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 HANSA INVESTMENT COMPANY LIMITED prediction models with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:HAN 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 Buy 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

1. Fundemental Analysis with Algorithmic Trading
2. Trust metric by Neural Network
3. Is Target price a good indicator?

## LON:HAN 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 HANSA INVESTMENT COMPANY LIMITED Stock Decision Process with Wilcoxon Sign-Rank Test 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(Wilcoxon Sign-Rank Test)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+3 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

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+3 month)

Sample Set: Neural Network
Stock/Index: LON:HAN HANSA INVESTMENT COMPANY LIMITED
Time series to forecast n: 07 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy 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 B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:HAN 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 Buy LON:HAN stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 7460
Market Risk8049
Technical Analysis3750
Fundamental Analysis3990
Risk Unsystematic7037

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 569 signals.

## References

1. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
2. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
3. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
4. 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
5. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
6. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
7. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HAN stock?
A: LON:HAN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test
Q: Is LON:HAN stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:HAN Stock.
Q: Is HANSA INVESTMENT COMPANY LIMITED stock a good investment?
A: The consensus rating for HANSA INVESTMENT COMPANY LIMITED is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:HAN stock?
A: The consensus rating for LON:HAN is Buy.
Q: What is the prediction period for LON:HAN stock?
A: The prediction period for LON:HAN is (n+3 month)