In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. We evaluate WINTRUST FINL CORP prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Paired T-Test1,2,3,4 and conclude that the WTFC stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold WTFC stock.

Keywords: WTFC, WINTRUST FINL CORP, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Can machine learning predict?
2. Game Theory
3. What is Markov decision process in reinforcement learning? ## WTFC Target Price Prediction Modeling Methodology

Different machine learning algorithms are discussed in this literature review. These algorithms can be used for predicting the stock market. The prediction of the stock market is one of the challenging tasks that must have to be handled. In this paper, it is discussed how the machine learning algorithms can be used for predicting the stock value. We consider WINTRUST FINL CORP Stock Decision Process with Paired T-Test where A is the set of discrete actions of WTFC 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(Paired T-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 (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of WTFC 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?

## WTFC Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: WTFC WINTRUST FINL CORP
Time series to forecast n: 03 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold WTFC 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

WINTRUST FINL CORP assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Paired T-Test1,2,3,4 and conclude that the WTFC stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold WTFC stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba3
Operational Risk 6945
Market Risk7048
Technical Analysis8970
Fundamental Analysis6482
Risk Unsystematic5179

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 599 signals.

## References

1. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
2. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
3. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
4. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
5. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
6. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
7. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
Frequently Asked QuestionsQ: What is the prediction methodology for WTFC stock?
A: WTFC stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Paired T-Test
Q: Is WTFC stock a buy or sell?
A: The dominant strategy among neural network is to Hold WTFC Stock.
Q: Is WINTRUST FINL CORP stock a good investment?
A: The consensus rating for WINTRUST FINL CORP is Hold and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of WTFC stock?
A: The consensus rating for WTFC is Hold.
Q: What is the prediction period for WTFC stock?
A: The prediction period for WTFC is (n+3 month)