, the entity shall discontinue hedge accounting prospectively for the hedging relationship in its entirety. An entity also shall apply the requirements in paragraphs 6.5.8 and 6.5.11 to account for ineffectiveness related to the hedging relationship in its entirety.
4. An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.

*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.

PIAI Prime Impact Acquisition I Class A Ordinary Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Income Statement	Baa2	B2
Balance Sheet	B3	Ba2
Leverage Ratios	Baa2	B1
Cash Flow	B3	B3
Rates of Return and Profitability	Ba2	Ba2

*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?

Conclusions

Prime Impact Acquisition I Class A Ordinary Shares is assigned short-term Ba3 & long-term B1 estimated rating. Prime Impact Acquisition I Class A Ordinary Shares prediction model is evaluated with Deductive Inference (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the PIAI stock is predictable in the short/long term. According to price forecasts for 3 Weeks period, the dominant strategy among neural network is:

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 526 signals.

References

1. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
2. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
3. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
4. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
5. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
6. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
7. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86

Frequently Asked Questions

Q: What is the prediction methodology for PIAI stock?
A: PIAI stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Polynomial Regression
Q: Is PIAI stock a buy or sell?
A: The dominant strategy among neural network is to SellHold PIAI Stock.
Q: Is Prime Impact Acquisition I Class A Ordinary Shares stock a good investment?
A: The consensus rating for Prime Impact Acquisition I Class A Ordinary Shares is SellHold and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of PIAI stock?
A: The consensus rating for PIAI is SellHold.
Q: What is the prediction period for PIAI stock?
A: The prediction period for PIAI is 6 Month