TA 35 Index Target Price Prediction (Forecast)

Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. We evaluate TA 35 Index prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the TA 35 Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold TA 35 Index stock.

Keywords: TA 35 Index, TA 35 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Is Target price a good indicator?
2. Can stock prices be predicted?
3. Market Outlook

TA 35 Index Target Price Prediction Modeling Methodology

As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. We consider TA 35 Index Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of TA 35 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of TA 35 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?

TA 35 Index Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: TA 35 Index TA 35 Index
Time series to forecast n: 15 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold TA 35 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

TA 35 Index assigned short-term Ba3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the TA 35 Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold TA 35 Index stock.

Financial State Forecast for TA 35 Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Baa2
Operational Risk	38	88
Market Risk	45	61
Technical Analysis	77	77
Fundamental Analysis	74	82
Risk Unsystematic	82	84

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 763 signals.

References

1. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
2. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
3. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
4. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
5. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
6. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
7. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for TA 35 Index stock?
A: TA 35 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test
Q: Is TA 35 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold TA 35 Index Stock.
Q: Is TA 35 Index stock a good investment?
A: The consensus rating for TA 35 Index is Hold and assigned short-term Ba3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of TA 35 Index stock?
A: The consensus rating for TA 35 Index is Hold.
Q: What is the prediction period for TA 35 Index stock?
A: The prediction period for TA 35 Index is (n+4 weeks)

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