This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate HANSA INVESTMENT COMPANY LIMITED prediction models with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:HANA 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:HANA stock.
Keywords: LON:HANA, HANSA INVESTMENT COMPANY LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is the use of Markov decision process?
- Why do we need predictive models?
- What statistical methods are used to analyze data?
LON:HANA Target Price Prediction Modeling Methodology
Short - term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short - period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. We consider HANSA INVESTMENT COMPANY LIMITED Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:HANA 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(Wilcoxon Sign-Rank Test)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:HANA 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:HANA Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: LON:HANA HANSA INVESTMENT COMPANY LIMITED
Time series to forecast n: 18 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:HANA 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
HANSA INVESTMENT COMPANY LIMITED assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:HANA 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:HANA stock.
Financial State Forecast for LON:HANA Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B1 |
| Operational Risk | 54 | 77 |
| Market Risk | 52 | 86 |
| Technical Analysis | 82 | 43 |
| Fundamental Analysis | 66 | 48 |
| Risk Unsystematic | 67 | 42 |
Prediction Confidence Score
References
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
- Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
Frequently Asked Questions
Q: What is the prediction methodology for LON:HANA stock?A: LON:HANA stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is LON:HANA stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HANA Stock.
Q: Is HANSA INVESTMENT COMPANY LIMITED stock a good investment?
A: The consensus rating for HANSA INVESTMENT COMPANY LIMITED is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:HANA stock?
A: The consensus rating for LON:HANA is Hold.
Q: What is the prediction period for LON:HANA stock?
A: The prediction period for LON:HANA is (n+4 weeks)
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