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 evaluate First Horizon prediction models with Transductive Learning (ML) and Multiple Regression1,2,3,4 and conclude that the FHN 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 Sell FHN stock.

Keywords: FHN, First Horizon, 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. Can stock prices be predicted?
3. Nash Equilibria

## FHN Target Price Prediction Modeling Methodology

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We consider First Horizon Stock Decision Process with Multiple Regression where A is the set of discrete actions of FHN 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(Multiple Regression)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(Transductive Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of FHN 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?

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

Sample Set: Neural Network
Stock/Index: FHN First Horizon
Time series to forecast n: 13 Sep 2022 for (n+16 weeks)

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

First Horizon assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Multiple Regression1,2,3,4 and conclude that the FHN 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 Sell FHN stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Operational Risk 7032
Market Risk4167
Technical Analysis4361
Fundamental Analysis7586
Risk Unsystematic8739

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 624 signals.

## References

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2. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
3. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
4. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
5. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
6. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
7. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
Frequently Asked QuestionsQ: What is the prediction methodology for FHN stock?
A: FHN stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Multiple Regression
Q: Is FHN stock a buy or sell?
A: The dominant strategy among neural network is to Sell FHN Stock.
Q: Is First Horizon stock a good investment?
A: The consensus rating for First Horizon is Sell and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of FHN stock?
A: The consensus rating for FHN is Sell.
Q: What is the prediction period for FHN stock?
A: The prediction period for FHN is (n+16 weeks)