Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices.** We evaluate Dow Jones New Zealand Index prediction models with Modular Neural Network (Market News Sentiment Analysis) and Paired T-Test ^{1,2,3,4} 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 Hold Dow Jones New Zealand Index stock.**

**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.**

*Keywords:*## Key Points

- What are the most successful trading algorithms?
- Operational Risk
- How can neural networks improve predictions?

## Dow Jones New Zealand Index Target Price Prediction Modeling Methodology

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We consider Dow Jones New Zealand Index Stock Decision Process with Paired T-Test 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(Paired T-Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+16 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

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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How do AC Investment Research machine learning (predictive) algorithms actually work?

## 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: 22 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) 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 Caa2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Paired T-Test ^{1,2,3,4} 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 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* | Caa2 | B1 |

Operational Risk | 36 | 90 |

Market Risk | 30 | 53 |

Technical Analysis | 48 | 61 |

Fundamental Analysis | 44 | 63 |

Risk Unsystematic | 43 | 31 |

### Prediction Confidence Score

## References

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- Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
- Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]

## 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 Modular Neural Network (Market News Sentiment Analysis) and Paired T-Test

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 Caa2 & 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+16 weeks)

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