## Summary

Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends.** We evaluate NASDAQ Composite Index prediction models with Multi-Task Learning (ML) and Beta ^{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+4 weeks) period: The dominant strategy among neural network is to Hold NASDAQ Composite Index stock.**

## Key Points

- Market Outlook
- How do you decide buy or sell a stock?
- What are buy sell or hold recommendations?

## NASDAQ Composite Index Target Price Prediction Modeling Methodology

We consider NASDAQ Composite Index Stock Decision Process with Multi-Task Learning (ML) 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(Beta)

^{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(Multi-Task Learning (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

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+4 weeks)

**Sample Set:**Neural Network

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

**Time series to forecast n: 29 Nov 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) 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 there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
- For hedges other than hedges of foreign currency risk, when an entity designates a non-derivative financial asset or a non-derivative financial liability measured at fair value through profit or loss as a hedging instrument, it may only designate the non-derivative financial instrument in its entirety or a proportion of it.
- The methods used to determine whether credit risk has increased significantly on a financial instrument since initial recognition should consider the characteristics of the financial instrument (or group of financial instruments) and the default patterns in the past for comparable financial instruments. Despite the requirement in paragraph 5.5.9, for financial instruments for which default patterns are not concentrated at a specific point during the expected life of the financial instrument, changes in the risk of a default occurring over the next 12 months may be a reasonable approximation of the changes in the lifetime risk of a default occurring. In such cases, an entity may use changes in the risk of a default occurring over the next 12 months to determine whether credit risk has increased significantly since initial recognition, unless circumstances indicate that a lifetime assessment is necessary
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*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 B2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Multi-Task Learning (ML) with Beta ^{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+4 weeks) 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* | B2 | Ba1 |

Operational Risk | 89 | 58 |

Market Risk | 36 | 52 |

Technical Analysis | 33 | 87 |

Fundamental Analysis | 83 | 89 |

Risk Unsystematic | 47 | 73 |

### Prediction Confidence Score

## References

- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94

## 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 Multi-Task Learning (ML) and Beta

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 B2 & long-term Ba1 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+4 weeks)