LON:SERE Stock: A Bubble Waiting to Burst

Outlook: SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC is assigned short-term B2 & 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 : Transductive 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

SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC prediction model is evaluated with Transductive Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the LON:SERE stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Key Points

Probability Distribution
Understanding Buy, Sell, and Hold Ratings
Game Theory

LON:SERE Target Price Prediction Modeling Methodology

We consider SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC Decision Process with Transductive Learning (ML) where A is the set of discrete actions of LON:SERE 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=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Transductive Learning (ML)) X S(n):→ 16 Weeks

\vec{S} = (s_{1}, s_{2}, s_{3})

n:Time series to forecast

p:Price signals of LON:SERE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transductive Learning (ML)

Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.

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:SERE Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:SERE SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC
Time series to forecast: 16 Weeks

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 Transductive Learning (ML) based LON:SERE Stock Prediction Model

Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.
However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).

*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:SERE SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Income Statement	B2	Baa2
Balance Sheet	Baa2	C
Leverage Ratios	Caa2	Caa2
Cash Flow	C	Ba2
Rates of Return and Profitability	Ba3	Caa2

*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

SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC is assigned short-term B2 & long-term B1 estimated rating. SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC prediction model is evaluated with Transductive Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the LON:SERE stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 587 signals.

References

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E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28

Frequently Asked Questions

Q: What is the prediction methodology for LON:SERE stock?
A: LON:SERE stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Statistical Hypothesis Testing
Q: Is LON:SERE stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SERE Stock.
Q: Is SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC stock a good investment?
A: The consensus rating for SCHRODER EUROPEAN REAL ESTATE INVESTMENT TRUST PLC is Hold and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:SERE stock?
A: The consensus rating for LON:SERE is Hold.
Q: What is the prediction period for LON:SERE stock?
A: The prediction period for LON:SERE is 16 Weeks