Outlook: Ziff Davis Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 26 Feb 2023 for (n+3 month)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

## Abstract

Ziff Davis Inc. Common Stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Logistic Regression1,2,3,4 and it is concluded that the ZD stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

## Key Points

1. How can neural networks improve predictions?
2. Stock Forecast Based On a Predictive Algorithm

## ZD Target Price Prediction Modeling Methodology

We consider Ziff Davis Inc. Common Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of ZD 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(Logistic Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of ZD 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?

## ZD Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: ZD Ziff Davis Inc. Common Stock
Time series to forecast n: 26 Feb 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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 (Grey to Black): *Technical Analysis%

## IFRS Reconciliation Adjustments for Ziff Davis Inc. Common Stock

1. If a financial asset contains a contractual term that could change the timing or amount of contractual cash flows (for example, if the asset can be prepaid before maturity or its term can be extended), the entity must determine whether the contractual cash flows that could arise over the life of the instrument due to that contractual term are solely payments of principal and interest on the principal amount outstanding. To make this determination, the entity must assess the contractual cash flows that could arise both before, and after, the change in contractual cash flows. The entity may also need to assess the nature of any contingent event (ie the trigger) that would change the timing or amount of the contractual cash flows. While the nature of the contingent event in itself is not a determinative factor in assessing whether the contractual cash flows are solely payments of principal and interest, it may be an indicator. For example, compare a financial instrument with an interest rate that is reset to a higher rate if the debtor misses a particular number of payments to a financial instrument with an interest rate that is reset to a higher rate if a specified equity index reaches a particular level. It is more likely in the former case that the contractual cash flows over the life of the instrument will be solely payments of principal and interest on the principal amount outstanding because of the relationship between missed payments and an increase in credit risk. (See also paragraph B4.1.18.)
2. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period
3. An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.
4. If an entity previously accounted at cost (in accordance with IAS 39), for an investment in an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) (or for a derivative asset that is linked to and must be settled by delivery of such an equity instrument) it shall measure that instrument at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

## Conclusions

Ziff Davis Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Ziff Davis Inc. Common Stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Logistic Regression1,2,3,4 and it is concluded that the ZD stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

### ZD Ziff Davis Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCBa1
Balance SheetB3C
Leverage RatiosBaa2Caa2
Cash FlowBa1C
Rates of Return and ProfitabilityBaa2Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 518 signals.

## References

1. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., When to Sell and When to Hold AQN Stock. AC Investment Research Journal, 101(3).
3. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
4. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., What are buy sell or hold recommendations?(AIRC Stock Forecast). AC Investment Research Journal, 101(3).
6. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
7. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
Frequently Asked QuestionsQ: What is the prediction methodology for ZD stock?
A: ZD stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Logistic Regression
Q: Is ZD stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes ZD Stock.
Q: Is Ziff Davis Inc. Common Stock stock a good investment?
A: The consensus rating for Ziff Davis Inc. Common Stock is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ZD stock?
A: The consensus rating for ZD is Wait until speculative trend diminishes.
Q: What is the prediction period for ZD stock?
A: The prediction period for ZD is (n+3 month)