The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. We evaluate Dow Jones New Zealand Index prediction models with Ensemble Learning (ML) and Factor1,2,3,4 and conclude that the Dow Jones New Zealand Index 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 Dow Jones New Zealand Index stock.
Keywords: Dow Jones New Zealand Index, Dow Jones New Zealand Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Prediction Modeling
- What is neural prediction?
- Reaction Function
Dow Jones New Zealand Index Target Price Prediction Modeling Methodology
Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We consider Dow Jones New Zealand Index Stock Decision Process with Factor where A is the set of discrete actions of Dow Jones New Zealand Index 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(Factor)5,6,7= X R(Ensemble Learning (ML)) X S(n):→ (n+3 month)
n:Time series to forecast
p:Price signals of Dow Jones New Zealand Index stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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Dow Jones New Zealand Index Stock Forecast (Buy or Sell) for (n+3 month)
Sample Set: Neural NetworkStock/Index: Dow Jones New Zealand Index Dow Jones New Zealand Index
Time series to forecast n: 03 Oct 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold Dow Jones New Zealand Index 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
Dow Jones New Zealand Index assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Factor1,2,3,4 and conclude that the Dow Jones New Zealand Index 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 Dow Jones New Zealand Index stock.
Financial State Forecast for Dow Jones New Zealand Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B1 |
| Operational Risk | 60 | 47 |
| Market Risk | 56 | 31 |
| Technical Analysis | 37 | 74 |
| Fundamental Analysis | 78 | 46 |
| Risk Unsystematic | 87 | 86 |
Prediction Confidence Score
References
- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
- N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- 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.
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
Frequently Asked Questions
Q: What is the prediction methodology for Dow Jones New Zealand Index stock?A: Dow Jones New Zealand Index stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Factor
Q: Is Dow Jones New Zealand Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold Dow Jones New Zealand Index Stock.
Q: Is Dow Jones New Zealand Index stock a good investment?
A: The consensus rating for Dow Jones New Zealand Index is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of Dow Jones New Zealand Index stock?
A: The consensus rating for Dow Jones New Zealand Index is Hold.
Q: What is the prediction period for Dow Jones New Zealand Index stock?
A: The prediction period for Dow Jones New Zealand Index is (n+3 month)
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