Modelling A.I. in Economics

QLI Stock: A Bright Investment or a Bubble Waiting to Burst? (Forecast)

Outlook: Qilian International Holding Group Ltd. Ordinary Shares is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Ensemble Learning (ML)
Hypothesis Testing : Ridge Regression
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.

Summary

Qilian International Holding Group Ltd. Ordinary Shares prediction model is evaluated with Ensemble Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the QLI stock is predictable in the short/long term. Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold

Graph 13

Key Points

  1. How accurate is machine learning in stock market?
  2. How can neural networks improve predictions?
  3. Operational Risk

QLI Target Price Prediction Modeling Methodology

We consider Qilian International Holding Group Ltd. Ordinary Shares Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of QLI 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(Ridge Regression)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(Ensemble Learning (ML)) X S(n):→ 3 Month R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of QLI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Ensemble Learning (ML)

Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average.

Ridge Regression

Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.

 

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?

QLI Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: QLI Qilian International Holding Group Ltd. Ordinary Shares
Time series to forecast: 3 Month

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

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 Ensemble Learning (ML) based QLI Stock Prediction Model

  1. If a component of the cash flows of a financial or a non-financial item is designated as the hedged item, that component must be less than or equal to the total cash flows of the entire item. However, all of the cash flows of the entire item may be designated as the hedged item and hedged for only one particular risk (for example, only for those changes that are attributable to changes in LIBOR or a benchmark commodity price).
  2. An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
  3. When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
  4. An entity may retain the right to a part of the interest payments on transferred assets as compensation for servicing those assets. The part of the interest payments that the entity would give up upon termination or transfer of the servicing contract is allocated to the servicing asset or servicing liability. The part of the interest payments that the entity would not give up is an interest-only strip receivable. For example, if the entity would not give up any interest upon termination or transfer of the servicing contract, the entire interest spread is an interest-only strip receivable. For the purposes of applying paragraph 3.2.13, the fair values of the servicing asset and interest-only strip receivable are used to allocate the carrying amount of the receivable between the part of the asset that is derecognised and the part that continues to be recognised. If there is no servicing fee specified or the fee to be received is not expected to compensate the entity adequately for performing the servicing, a liability for the servicing obligation is recognised at fair value.

*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.

QLI Qilian International Holding Group Ltd. Ordinary Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Income StatementCC
Balance SheetBa3Baa2
Leverage RatiosCBaa2
Cash FlowBaa2Ba3
Rates of Return and ProfitabilityCaa2Ba3

*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

Qilian International Holding Group Ltd. Ordinary Shares is assigned short-term B2 & long-term Ba3 estimated rating. Qilian International Holding Group Ltd. Ordinary Shares prediction model is evaluated with Ensemble Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the QLI stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 663 signals.

References

  1. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
  2. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
  3. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
  4. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
  5. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
  6. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
  7. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
Frequently Asked QuestionsQ: What is the prediction methodology for QLI stock?
A: QLI stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Ridge Regression
Q: Is QLI stock a buy or sell?
A: The dominant strategy among neural network is to Hold QLI Stock.
Q: Is Qilian International Holding Group Ltd. Ordinary Shares stock a good investment?
A: The consensus rating for Qilian International Holding Group Ltd. Ordinary Shares is Hold and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of QLI stock?
A: The consensus rating for QLI is Hold.
Q: What is the prediction period for QLI stock?
A: The prediction period for QLI is 3 Month

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.