Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We evaluate Avis Budget Group prediction models with Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the CAR stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold CAR stock.
Keywords: CAR, Avis Budget Group, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How accurate is machine learning in stock market?
- Understanding Buy, Sell, and Hold Ratings
- What is neural prediction?
CAR Target Price Prediction Modeling Methodology
Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We consider Avis Budget Group Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of CAR 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(Wilcoxon Rank-Sum Test)5,6,7= X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of CAR stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
CAR Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: CAR Avis Budget Group
Time series to forecast n: 04 Oct 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold CAR 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
Avis Budget Group assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the CAR stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold CAR stock.
Financial State Forecast for CAR Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B2 |
| Operational Risk | 76 | 53 |
| Market Risk | 54 | 57 |
| Technical Analysis | 50 | 82 |
| Fundamental Analysis | 75 | 42 |
| Risk Unsystematic | 38 | 40 |
Prediction Confidence Score
References
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- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
- T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
Frequently Asked Questions
Q: What is the prediction methodology for CAR stock?A: CAR stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test
Q: Is CAR stock a buy or sell?
A: The dominant strategy among neural network is to Hold CAR Stock.
Q: Is Avis Budget Group stock a good investment?
A: The consensus rating for Avis Budget Group is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of CAR stock?
A: The consensus rating for CAR is Hold.
Q: What is the prediction period for CAR stock?
A: The prediction period for CAR is (n+8 weeks)
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