In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour. We evaluate HANSA INVESTMENT COMPANY LIMITED prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Beta1,2,3,4 and conclude that the LON:HAN stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:HAN stock.
Keywords: LON:HAN, 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 statistical models in machine learning?
- Nash Equilibria
- Is it better to buy and sell or hold?
LON:HAN Target Price Prediction Modeling Methodology
This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. We consider HANSA INVESTMENT COMPANY LIMITED Stock Decision Process with Beta where A is the set of discrete actions of LON:HAN 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(Beta)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:HAN 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:HAN Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:HAN HANSA INVESTMENT COMPANY LIMITED
Time series to forecast n: 04 Oct 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:HAN 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 Modular Neural Network (News Feed Sentiment Analysis) with Beta1,2,3,4 and conclude that the LON:HAN stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:HAN stock.
Financial State Forecast for LON:HAN Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B1 |
| Operational Risk | 41 | 46 |
| Market Risk | 86 | 54 |
| Technical Analysis | 58 | 79 |
| Fundamental Analysis | 85 | 51 |
| Risk Unsystematic | 67 | 50 |
Prediction Confidence Score
References
- Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
- Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
- Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
Frequently Asked Questions
Q: What is the prediction methodology for LON:HAN stock?A: LON:HAN stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Beta
Q: Is LON:HAN stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:HAN Stock.
Q: Is HANSA INVESTMENT COMPANY LIMITED stock a good investment?
A: The consensus rating for HANSA INVESTMENT COMPANY LIMITED is Sell and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:HAN stock?
A: The consensus rating for LON:HAN is Sell.
Q: What is the prediction period for LON:HAN stock?
A: The prediction period for LON:HAN is (n+1 year)
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