QSI Stock Forecast: A Sell For The Next 16 Weeks

Outlook: Quantum-Si Incorporated Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 17 Jun 2023 for 16 Weeks
Methodology : Multi-Instance Learning (ML)

Abstract

Quantum-Si Incorporated Class A Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the QSI stock is predictable in the short/long term. Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. What are main components of Markov decision process?
2. Trading Interaction
3. Dominated Move

QSI Target Price Prediction Modeling Methodology

We consider Quantum-Si Incorporated Class A Common Stock Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of QSI 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(Wilcoxon Sign-Rank Test)^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(Multi-Instance Learning (ML)) X S(n):→ 16 Weeks $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of QSI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Instance Learning (ML)

Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance.

Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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?

QSI Stock Forecast (Buy or Sell) for 16 Weeks

Sample Set: Neural Network
Stock/Index: QSI Quantum-Si Incorporated Class A Common Stock
Time series to forecast n: 17 Jun 2023 for 16 Weeks

According to price forecasts for 16 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 Quantum-Si Incorporated Class A Common Stock

1. One of the defining characteristics of a derivative is that it has an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. An option contract meets that definition because the premium is less than the investment that would be required to obtain the underlying financial instrument to which the option is linked. A currency swap that requires an initial exchange of different currencies of equal fair values meets the definition because it has a zero initial net investment.
2. There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.
3. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
4. Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.

*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

Quantum-Si Incorporated Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Quantum-Si Incorporated Class A Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the QSI stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell

QSI Quantum-Si Incorporated Class A Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	Caa2
Balance Sheet	Baa2	B1
Leverage Ratios	Baa2	Baa2
Cash Flow	C	B1
Rates of Return and Profitability	Ba3	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

Trust metric by Neural Network: 79 out of 100 with 498 signals.

References

1. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
2. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
3. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
4. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
5. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
6. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
7. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791

Frequently Asked Questions

Q: What is the prediction methodology for QSI stock?
A: QSI stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is QSI stock a buy or sell?
A: The dominant strategy among neural network is to Sell QSI Stock.
Q: Is Quantum-Si Incorporated Class A Common Stock stock a good investment?
A: The consensus rating for Quantum-Si Incorporated Class A Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of QSI stock?
A: The consensus rating for QSI is Sell.
Q: What is the prediction period for QSI stock?
A: The prediction period for QSI is 16 Weeks