Where Will LON:MACF Stock Be in 8 Weeks?

Outlook: MACFARLANE GROUP PLC is assigned short-term Baa2 & long-term Ba1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Speculative Trend
Time series to forecast n: for Weeks²
Methodology : Supervised Machine Learning (ML)
Hypothesis Testing : Factor
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

MACFARLANE GROUP PLC prediction model is evaluated with Supervised Machine Learning (ML) and Factor^1,2,3,4 and it is concluded that the LON:MACF stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Speculative Trend

Key Points

1. Can statistics predict the future?
2. Trading Interaction
3. How accurate is machine learning in stock market?

LON:MACF Target Price Prediction Modeling Methodology

We consider MACFARLANE GROUP PLC Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of LON:MACF 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(Factor)^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(Supervised Machine Learning (ML)) X S(n):→ 8 Weeks $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:MACF stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Supervised Machine Learning (ML)

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

Factor

In statistics, a factor is a variable that can influence the value of another variable. Factors can be categorical or continuous. Categorical factors have a limited number of possible values, such as gender (male or female) or blood type (A, B, AB, or O). Continuous factors can have an infinite number of possible values, such as height or weight. Factors can be used to explain the variation in a dependent variable. For example, a study might find that there is a relationship between gender and height. In this case, gender would be the independent variable, height would be the dependent variable, and the factor would be gender.

 

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:MACF Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:MACF MACFARLANE GROUP PLC
Time series to forecast: 8 Weeks

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

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 Supervised Machine Learning (ML) based LON:MACF Stock Prediction Model

1. 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.
2. If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
3. Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.
4. When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)

*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:MACF MACFARLANE GROUP PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Ba1
Income Statement	Baa2	C
Balance Sheet	Baa2	Baa2
Leverage Ratios	B1	Ba2
Cash Flow	Ba3	Baa2
Rates of Return and Profitability	Baa2	Baa2

*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

MACFARLANE GROUP PLC is assigned short-term Baa2 & long-term Ba1 estimated rating. MACFARLANE GROUP PLC prediction model is evaluated with Supervised Machine Learning (ML) and Factor^1,2,3,4 and it is concluded that the LON:MACF stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Speculative Trend

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 518 signals.

References

1. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
2. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
3. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
4. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
5. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
6. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
7. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678

Frequently Asked Questions

Q: What is the prediction methodology for LON:MACF stock?
A: LON:MACF stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Factor
Q: Is LON:MACF stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend LON:MACF Stock.
Q: Is MACFARLANE GROUP PLC stock a good investment?
A: The consensus rating for MACFARLANE GROUP PLC is Speculative Trend and is assigned short-term Baa2 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:MACF stock?
A: The consensus rating for LON:MACF is Speculative Trend.
Q: What is the prediction period for LON:MACF stock?
A: The prediction period for LON:MACF is 8 Weeks

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