Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We evaluate LONDON FINANCE & INVESTMENT GROUP PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the LON:LFI 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 Hold LON:LFI stock.
Keywords: LON:LFI, LONDON FINANCE & INVESTMENT GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Is now good time to invest?
- What is the best way to predict stock prices?
- Prediction Modeling
LON:LFI Target Price Prediction Modeling Methodology
Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods We consider LONDON FINANCE & INVESTMENT GROUP PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:LFI 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(Multiple Regression)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of LON:LFI 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:LFI Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: LON:LFI LONDON FINANCE & INVESTMENT GROUP PLC
Time series to forecast n: 03 Oct 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:LFI 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
LONDON FINANCE & INVESTMENT GROUP PLC assigned short-term Ba3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the LON:LFI 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 Hold LON:LFI stock.
Financial State Forecast for LON:LFI Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B3 |
| Operational Risk | 53 | 35 |
| Market Risk | 56 | 66 |
| Technical Analysis | 78 | 56 |
| Fundamental Analysis | 90 | 31 |
| Risk Unsystematic | 51 | 43 |
Prediction Confidence Score
References
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
- Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
- Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
Frequently Asked Questions
Q: What is the prediction methodology for LON:LFI stock?A: LON:LFI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression
Q: Is LON:LFI stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:LFI Stock.
Q: Is LONDON FINANCE & INVESTMENT GROUP PLC stock a good investment?
A: The consensus rating for LONDON FINANCE & INVESTMENT GROUP PLC is Hold and assigned short-term Ba3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:LFI stock?
A: The consensus rating for LON:LFI is Hold.
Q: What is the prediction period for LON:LFI stock?
A: The prediction period for LON:LFI is (n+8 weeks)
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