The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. We evaluate HARLAND & WOLFF GROUP HOLDINGS PLC prediction models with Modular Neural Network (CNN Layer) and Ridge Regression1,2,3,4 and conclude that the LON:HARL 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:HARL stock.
Keywords: LON:HARL, HARLAND & WOLFF GROUP HOLDINGS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Understanding Buy, Sell, and Hold Ratings
- Stock Forecast Based On a Predictive Algorithm
- Reaction Function
LON:HARL 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 HARLAND & WOLFF GROUP HOLDINGS PLC Stock Decision Process with Ridge Regression where A is the set of discrete actions of LON:HARL 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(Ridge Regression)5,6,7= X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of LON:HARL 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:HARL Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: LON:HARL HARLAND & WOLFF GROUP HOLDINGS PLC
Time series to forecast n: 20 Oct 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:HARL 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
HARLAND & WOLFF GROUP HOLDINGS PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Ridge Regression1,2,3,4 and conclude that the LON:HARL 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:HARL stock.
Financial State Forecast for LON:HARL Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B1 |
| Operational Risk | 57 | 61 |
| Market Risk | 90 | 68 |
| Technical Analysis | 34 | 62 |
| Fundamental Analysis | 42 | 63 |
| Risk Unsystematic | 40 | 50 |
Prediction Confidence Score
References
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- 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]
- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
Frequently Asked Questions
Q: What is the prediction methodology for LON:HARL stock?A: LON:HARL stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Ridge Regression
Q: Is LON:HARL stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HARL Stock.
Q: Is HARLAND & WOLFF GROUP HOLDINGS PLC stock a good investment?
A: The consensus rating for HARLAND & WOLFF GROUP HOLDINGS PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:HARL stock?
A: The consensus rating for LON:HARL is Hold.
Q: What is the prediction period for LON:HARL stock?
A: The prediction period for LON:HARL is (n+6 month)
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