With technological advancements, big data can be easily generated and collected in many applications. Embedded in these big data are useful information and knowledge that can be discovered by machine learning and data mining models, techniques or algorithms.** We evaluate AIR CHINA LD prediction models with Ensemble Learning (ML) and Pearson Correlation ^{1,2,3,4} and conclude that the LON:AIRC stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:AIRC stock.**

**LON:AIRC, AIR CHINA LD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Is now good time to invest?
- Can stock prices be predicted?
- Probability Distribution

## LON:AIRC Target Price Prediction Modeling Methodology

Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We consider AIR CHINA LD Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:AIRC 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(Pearson Correlation)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:AIRC 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:AIRC Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:AIRC AIR CHINA LD

**Time series to forecast n: 22 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:AIRC 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

AIR CHINA LD assigned short-term B1 & long-term B2 forecasted stock rating.** We evaluate the prediction models Ensemble Learning (ML) with Pearson Correlation ^{1,2,3,4} and conclude that the LON:AIRC stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:AIRC stock.**

### Financial State Forecast for LON:AIRC Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | B2 |

Operational Risk | 85 | 32 |

Market Risk | 33 | 43 |

Technical Analysis | 61 | 70 |

Fundamental Analysis | 56 | 38 |

Risk Unsystematic | 78 | 81 |

### Prediction Confidence Score

## References

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- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:AIRC stock?A: LON:AIRC stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Pearson Correlation

Q: Is LON:AIRC stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:AIRC Stock.

Q: Is AIR CHINA LD stock a good investment?

A: The consensus rating for AIR CHINA LD is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of LON:AIRC stock?

A: The consensus rating for LON:AIRC is Hold.

Q: What is the prediction period for LON:AIRC stock?

A: The prediction period for LON:AIRC is (n+3 month)

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