Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We evaluate Truist prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Statistical Hypothesis Testing1,2,3,4 and conclude that the TFC 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 Hold TFC stock.

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

## Key Points

1. Can neural networks predict stock market?
2. How do predictive algorithms actually work?
3. What are the most successful trading algorithms?

## TFC Target Price Prediction Modeling Methodology

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We consider Truist Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of TFC 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(Statistical Hypothesis Testing)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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+16 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of TFC 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?

## TFC Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: TFC Truist
Time series to forecast n: 23 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold TFC 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

Truist assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Statistical Hypothesis Testing1,2,3,4 and conclude that the TFC 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 Hold TFC stock.

### Financial State Forecast for TFC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 4335
Market Risk3839
Technical Analysis8164
Fundamental Analysis6183
Risk Unsystematic5167

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 763 signals.

## References

1. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
2. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
4. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
5. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
6. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
7. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
Frequently Asked QuestionsQ: What is the prediction methodology for TFC stock?
A: TFC stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Statistical Hypothesis Testing
Q: Is TFC stock a buy or sell?
A: The dominant strategy among neural network is to Hold TFC Stock.
Q: Is Truist stock a good investment?
A: The consensus rating for Truist is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of TFC stock?
A: The consensus rating for TFC is Hold.
Q: What is the prediction period for TFC stock?
A: The prediction period for TFC is (n+16 weeks)