Can stock prices be predicted? (LON:FUTR Stock Forecast)

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. We evaluate FUTURE PLC prediction models with Transductive Learning (ML) and Multiple Regression^1,2,3,4 and conclude that the LON:FUTR 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 HoldBuy LON:FUTR stock.

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

Key Points

1. Which neural network is best for prediction?
2. What is prediction in deep learning?
3. How do you know when a stock will go up or down?

LON:FUTR Target Price Prediction Modeling Methodology

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. We consider FUTURE PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:FUTR 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transductive Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:FUTR 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:FUTR Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:FUTR FUTURE PLC
Time series to forecast n: 26 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to HoldBuy LON:FUTR 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

FUTURE PLC assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Multiple Regression^1,2,3,4 and conclude that the LON:FUTR 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 HoldBuy LON:FUTR stock.

Financial State Forecast for LON:FUTR Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B3
Operational Risk	48	49
Market Risk	72	53
Technical Analysis	75	34
Fundamental Analysis	55	36
Risk Unsystematic	47	42

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 683 signals.

References

1. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
2. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
3. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
4. Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
5. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
6. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
7. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67

Frequently Asked Questions

Q: What is the prediction methodology for LON:FUTR stock?
A: LON:FUTR stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Multiple Regression
Q: Is LON:FUTR stock a buy or sell?
A: The dominant strategy among neural network is to HoldBuy LON:FUTR Stock.
Q: Is FUTURE PLC stock a good investment?
A: The consensus rating for FUTURE PLC is HoldBuy and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:FUTR stock?
A: The consensus rating for LON:FUTR is HoldBuy.
Q: What is the prediction period for LON:FUTR stock?
A: The prediction period for LON:FUTR is (n+6 month)

Premium

• 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)

Login