Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We evaluate Graham Holdings Company prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Pearson Correlation1,2,3,4 and conclude that the GHC stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy GHC stock.
Keywords: GHC, Graham Holdings Company, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Is now good time to invest?
- Market Outlook
- Can statistics predict the future?
GHC Target Price Prediction Modeling Methodology
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 consider Graham Holdings Company Stock Decision Process with Pearson Correlation where A is the set of discrete actions of GHC 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(Pearson Correlation)5,6,7= X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of GHC stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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GHC Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: GHC Graham Holdings Company
Time series to forecast n: 03 Oct 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy GHC 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
Graham Holdings Company assigned short-term B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Pearson Correlation1,2,3,4 and conclude that the GHC stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy GHC stock.
Financial State Forecast for GHC Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba2 |
| Operational Risk | 58 | 67 |
| Market Risk | 42 | 90 |
| Technical Analysis | 49 | 42 |
| Fundamental Analysis | 59 | 58 |
| Risk Unsystematic | 42 | 89 |
Prediction Confidence Score
References
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
Frequently Asked Questions
Q: What is the prediction methodology for GHC stock?A: GHC stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Pearson Correlation
Q: Is GHC stock a buy or sell?
A: The dominant strategy among neural network is to Buy GHC Stock.
Q: Is Graham Holdings Company stock a good investment?
A: The consensus rating for Graham Holdings Company is Buy and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of GHC stock?
A: The consensus rating for GHC is Buy.
Q: What is the prediction period for GHC stock?
A: The prediction period for GHC is (n+4 weeks)
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