Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We evaluate INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC prediction models with Modular Neural Network (Market Direction Analysis) and Pearson Correlation1,2,3,4 and conclude that the LON:IPU stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:IPU stock.
Keywords: LON:IPU, INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can statistics predict the future?
- Probability Distribution
- What is a prediction confidence?
LON:IPU Target Price Prediction Modeling Methodology
Market systems are so complex that they overwhelm the ability of any individual to predict. But it is crucial for the investors to predict stock market price to generate notable profit. We have taken into factors such as Commodity Prices (crude oil, gold, silver), Market History, and Foreign exchange rate (FEX) that influence the stock trend. We consider INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:IPU 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 (Market Direction Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of LON:IPU 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:IPU Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: LON:IPU INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC
Time series to forecast n: 15 Oct 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:IPU 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
INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Pearson Correlation1,2,3,4 and conclude that the LON:IPU stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:IPU stock.
Financial State Forecast for LON:IPU Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B1 |
| Operational Risk | 80 | 40 |
| Market Risk | 76 | 76 |
| Technical Analysis | 36 | 63 |
| Fundamental Analysis | 42 | 73 |
| Risk Unsystematic | 50 | 30 |
Prediction Confidence Score
References
- 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.
- Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
- Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
Frequently Asked Questions
Q: What is the prediction methodology for LON:IPU stock?A: LON:IPU stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Pearson Correlation
Q: Is LON:IPU stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:IPU Stock.
Q: Is INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC stock a good investment?
A: The consensus rating for INVESCO PERPETUAL UK SMALLER COMPANIES INVESTMENT TRUST PLC is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:IPU stock?
A: The consensus rating for LON:IPU is Buy.
Q: What is the prediction period for LON:IPU stock?
A: The prediction period for LON:IPU is (n+16 weeks)
- 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)