Modelling A.I. in Economics

LON:DFI Stock: Future is Bright, But the Stock May Be Overvalued

Outlook: DFI RETAIL GROUP HOLDINGS LIMITED is assigned short-term B2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Transfer Learning (ML)
Hypothesis Testing : Sign 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.


DFI RETAIL GROUP HOLDINGS LIMITED prediction model is evaluated with Transfer Learning (ML) and Sign Test1,2,3,4 and it is concluded that the LON:DFI stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy

Graph 2

Key Points

  1. What is prediction in deep learning?
  2. Market Signals
  3. Nash Equilibria

LON:DFI Target Price Prediction Modeling Methodology

We consider DFI RETAIL GROUP HOLDINGS LIMITED Decision Process with Transfer Learning (ML) where A is the set of discrete actions of LON:DFI 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(Sign 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(Transfer Learning (ML)) X S(n):→ 1 Year R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of LON:DFI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transfer Learning (ML)

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.

Sign Test

The sign test is a non-parametric hypothesis test that is used to compare two paired samples. In a paired sample, 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 sign test is a non-parametric test, which means that it does not assume that the data is normally distributed. The sign 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?

LON:DFI Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:DFI DFI RETAIL GROUP HOLDINGS LIMITED
Time series to forecast: 1 Year

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 Transfer Learning (ML) based LON:DFI Stock Prediction Model

  1. 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.
  2. When an entity first applies this Standard, it may choose as its accounting policy to continue to apply the hedge accounting requirements of IAS 39 instead of the requirements in Chapter 6 of this Standard. An entity shall apply that policy to all of its hedging relationships. An entity that chooses that policy shall also apply IFRIC 16 Hedges of a Net Investment in a Foreign Operation without the amendments that conform that Interpretation to the requirements in Chapter 6 of this Standard.
  3. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
  4. An entity's business model refers to how an entity manages its financial assets in order to generate cash flows. That is, the entity's business model determines whether cash flows will result from collecting contractual cash flows, selling financial assets or both. Consequently, this assessment is not performed on the basis of scenarios that the entity does not reasonably expect to occur, such as so-called 'worst case' or 'stress case' scenarios. For example, if an entity expects that it will sell a particular portfolio of financial assets only in a stress case scenario, that scenario would not affect the entity's assessment of the business model for those assets if the entity reasonably expects that such a scenario will not occur. If cash flows are realised in a way that is different from the entity's expectations at the date that the entity assessed the business model (for example, if the entity sells more or fewer financial assets than it expected when it classified the assets), that does not give rise to a prior period error in the entity's financial statements (see IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors) nor does it change the classification of the remaining financial assets held in that business model (ie those assets that the entity recognised in prior periods and still holds) as long as the entity considered all relevant information that was available at the time that it made the business model assessment.

*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:DFI DFI RETAIL GROUP HOLDINGS LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2B1
Income StatementBa3Ba3
Balance SheetCaa2B3
Leverage RatiosCBaa2
Cash FlowCC
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?

References

  1. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Tesla Stock: Hold for Now, But Watch for Opportunities. AC Investment Research Journal, 220(44).
  2. 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
  3. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
  4. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
  5. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
  6. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
  7. Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
Frequently Asked QuestionsQ: What is the prediction methodology for LON:DFI stock?
A: LON:DFI stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Sign Test
Q: Is LON:DFI stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:DFI Stock.
Q: Is DFI RETAIL GROUP HOLDINGS LIMITED stock a good investment?
A: The consensus rating for DFI RETAIL GROUP HOLDINGS LIMITED is Buy and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:DFI stock?
A: The consensus rating for LON:DFI is Buy.
Q: What is the prediction period for LON:DFI stock?
A: The prediction period for LON:DFI is 1 Year

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