Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends. We evaluate INTERNATIONAL PUBLIC PARTNERSHIPS LD prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:INPP 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 Hold LON:INPP stock.
Keywords: LON:INPP, INTERNATIONAL PUBLIC PARTNERSHIPS LD, 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?
- What are the most successful trading algorithms?
- How do you decide buy or sell a stock?
LON:INPP Target Price Prediction Modeling Methodology
Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We consider INTERNATIONAL PUBLIC PARTNERSHIPS LD Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:INPP 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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:INPP 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:INPP Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:INPP INTERNATIONAL PUBLIC PARTNERSHIPS LD
Time series to forecast n: 23 Oct 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:INPP 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
INTERNATIONAL PUBLIC PARTNERSHIPS LD assigned short-term Ba2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:INPP 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 Hold LON:INPP stock.
Financial State Forecast for LON:INPP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | B1 |
| Operational Risk | 84 | 43 |
| Market Risk | 46 | 80 |
| Technical Analysis | 61 | 88 |
| Fundamental Analysis | 77 | 45 |
| Risk Unsystematic | 78 | 35 |
Prediction Confidence Score
References
- A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
Frequently Asked Questions
Q: What is the prediction methodology for LON:INPP stock?A: LON:INPP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression
Q: Is LON:INPP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:INPP Stock.
Q: Is INTERNATIONAL PUBLIC PARTNERSHIPS LD stock a good investment?
A: The consensus rating for INTERNATIONAL PUBLIC PARTNERSHIPS LD is Hold and assigned short-term Ba2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:INPP stock?
A: The consensus rating for LON:INPP is Hold.
Q: What is the prediction period for LON:INPP stock?
A: The prediction period for LON:INPP is (n+1 year)
- 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)