Modelling A.I. in Economics

Jianpu Technology Inc. American depositary shares Forecast & Analysis

Outlook: Jianpu Technology Inc. American depositary shares is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised :
Dominant Strategy : Hold
Time series to forecast n: for 1 Year
Methodology : Modular Neural Network (Social Media Sentiment Analysis)
Hypothesis Testing : Paired T-Test
Surveillance : Major exchange and OTC

Abstract

Jianpu Technology Inc. American depositary shares prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Paired T-Test1,2,3,4 and it is concluded that the JT stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for social media sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of social media sentiment analysis, MNNs can be used to identify the sentiment of social media posts, such as tweets, Facebook posts, and Instagram stories. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

Graph 27

Key Points

  1. Buy, Sell and Hold Signals
  2. Prediction Modeling
  3. Game Theory

JT Target Price Prediction Modeling Methodology

We consider Jianpu Technology Inc. American depositary shares Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of JT 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(Paired T-Test)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ 1 Year e x rx

n:Time series to forecast

p:Price signals of JT stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Social Media Sentiment Analysis)

A modular neural network (MNN) is a type of artificial neural network that can be used for social media sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of social media sentiment analysis, MNNs can be used to identify the sentiment of social media posts, such as tweets, Facebook posts, and Instagram stories. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.

Paired T-Test

A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.

 

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?

JT Stock Forecast (Buy or Sell) for 1 Year

Sample Set: Neural Network
Stock/Index: JT Jianpu Technology Inc. American depositary shares
Time series to forecast: 1 Year

According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

Strategic Interaction Table

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 (Social Media Sentiment Analysis) based JT Prediction Model

  1. However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
  2. As with all fair value measurements, an entity's measurement method for determining the portion of the change in the liability's fair value that is attributable to changes in its credit risk must make maximum use of relevant observable inputs and minimum use of unobservable inputs.
  3. 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.
  4. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.

*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

Jianpu Technology Inc. American depositary shares is assigned short-term Ba3 & long-term B1 estimated rating. Jianpu Technology Inc. American depositary shares prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Paired T-Test1,2,3,4 and it is concluded that the JT stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

JT Jianpu Technology Inc. American depositary shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Income StatementBaa2Baa2
Balance SheetB3Baa2
Leverage RatiosCC
Cash FlowB1B2
Rates of Return and ProfitabilityBaa2Caa2

*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: 72 out of 100 with 454 signals.

References

  1. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  2. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
  3. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
  4. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
  5. 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
  6. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
  7. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
Frequently Asked QuestionsQ: What is the prediction methodology for JT stock?
A: JT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Paired T-Test
Q: Is JT stock a buy or sell?
A: The dominant strategy among neural network is to Hold JT Stock.
Q: Is Jianpu Technology Inc. American depositary shares stock a good investment?
A: The consensus rating for Jianpu Technology Inc. American depositary shares is Hold and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of JT stock?
A: The consensus rating for JT is Hold.
Q: What is the prediction period for JT stock?
A: The prediction period for JT is 1 Year

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