Modelling A.I. in Economics

LON:WEN Options & Futures Prediction (Forecast)

With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. We evaluate WENTWORTH RESOURCES PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Lasso Regression1,2,3,4 and conclude that the LON:WEN 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 Hold LON:WEN stock.


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

Key Points

  1. Buy, Sell and Hold Signals
  2. How do you know when a stock will go up or down?
  3. Can statistics predict the future?

LON:WEN Target Price Prediction Modeling Methodology

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. We consider WENTWORTH RESOURCES PLC Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:WEN 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(Lasso Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:WEN WENTWORTH RESOURCES PLC
Time series to forecast n: 27 Sep 2022 for (n+6 month)

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

WENTWORTH RESOURCES PLC assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Lasso Regression1,2,3,4 and conclude that the LON:WEN 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 Hold LON:WEN stock.

Financial State Forecast for LON:WEN Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B2
Operational Risk 8932
Market Risk7249
Technical Analysis5068
Fundamental Analysis7961
Risk Unsystematic7963

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 588 signals.

References

  1. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
  2. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
  3. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
  4. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
  5. 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
  6. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  7. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
Frequently Asked QuestionsQ: What is the prediction methodology for LON:WEN stock?
A: LON:WEN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Lasso Regression
Q: Is LON:WEN stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:WEN Stock.
Q: Is WENTWORTH RESOURCES PLC stock a good investment?
A: The consensus rating for WENTWORTH RESOURCES PLC is Hold and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:WEN stock?
A: The consensus rating for LON:WEN is Hold.
Q: What is the prediction period for LON:WEN stock?
A: The prediction period for LON:WEN is (n+6 month)

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