In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. We evaluate ATX Index prediction models with Modular Neural Network (Market Direction Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the ATX Index 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 ATX Index stock.

Keywords: ATX Index, ATX Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Prediction Modeling
2. Market Signals
3. Trust metric by Neural Network

## ATX Index Target Price Prediction Modeling Methodology

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We consider ATX Index Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of ATX Index 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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ATX Index 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?

## ATX Index Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: ATX Index ATX Index
Time series to forecast n: 22 Sep 2022 for (n+8 weeks)

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

ATX Index assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the ATX Index 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 ATX Index stock.

### Financial State Forecast for ATX Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba1
Operational Risk 4861
Market Risk5688
Technical Analysis4161
Fundamental Analysis4972
Risk Unsystematic3974

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 508 signals.

## References

1. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
2. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
3. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
4. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
5. Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
6. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
7. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
Frequently Asked QuestionsQ: What is the prediction methodology for ATX Index stock?
A: ATX Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Wilcoxon Rank-Sum Test
Q: Is ATX Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold ATX Index Stock.
Q: Is ATX Index stock a good investment?
A: The consensus rating for ATX Index is Hold and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of ATX Index stock?
A: The consensus rating for ATX Index is Hold.
Q: What is the prediction period for ATX Index stock?
A: The prediction period for ATX Index is (n+8 weeks)