BURFORD CAPITAL LIMITED Stock Forecast & Analysis (Forecast)

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We evaluate BURFORD CAPITAL LIMITED prediction models with Modular Neural Network (Speculative Sentiment Analysis) and ElasticNet Regression^1,2,3,4 and conclude that the LON:BUR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:BUR stock.

Keywords: LON:BUR, BURFORD CAPITAL LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What are main components of Markov decision process?
2. Short/Long Term Stocks
3. Is Target price a good indicator?

LON:BUR Target Price Prediction Modeling Methodology

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We consider BURFORD CAPITAL LIMITED Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:BUR 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(ElasticNet Regression)^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(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:BUR stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

 

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:BUR Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:BUR BURFORD CAPITAL LIMITED
Time series to forecast n: 02 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:BUR stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for BURFORD CAPITAL LIMITED

1. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
2. An entity that first applies IFRS 17 as amended in June 2020 after it first applies this Standard shall apply paragraphs 7.2.39–7.2.42. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
3. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
4. If an entity prepares interim financial reports in accordance with IAS 34 Interim Financial Reporting the entity need not apply the requirements in this Standard to interim periods prior to the date of initial application if it is impracticable (as defined in IAS 8).

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

BURFORD CAPITAL LIMITED assigned short-term Ba2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with ElasticNet Regression^1,2,3,4 and conclude that the LON:BUR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:BUR stock.

Financial State Forecast for LON:BUR BURFORD CAPITAL LIMITED Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	Baa2
Operational Risk	52	66
Market Risk	72	87
Technical Analysis	59	79
Fundamental Analysis	75	74
Risk Unsystematic	84	84

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 604 signals.

References

1. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
2. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
3. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
4. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
5. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
6. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
7. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.

Frequently Asked Questions

Q: What is the prediction methodology for LON:BUR stock?
A: LON:BUR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and ElasticNet Regression
Q: Is LON:BUR stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:BUR Stock.
Q: Is BURFORD CAPITAL LIMITED stock a good investment?
A: The consensus rating for BURFORD CAPITAL LIMITED is Sell and assigned short-term Ba2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:BUR stock?
A: The consensus rating for LON:BUR is Sell.
Q: What is the prediction period for LON:BUR stock?
A: The prediction period for LON:BUR is (n+1 year)

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