This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms.** We evaluate DATANG INTERNATIONAL POWER GENERATION COMPANY LD prediction models with Multi-Instance Learning (ML) and ElasticNet Regression ^{1,2,3,4} and conclude that the LON:DAT stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:DAT stock.**

**LON:DAT, DATANG INTERNATIONAL POWER GENERATION COMPANY LD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What is a prediction confidence?
- Should I buy stocks now or wait amid such uncertainty?
- Market Outlook

## LON:DAT Target Price Prediction Modeling Methodology

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We consider DATANG INTERNATIONAL POWER GENERATION COMPANY LD Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:DAT 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(ElasticNet Regression)

^{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(Multi-Instance Learning (ML)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:DAT 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:DAT Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:DAT DATANG INTERNATIONAL POWER GENERATION COMPANY LD

**Time series to forecast n: 07 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:DAT 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

DATANG INTERNATIONAL POWER GENERATION COMPANY LD assigned short-term B2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Multi-Instance Learning (ML) with ElasticNet Regression ^{1,2,3,4} and conclude that the LON:DAT stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:DAT stock.**

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

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

Outlook* | B2 | Ba1 |

Operational Risk | 45 | 32 |

Market Risk | 51 | 86 |

Technical Analysis | 67 | 79 |

Fundamental Analysis | 30 | 78 |

Risk Unsystematic | 87 | 75 |

### Prediction Confidence Score

## References

- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:DAT stock?A: LON:DAT stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and ElasticNet Regression

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

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

Q: Is DATANG INTERNATIONAL POWER GENERATION COMPANY LD stock a good investment?

A: The consensus rating for DATANG INTERNATIONAL POWER GENERATION COMPANY LD is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.

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

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

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

A: The prediction period for LON:DAT is (n+8 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)