With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making.We evaluate CarMax prediction models with Modular Neural Network (Market Direction Analysis) and Paired T-Test1,2,3,4 and conclude that the KMX 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 KMX stock.

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

Key Points

1. Decision Making
2. Reaction Function
3. Nash Equilibria

KMX Target Price Prediction Modeling Methodology

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We consider CarMax Stock Decision Process with Paired T-Test where A is the set of discrete actions of KMX 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 (Market Direction Analysis)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of KMX 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?

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

Sample Set: Neural Network
Stock/Index: KMX CarMax
Time series to forecast n: 09 Sep 2022 for (n+3 month)

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

CarMax assigned short-term B2 & long-term Caa1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Paired T-Test1,2,3,4 and conclude that the KMX 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 KMX stock.

Financial State Forecast for KMX Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Caa1
Operational Risk 3630
Market Risk7931
Technical Analysis6742
Fundamental Analysis5448
Risk Unsystematic5239

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 595 signals.

References

1. 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
2. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
3. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
4. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
5. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
6. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
7. 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
Frequently Asked QuestionsQ: What is the prediction methodology for KMX stock?
A: KMX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Paired T-Test
Q: Is KMX stock a buy or sell?
A: The dominant strategy among neural network is to Hold KMX Stock.
Q: Is CarMax stock a good investment?
A: The consensus rating for CarMax is Hold and assigned short-term B2 & long-term Caa1 forecasted stock rating.
Q: What is the consensus rating of KMX stock?
A: The consensus rating for KMX is Hold.
Q: What is the prediction period for KMX stock?
A: The prediction period for KMX is (n+3 month)