Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We evaluate HWANGE COLLIERY COMPANY LIMITED prediction models with Inductive Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:HWA stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:HWA stock.

Keywords: LON:HWA, HWANGE COLLIERY COMPANY LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How useful are statistical predictions?
2. What is the best way to predict stock prices?
3. Dominated Move

## LON:HWA 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 HWANGE COLLIERY COMPANY LIMITED Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:HWA 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 Rank-Sum 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(Inductive Learning (ML)) X S(n):→ (n+16 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of LON:HWA 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:HWA Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:HWA HWANGE COLLIERY COMPANY LIMITED
Time series to forecast n: 12 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:HWA 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

HWANGE COLLIERY COMPANY LIMITED assigned short-term Ba3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:HWA stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:HWA stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba1
Operational Risk 8381
Market Risk8272
Technical Analysis8253
Fundamental Analysis3058
Risk Unsystematic5088

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 772 signals.

## References

1. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
2. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
3. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
4. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
5. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
6. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
7. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HWA stock?
A: LON:HWA stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:HWA stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:HWA Stock.
Q: Is HWANGE COLLIERY COMPANY LIMITED stock a good investment?
A: The consensus rating for HWANGE COLLIERY COMPANY LIMITED is Wait until speculative trend diminishes and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:HWA stock?
A: The consensus rating for LON:HWA is Wait until speculative trend diminishes.
Q: What is the prediction period for LON:HWA stock?
A: The prediction period for LON:HWA is (n+16 weeks)