## Summary

Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed.** We evaluate NASDAQ Composite Index prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the NASDAQ Composite Index stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NASDAQ Composite Index stock.**

## Key Points

- What is statistical models in machine learning?
- Decision Making
- Reaction Function

## NASDAQ Composite Index Target Price Prediction Modeling Methodology

We consider NASDAQ Composite Index Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of NASDAQ Composite 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(Pearson Correlation)

^{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 (News Feed Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of NASDAQ Composite Index stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

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

## NASDAQ Composite Index Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**NASDAQ Composite Index NASDAQ Composite Index

**Time series to forecast n: 27 Nov 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NASDAQ Composite 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%**

## Adjusted IFRS* Prediction Methods for NASDAQ Composite Index

- If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items
- Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.
- An entity shall apply the amendments to IFRS 9 made by IFRS 17 as amended in June 2020 retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.37–7.2.42.
- Contractual cash flows that are solely payments of principal and interest on the principal amount outstanding are consistent with a basic lending arrangement. In a basic lending arrangement, consideration for the time value of money (see paragraphs B4.1.9A–B4.1.9E) and credit risk are typically the most significant elements of interest. However, in such an arrangement, interest can also include consideration for other basic lending risks (for example, liquidity risk) and costs (for example, administrative costs) associated with holding the financial asset for a particular period of time. In addition, interest can include a profit margin that is consistent with a basic lending arrangement. In extreme economic circumstances, interest can be negative if, for example, the holder of a financial asset either explicitly or implicitly pays for the deposit of its money for a particular period of time (and that fee exceeds the consideration that the holder receives for the time value of money, credit risk and other basic lending risks and costs).

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

NASDAQ Composite Index assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the NASDAQ Composite Index stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NASDAQ Composite Index stock.**

### Financial State Forecast for NASDAQ Composite Index NASDAQ Composite Index Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | B1 |

Operational Risk | 50 | 56 |

Market Risk | 90 | 45 |

Technical Analysis | 30 | 72 |

Fundamental Analysis | 44 | 73 |

Risk Unsystematic | 89 | 35 |

### Prediction Confidence Score

## References

- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
- F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008

## Frequently Asked Questions

Q: What is the prediction methodology for NASDAQ Composite Index stock?A: NASDAQ Composite Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation

Q: Is NASDAQ Composite Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold NASDAQ Composite Index Stock.

Q: Is NASDAQ Composite Index stock a good investment?

A: The consensus rating for NASDAQ Composite Index is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of NASDAQ Composite Index stock?

A: The consensus rating for NASDAQ Composite Index is Hold.

Q: What is the prediction period for NASDAQ Composite Index stock?

A: The prediction period for NASDAQ Composite Index is (n+1 year)

- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)