AUC Score :
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n:
Methodology : Statistical Inference (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.
Summary
HIGHWAY CAPITAL PLC prediction model is evaluated with Statistical Inference (ML) and Sign Test1,2,3,4 and it is concluded that the LON:HWC stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend
Key Points
- Why do we need predictive models?
- What are the most successful trading algorithms?
- What is neural prediction?
LON:HWC Target Price Prediction Modeling Methodology
We consider HIGHWAY CAPITAL PLC Decision Process with Statistical Inference (ML) where A is the set of discrete actions of LON:HWC 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= X R(Statistical Inference (ML)) X S(n):→ 1 Year
n:Time series to forecast
p:Price signals of LON:HWC stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Statistical Inference (ML)
Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.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:HWC Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: LON:HWC HIGHWAY CAPITAL PLC
Time series to forecast: 1 Year
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 Statistical Inference (ML) based LON:HWC Stock Prediction Model
- The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
- Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.
- In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.
- For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
*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:HWC HIGHWAY CAPITAL PLC Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B1 | B2 |
Income Statement | Baa2 | B1 |
Balance Sheet | C | B3 |
Leverage Ratios | Baa2 | B1 |
Cash Flow | Ba1 | Caa2 |
Rates of Return and Profitability | Caa2 | 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
HIGHWAY CAPITAL PLC is assigned short-term B1 & long-term B2 estimated rating. HIGHWAY CAPITAL PLC prediction model is evaluated with Statistical Inference (ML) and Sign Test1,2,3,4 and it is concluded that the LON:HWC stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend
Prediction Confidence Score
References
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- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
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- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
Frequently Asked Questions
Q: What is the prediction methodology for LON:HWC stock?A: LON:HWC stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Sign Test
Q: Is LON:HWC stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend LON:HWC Stock.
Q: Is HIGHWAY CAPITAL PLC stock a good investment?
A: The consensus rating for HIGHWAY CAPITAL PLC is Speculative Trend and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of LON:HWC stock?
A: The consensus rating for LON:HWC is Speculative Trend.
Q: What is the prediction period for LON:HWC stock?
A: The prediction period for LON:HWC is 1 Year