## Abstract

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**Outlook:**QCR Holdings Inc. Common Stock assigned short-term B3 & long-term B1 forecasted stock rating.

**Signal:**Hold

**Time series to forecast n: 06 Dec 2022**for (n+4 weeks)

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Short - term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short - period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. (Moghar, A. and Hamiche, M., 2020. Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, pp.1168-1173.)** We evaluate QCR Holdings Inc. Common Stock prediction models with Modular Neural Network (Market Volatility Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the QCRH 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 QCRH stock.**

## Key Points

- Market Risk
- Trading Interaction
- Which neural network is best for prediction?

## QCRH Target Price Prediction Modeling Methodology

We consider QCR Holdings Inc. Common Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of QCRH 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 (Market Volatility Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of QCRH 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?

## QCRH Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**QCRH QCR Holdings Inc. Common Stock

**Time series to forecast n: 06 Dec 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold QCRH 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 QCR Holdings Inc. Common Stock

- An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
- 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.
- When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
- For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.

*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

QCR Holdings Inc. Common Stock assigned short-term B3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the QCRH 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 QCRH stock.**

### Financial State Forecast for QCRH QCR Holdings Inc. Common Stock Options & Futures

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

Outlook* | B3 | B1 |

Operational Risk | 76 | 35 |

Market Risk | 34 | 45 |

Technical Analysis | 50 | 77 |

Fundamental Analysis | 57 | 53 |

Risk Unsystematic | 37 | 76 |

### Prediction Confidence Score

## References

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- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
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- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008

## Frequently Asked Questions

Q: What is the prediction methodology for QCRH stock?A: QCRH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Pearson Correlation

Q: Is QCRH stock a buy or sell?

A: The dominant strategy among neural network is to Hold QCRH Stock.

Q: Is QCR Holdings Inc. Common Stock stock a good investment?

A: The consensus rating for QCR Holdings Inc. Common Stock is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of QCRH stock?

A: The consensus rating for QCRH is Hold.

Q: What is the prediction period for QCRH stock?

A: The prediction period for QCRH is (n+4 weeks)