Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We evaluate COUNTRYSIDE PARTNERSHIPS PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:CSP stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:CSP stock.
Keywords: LON:CSP, COUNTRYSIDE PARTNERSHIPS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is a prediction confidence?
- Prediction Modeling
- Understanding Buy, Sell, and Hold Ratings
LON:CSP Target Price Prediction Modeling Methodology
This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. We consider COUNTRYSIDE PARTNERSHIPS PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:CSP 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(Stepwise Regression)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of LON:CSP 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:CSP Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: LON:CSP COUNTRYSIDE PARTNERSHIPS PLC
Time series to forecast n: 13 Sep 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:CSP 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
COUNTRYSIDE PARTNERSHIPS PLC assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:CSP stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:CSP stock.
Financial State Forecast for LON:CSP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | B1 |
| Operational Risk | 35 | 61 |
| Market Risk | 36 | 81 |
| Technical Analysis | 34 | 59 |
| Fundamental Analysis | 57 | 54 |
| Risk Unsystematic | 68 | 33 |
Prediction Confidence Score
References
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
Frequently Asked Questions
Q: What is the prediction methodology for LON:CSP stock?A: LON:CSP stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression
Q: Is LON:CSP stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:CSP Stock.
Q: Is COUNTRYSIDE PARTNERSHIPS PLC stock a good investment?
A: The consensus rating for COUNTRYSIDE PARTNERSHIPS PLC is Buy and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:CSP stock?
A: The consensus rating for LON:CSP is Buy.
Q: What is the prediction period for LON:CSP stock?
A: The prediction period for LON:CSP is (n+8 weeks)
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