Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets.** We evaluate Hargreaves Lansdown prediction models with Transfer Learning (ML) and Paired T-Test ^{1,2,3,4} and conclude that the HL 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 HL stock.**

**HL, Hargreaves Lansdown, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Is it better to buy and sell or hold?
- Why do we need predictive models?
- How can neural networks improve predictions?

## HL Target Price Prediction Modeling Methodology

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We consider Hargreaves Lansdown Stock Decision Process with Paired T-Test where A is the set of discrete actions of HL 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Paired T-Test)

^{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(Transfer Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of HL 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?

## HL Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**HL Hargreaves Lansdown

**Time series to forecast n: 13 Oct 2022**for (n+3 month)

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

Hargreaves Lansdown assigned short-term B1 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Paired T-Test ^{1,2,3,4} and conclude that the HL 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 HL stock.**

### Financial State Forecast for HL Stock Options & Futures

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

Outlook* | B1 | Ba1 |

Operational Risk | 70 | 62 |

Market Risk | 49 | 76 |

Technical Analysis | 84 | 54 |

Fundamental Analysis | 45 | 87 |

Risk Unsystematic | 63 | 76 |

### Prediction Confidence Score

## References

- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
- Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008

## Frequently Asked Questions

Q: What is the prediction methodology for HL stock?A: HL stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Paired T-Test

Q: Is HL stock a buy or sell?

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

Q: Is Hargreaves Lansdown stock a good investment?

A: The consensus rating for Hargreaves Lansdown is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of HL stock?

A: The consensus rating for HL is Hold.

Q: What is the prediction period for HL stock?

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