Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. We evaluate Tripadvisor prediction models with Modular Neural Network (DNN Layer) and Spearman Correlation1,2,3,4 and conclude that the TRIP 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 Hold TRIP stock.
Keywords: TRIP, Tripadvisor, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Is Target price a good indicator?
- Nash Equilibria
- Can stock prices be predicted?
TRIP Target Price Prediction Modeling Methodology
Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. We consider Tripadvisor Stock Decision Process with Spearman Correlation where A is the set of discrete actions of TRIP 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(Spearman Correlation)5,6,7= X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of TRIP stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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TRIP Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: TRIP Tripadvisor
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 Hold TRIP 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
Tripadvisor assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Spearman Correlation1,2,3,4 and conclude that the TRIP 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 Hold TRIP stock.
Financial State Forecast for TRIP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Ba3 |
| Operational Risk | 48 | 72 |
| Market Risk | 77 | 32 |
| Technical Analysis | 40 | 80 |
| Fundamental Analysis | 65 | 61 |
| Risk Unsystematic | 43 | 85 |
Prediction Confidence Score
References
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- Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
- S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
- J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
- Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
Frequently Asked Questions
Q: What is the prediction methodology for TRIP stock?A: TRIP stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Spearman Correlation
Q: Is TRIP stock a buy or sell?
A: The dominant strategy among neural network is to Hold TRIP Stock.
Q: Is Tripadvisor stock a good investment?
A: The consensus rating for Tripadvisor is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of TRIP stock?
A: The consensus rating for TRIP is Hold.
Q: What is the prediction period for TRIP stock?
A: The prediction period for TRIP is (n+16 weeks)
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