Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We evaluate KAINOS GROUP PLC prediction models with Statistical Inference (ML) and Paired T-Test1,2,3,4 and conclude that the LON:KNOS 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 Wait until speculative trend diminishes LON:KNOS stock.
Keywords: LON:KNOS, KAINOS GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Dominated Move
- Understanding Buy, Sell, and Hold Ratings
- Technical Analysis with Algorithmic Trading
LON:KNOS Target Price Prediction Modeling Methodology
The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We consider KAINOS GROUP PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:KNOS 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(Paired T-Test)5,6,7= X R(Statistical Inference (ML)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:KNOS stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
LON:KNOS Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:KNOS KAINOS GROUP PLC
Time series to forecast n: 14 Sep 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:KNOS 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%
Conclusions
KAINOS GROUP PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Paired T-Test1,2,3,4 and conclude that the LON:KNOS 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 Wait until speculative trend diminishes LON:KNOS stock.
Financial State Forecast for LON:KNOS Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B1 |
| Operational Risk | 47 | 34 |
| Market Risk | 66 | 60 |
| Technical Analysis | 80 | 73 |
| Fundamental Analysis | 75 | 42 |
| Risk Unsystematic | 37 | 72 |
Prediction Confidence Score
References
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- Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
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- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
Frequently Asked Questions
Q: What is the prediction methodology for LON:KNOS stock?A: LON:KNOS stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Paired T-Test
Q: Is LON:KNOS stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:KNOS Stock.
Q: Is KAINOS GROUP PLC stock a good investment?
A: The consensus rating for KAINOS GROUP PLC is Wait until speculative trend diminishes and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:KNOS stock?
A: The consensus rating for LON:KNOS is Wait until speculative trend diminishes.
Q: What is the prediction period for LON:KNOS stock?
A: The prediction period for LON:KNOS is (n+1 year)
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