Dow Jones New Zealand Index Stock Price Prediction


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 Active Learning (ML) and Ridge Regression1,2,3,4 and conclude that the Dow Jones New Zealand Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell 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

  1. What is Markov decision process in reinforcement learning?
  2. Investment Risk
  3. Trading Signals

Dow Jones New Zealand Index Target Price Prediction Modeling Methodology

The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We consider Dow Jones New Zealand Index Stock Decision Process with Ridge Regression 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(Ridge Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Active Learning (ML)) X S(n):→ (n+16 weeks) R = r 1 r 2 r 3

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+16 weeks)

Sample Set: Neural Network
Stock/Index: Dow Jones New Zealand Index Dow Jones New Zealand Index
Time series to forecast n: 15 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell 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 Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Ridge Regression1,2,3,4 and conclude that the Dow Jones New Zealand Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell 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*Baa2B1
Operational Risk 8961
Market Risk5152
Technical Analysis8077
Fundamental Analysis7663
Risk Unsystematic8730

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 599 signals.

References

  1. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
  2. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
  3. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
  4. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
  5. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
  6. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
  7. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
Frequently Asked QuestionsQ: 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 Active Learning (ML) and Ridge Regression
Q: Is Dow Jones New Zealand Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell 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 Sell and assigned short-term Baa2 & 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 Sell.
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+16 weeks)

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