With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. We evaluate CITY OF LONDON INVESTMENT TRUST PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the LON:BA47 stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:BA47 stock.
Keywords: LON:BA47, CITY OF LONDON INVESTMENT TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Trading Interaction
- Market Risk
- Market Risk
LON:BA47 Target Price Prediction Modeling Methodology
Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We consider CITY OF LONDON INVESTMENT TRUST PLC Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:BA47 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(Pearson Correlation)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of LON:BA47 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:BA47 Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: LON:BA47 CITY OF LONDON INVESTMENT TRUST PLC
Time series to forecast n: 14 Oct 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:BA47 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
CITY OF LONDON INVESTMENT TRUST PLC assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the LON:BA47 stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:BA47 stock.
Financial State Forecast for LON:BA47 Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba3 |
| Operational Risk | 71 | 66 |
| Market Risk | 43 | 89 |
| Technical Analysis | 59 | 66 |
| Fundamental Analysis | 30 | 47 |
| Risk Unsystematic | 45 | 61 |
Prediction Confidence Score
References
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
Frequently Asked Questions
Q: What is the prediction methodology for LON:BA47 stock?A: LON:BA47 stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation
Q: Is LON:BA47 stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:BA47 Stock.
Q: Is CITY OF LONDON INVESTMENT TRUST PLC stock a good investment?
A: The consensus rating for CITY OF LONDON INVESTMENT TRUST PLC is Buy and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:BA47 stock?
A: The consensus rating for LON:BA47 is Buy.
Q: What is the prediction period for LON:BA47 stock?
A: The prediction period for LON:BA47 is (n+6 month)
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