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 GOOCH & HOUSEGO PLC prediction models with Multi-Instance Learning (ML) and Linear Regression1,2,3,4 and conclude that the LON:GHH stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:GHH stock.
Keywords: LON:GHH, GOOCH & HOUSEGO PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Which neural network is best for prediction?
- Can stock prices be predicted?
- Is it better to buy and sell or hold?
LON:GHH 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 GOOCH & HOUSEGO PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:GHH 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(Linear Regression)5,6,7= X R(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of LON:GHH 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:GHH Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: LON:GHH GOOCH & HOUSEGO PLC
Time series to forecast n: 14 Oct 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:GHH 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
GOOCH & HOUSEGO PLC assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Linear Regression1,2,3,4 and conclude that the LON:GHH stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:GHH stock.
Financial State Forecast for LON:GHH Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba3 |
| Operational Risk | 47 | 53 |
| Market Risk | 58 | 81 |
| Technical Analysis | 61 | 86 |
| Fundamental Analysis | 40 | 46 |
| Risk Unsystematic | 44 | 55 |
Prediction Confidence Score
References
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
- Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
- Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
Frequently Asked Questions
Q: What is the prediction methodology for LON:GHH stock?A: LON:GHH stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Linear Regression
Q: Is LON:GHH stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:GHH Stock.
Q: Is GOOCH & HOUSEGO PLC stock a good investment?
A: The consensus rating for GOOCH & HOUSEGO PLC is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:GHH stock?
A: The consensus rating for LON:GHH is Hold.
Q: What is the prediction period for LON:GHH stock?
A: The prediction period for LON:GHH is (n+4 weeks)
- 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)