Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends. We evaluate HARGREAVES LANSDOWN PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Factor1,2,3,4 and conclude that the LON:HL. 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 Hold LON:HL. stock.

Keywords: LON:HL., HARGREAVES LANSDOWN PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is neural prediction?
2. Reaction Function
3. Technical Analysis with Algorithmic Trading

## LON:HL. Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider HARGREAVES LANSDOWN PLC Stock Decision Process with Factor where A is the set of discrete actions of LON: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 γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Factor)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON: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?

## LON:HL. Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:HL. HARGREAVES LANSDOWN PLC
Time series to forecast n: 13 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON: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 PLC assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Factor1,2,3,4 and conclude that the LON:HL. 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 Hold LON:HL. stock.

### Financial State Forecast for LON:HL. Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba1
Operational Risk 3378
Market Risk7289
Technical Analysis6164
Fundamental Analysis6779
Risk Unsystematic6146

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 752 signals.

## References

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2. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
3. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
4. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
5. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
6. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
7. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HL. stock?
A: LON:HL. stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Factor
Q: Is LON:HL. stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HL. Stock.
Q: Is HARGREAVES LANSDOWN PLC stock a good investment?
A: The consensus rating for HARGREAVES LANSDOWN PLC is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:HL. stock?
A: The consensus rating for LON:HL. is Hold.
Q: What is the prediction period for LON:HL. stock?
A: The prediction period for LON:HL. is (n+1 year)

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