Modelling A.I. in Economics

LON:AMOI Stock: The Can That's Slowly Rotting Away

Outlook: ANEMOI INTERNATIONAL LTD is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Statistical Hypothesis Testing
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

ANEMOI INTERNATIONAL LTD prediction model is evaluated with Multi-Task Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the LON:AMOI stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Graph 44

Key Points

  1. What is a prediction confidence?
  2. Game Theory
  3. Can machine learning predict?

LON:AMOI Target Price Prediction Modeling Methodology

We consider ANEMOI INTERNATIONAL LTD Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of LON:AMOI 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(Statistical Hypothesis Testing)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(Multi-Task Learning (ML)) X S(n):→ 6 Month e x rx

n:Time series to forecast

p:Price signals of LON:AMOI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Task Learning (ML)

Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

Statistical Hypothesis Testing

Statistical hypothesis testing is a process used to determine whether there is enough evidence to support a claim about a population based on a sample. The process involves making two hypotheses, a null hypothesis and an alternative hypothesis, and then collecting data and using statistical tests to determine which hypothesis is more likely to be true. The null hypothesis is the statement that there is no difference between the population and the sample. The alternative hypothesis is the statement that there is a difference between the population and the sample. The statistical test is used to calculate a p-value, which is the probability of obtaining the observed data or more extreme data if the null hypothesis is true. A p-value of less than 0.05 is typically considered to be statistically significant, which means that there is less than a 5% chance of obtaining the observed data or more extreme data if 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?

LON:AMOI Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:AMOI ANEMOI INTERNATIONAL LTD
Time series to forecast: 6 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 Multi-Task Learning (ML) based LON:AMOI Stock Prediction Model

  1. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
  2. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.
  3. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
  4. In some circumstances, the renegotiation or modification of the contractual cash flows of a financial asset can lead to the derecognition of the existing financial asset in accordance with this Standard. When the modification of a financial asset results in the derecognition of the existing financial asset and the subsequent recognition of the modified financial asset, the modified asset is considered a 'new' financial asset for the purposes of this Standard.

*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.

LON:AMOI ANEMOI INTERNATIONAL LTD Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Income StatementB2B3
Balance SheetBaa2Baa2
Leverage RatiosBa2B3
Cash FlowBaa2Ba2
Rates of Return and ProfitabilityCaa2C

*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

ANEMOI INTERNATIONAL LTD is assigned short-term Ba3 & long-term B1 estimated rating. ANEMOI INTERNATIONAL LTD prediction model is evaluated with Multi-Task Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the LON:AMOI stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 826 signals.

References

  1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
  2. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
  3. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
  4. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
  5. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
  6. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
  7. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:AMOI stock?
A: LON:AMOI stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Statistical Hypothesis Testing
Q: Is LON:AMOI stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:AMOI Stock.
Q: Is ANEMOI INTERNATIONAL LTD stock a good investment?
A: The consensus rating for ANEMOI INTERNATIONAL LTD is Hold and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:AMOI stock?
A: The consensus rating for LON:AMOI is Hold.
Q: What is the prediction period for LON:AMOI stock?
A: The prediction period for LON:AMOI is 6 Month

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