In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. We evaluate JUPITER FUND MANAGEMENT PLC prediction models with Deductive Inference (ML) and Multiple Regression1,2,3,4 and conclude that the LON:JUP 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 Sell LON:JUP stock.

Keywords: LON:JUP, JUPITER FUND MANAGEMENT PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Investment Risk
2. What statistical methods are used to analyze data?
3. How do predictive algorithms actually work?

LON:JUP Target Price Prediction Modeling Methodology

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We consider JUPITER FUND MANAGEMENT PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:JUP 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+8 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:JUP 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:JUP Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:JUP JUPITER FUND MANAGEMENT PLC
Time series to forecast n: 22 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:JUP 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

JUPITER FUND MANAGEMENT PLC assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Multiple Regression1,2,3,4 and conclude that the LON:JUP 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 Sell LON:JUP stock.

Financial State Forecast for LON:JUP Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Operational Risk 8966
Market Risk8368
Technical Analysis7787
Fundamental Analysis3759
Risk Unsystematic4030

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 718 signals.

References

1. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
2. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
3. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
4. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
5. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
6. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
7. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
Frequently Asked QuestionsQ: What is the prediction methodology for LON:JUP stock?
A: LON:JUP stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Multiple Regression
Q: Is LON:JUP stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:JUP Stock.
Q: Is JUPITER FUND MANAGEMENT PLC stock a good investment?
A: The consensus rating for JUPITER FUND MANAGEMENT PLC is Sell and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:JUP stock?
A: The consensus rating for LON:JUP is Sell.
Q: What is the prediction period for LON:JUP stock?
A: The prediction period for LON:JUP is (n+8 weeks)