Modelling A.I. in Economics

WTFC Target Price Forecast (Forecast)

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate Wintrust prediction models with Statistical Inference (ML) and Logistic Regression1,2,3,4 and conclude that the WTFC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy WTFC stock.


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

Key Points

  1. Why do we need predictive models?
  2. What is Markov decision process in reinforcement learning?
  3. What is prediction model?

WTFC Target Price Prediction Modeling Methodology

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. We consider Wintrust Stock Decision Process with Logistic Regression 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(Logistic Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Statistical Inference (ML)) X S(n):→ (n+1 year) i = 1 n a i

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+1 year)

Sample Set: Neural Network
Stock/Index: WTFC Wintrust
Time series to forecast n: 04 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy 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 assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Logistic Regression1,2,3,4 and conclude that the WTFC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy WTFC stock.

Financial State Forecast for WTFC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 8355
Market Risk5031
Technical Analysis5256
Fundamental Analysis7756
Risk Unsystematic3062

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 555 signals.

References

  1. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
  2. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
  3. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
  4. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
  5. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
  6. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
  7. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
Frequently Asked QuestionsQ: What is the prediction methodology for WTFC stock?
A: WTFC stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Logistic Regression
Q: Is WTFC stock a buy or sell?
A: The dominant strategy among neural network is to Buy WTFC Stock.
Q: Is Wintrust stock a good investment?
A: The consensus rating for Wintrust is Buy and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of WTFC stock?
A: The consensus rating for WTFC is Buy.
Q: What is the prediction period for WTFC stock?
A: The prediction period for WTFC is (n+1 year)

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