Outlook: Mediaco Holding Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 02 Apr 2023 for (n+8 weeks)
Methodology : Modular Neural Network (Market Direction Analysis)

## Abstract

Mediaco Holding Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Chi-Square1,2,3,4 and it is concluded that the MDIA stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

## Key Points

1. What is neural prediction?
2. Stock Rating
3. Market Signals

## MDIA Target Price Prediction Modeling Methodology

We consider Mediaco Holding Inc. Class A Common Stock Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of MDIA 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(Chi-Square)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 (Market Direction Analysis)) X S(n):→ (n+8 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of MDIA 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?

## MDIA Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: MDIA Mediaco Holding Inc. Class A Common Stock
Time series to forecast n: 02 Apr 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

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 Mediaco Holding Inc. Class A Common Stock

1. For example, an entity hedges an exposure to Foreign Currency A using a currency derivative that references Foreign Currency B and Foreign Currencies A and B are pegged (ie their exchange rate is maintained within a band or at an exchange rate set by a central bank or other authority). If the exchange rate between Foreign Currency A and Foreign Currency B were changed (ie a new band or rate was set), rebalancing the hedging relationship to reflect the new exchange rate would ensure that the hedging relationship would continue to meet the hedge effectiveness requirement for the hedge ratio in the new circumstances. In contrast, if there was a default on the currency derivative, changing the hedge ratio could not ensure that the hedging relationship would continue to meet that hedge effectiveness requirement. Hence, rebalancing does not facilitate the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item changes in a way that cannot be compensated for by adjusting the hedge ratio
2. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
3. For the purposes of measuring expected credit losses, the estimate of expected cash shortfalls shall reflect the cash flows expected from collateral and other credit enhancements that are part of the contractual terms and are not recognised separately by the entity. The estimate of expected cash shortfalls on a collateralised financial instrument reflects the amount and timing of cash flows that are expected from foreclosure on the collateral less the costs of obtaining and selling the collateral, irrespective of whether foreclosure is probable (ie the estimate of expected cash flows considers the probability of a foreclosure and the cash flows that would result from it). Consequently, any cash flows that are expected from the realisation of the collateral beyond the contractual maturity of the contract should be included in this analysis. Any collateral obtained as a result of foreclosure is not recognised as an asset that is separate from the collateralised financial instrument unless it meets the relevant recognition criteria for an asset in this or other Standards.
4. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.

*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

Mediaco Holding Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Mediaco Holding Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Chi-Square1,2,3,4 and it is concluded that the MDIA stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

### MDIA Mediaco Holding Inc. Class A Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB3Baa2
Balance SheetCCaa2
Leverage RatiosB3Ba1
Cash FlowBaa2Ba2
Rates of Return and ProfitabilityB3Caa2

*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 569 signals.

## References

1. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
2. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
3. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
4. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
6. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
7. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
Frequently Asked QuestionsQ: What is the prediction methodology for MDIA stock?
A: MDIA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Chi-Square
Q: Is MDIA stock a buy or sell?
A: The dominant strategy among neural network is to Sell MDIA Stock.
Q: Is Mediaco Holding Inc. Class A Common Stock stock a good investment?
A: The consensus rating for Mediaco Holding Inc. Class A Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of MDIA stock?
A: The consensus rating for MDIA is Sell.
Q: What is the prediction period for MDIA stock?
A: The prediction period for MDIA is (n+8 weeks)