Where Will INDI Stock Be in 3 Month?

Outlook: indie Semiconductor Inc. Class A Common Stock is assigned short-term Ba3 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (CNN Layer)
Hypothesis Testing : Independent T-Test
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Abstract

indie Semiconductor Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Independent T-Test^1,2,3,4 and it is concluded that the INDI stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

Key Points

1. Can stock prices be predicted?
2. Investment Risk
3. Technical Analysis with Algorithmic Trading

INDI Target Price Prediction Modeling Methodology

We consider indie Semiconductor Inc. Class A Common Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of INDI 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(Independent T-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(Modular Neural Network (CNN Layer)) X S(n):→ 3 Month $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of INDI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (CNN Layer)

CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.

Independent T-Test

An independent t-test is a statistical test that compares the means of two independent samples. In an independent t-test, the data points in each sample are not related to each other. The independent t-test is a parametric test, which means that it assumes that the data is normally distributed. The independent t-test is also a two-sample test, which means that it compares the means of two independent samples.

 

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?

INDI Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: INDI indie Semiconductor Inc. Class A Common Stock
Time series to forecast: 3 Month

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

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 Modular Neural Network (CNN Layer) based INDI Stock Prediction Model

1. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
2. Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.
3. When an entity separates the foreign currency basis spread from a financial instrument and excludes it from the designation of that financial instrument as the hedging instrument (see paragraph 6.2.4(b)), the application guidance in paragraphs B6.5.34–B6.5.38 applies to the foreign currency basis spread in the same manner as it is applied to the forward element of a forward contract.
4. Financial assets that are held within a business model whose objective is to hold assets in order to collect contractual cash flows are managed to realise cash flows by collecting contractual payments over the life of the instrument. That is, the entity manages the assets held within the portfolio to collect those particular contractual cash flows (instead of managing the overall return on the portfolio by both holding and selling assets). In determining whether cash flows are going to be realised by collecting the financial assets' contractual cash flows, it is necessary to consider the frequency, value and timing of sales in prior periods, the reasons for those sales and expectations about future sales activity. However sales in themselves do not determine the business model and therefore cannot be considered in isolation. Instead, information about past sales and expectations about future sales provide evidence related to how the entity's stated objective for managing the financial assets is achieved and, specifically, how cash flows are realised. An entity must consider information about past sales within the context of the reasons for those sales and the conditions that existed at that time as compared to current conditions.

*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.

INDI indie Semiconductor Inc. Class A Common Stock Financial Analysis*

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

*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

indie Semiconductor Inc. Class A Common Stock is assigned short-term Ba3 & long-term Baa2 estimated rating. indie Semiconductor Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Independent T-Test^1,2,3,4 and it is concluded that the INDI stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 882 signals.

References

1. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
2. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
3. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
4. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
5. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
6. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
7. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.

Frequently Asked Questions

Q: What is the prediction methodology for INDI stock?
A: INDI stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Independent T-Test
Q: Is INDI stock a buy or sell?
A: The dominant strategy among neural network is to Buy INDI Stock.
Q: Is indie Semiconductor Inc. Class A Common Stock stock a good investment?
A: The consensus rating for indie Semiconductor Inc. Class A Common Stock is Buy and is assigned short-term Ba3 & long-term Baa2 estimated rating.
Q: What is the consensus rating of INDI stock?
A: The consensus rating for INDI is Buy.
Q: What is the prediction period for INDI stock?
A: The prediction period for INDI is 3 Month

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