Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. We evaluate Norfolk Southern Railway prediction models with Reinforcement Machine Learning (ML) and Chi-Square1,2,3,4 and conclude that the NSC 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 NSC stock.
Keywords: NSC, Norfolk Southern Railway, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Dominated Move
- What are buy sell or hold recommendations?
- What are buy sell or hold recommendations?
NSC Target Price Prediction Modeling Methodology
The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. We consider Norfolk Southern Railway Stock Decision Process with Chi-Square where A is the set of discrete actions of NSC 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(Chi-Square)5,6,7= X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of NSC 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?
NSC Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: NSC Norfolk Southern Railway
Time series to forecast n: 21 Sep 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSC 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
Norfolk Southern Railway assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Chi-Square1,2,3,4 and conclude that the NSC 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 NSC stock.
Financial State Forecast for NSC Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba3 |
| Operational Risk | 55 | 58 |
| Market Risk | 45 | 81 |
| Technical Analysis | 33 | 68 |
| Fundamental Analysis | 59 | 69 |
| Risk Unsystematic | 55 | 36 |
Prediction Confidence Score
References
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
Frequently Asked Questions
Q: What is the prediction methodology for NSC stock?A: NSC stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Chi-Square
Q: Is NSC stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSC Stock.
Q: Is Norfolk Southern Railway stock a good investment?
A: The consensus rating for Norfolk Southern Railway is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSC stock?
A: The consensus rating for NSC is Hold.
Q: What is the prediction period for NSC stock?
A: The prediction period for NSC is (n+8 weeks)
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