**Outlook:**Cinedigm Corp. Class A Common Stock is assigned short-term B3 & long-term B1 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Buy

**Time series to forecast n:** for

^{2}

**Methodology :**Reinforcement Machine Learning (ML)

**Hypothesis Testing :**ElasticNet Regression

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

Cinedigm Corp. Class A Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and ElasticNet Regression

^{1,2,3,4}and it is concluded that the CIDM stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

^{5}

**According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy**

## Key Points

- Can statistics predict the future?
- What is statistical models in machine learning?
- What are buy sell or hold recommendations?

## CIDM Stock Price Forecast

We consider Cinedigm Corp. Class A Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of CIDM 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}

**Sample Set:**Neural Network

**Stock/Index:**CIDM Cinedigm Corp. Class A Common Stock

**Time series to forecast:**16 Weeks

**According to price forecasts, the dominant strategy among neural network is: Buy**

^{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(Reinforcement Machine Learning (ML)) X S(n):→ 16 Weeks $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of CIDM stock

j:Nash equilibria (Neural Network)

k:Dominated move of CIDM stock holders

a:Best response for CIDM target price

Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

^{5}Elastic net regression is a type of regression analysis that combines the benefits of ridge regression and lasso regression. It is a regularized regression method that adds a penalty to the least squares objective function in order to reduce the variance of the estimates, induce sparsity in the model, and reduce overfitting. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients and the sum of the absolute values of the coefficients. The penalty terms are controlled by two parameters, called the ridge constant and the lasso constant. Elastic net regression can be used to address the problems of multicollinearity, overfitting, and sensitivity to outliers. It is a more flexible method than ridge regression or lasso regression, and it can often achieve better results.

^{6,7}

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?

### CIDM Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

**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%**

### Financial Data Adjustments for Reinforcement Machine Learning (ML) based CIDM Stock Prediction Model

- An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
- An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
- When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
- Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.

*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.

### CIDM Cinedigm Corp. Class A Common Stock Financial Analysis*

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

Outlook* | B3 | B1 |

Income Statement | B3 | C |

Balance Sheet | B2 | B3 |

Leverage Ratios | Caa2 | Ba3 |

Cash Flow | Caa2 | Ba1 |

Rates of Return and Profitability | B2 | B2 |

*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?

## References

- Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
- K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
- P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791

## Frequently Asked Questions

Q: What is the prediction methodology for CIDM stock?A: CIDM stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and ElasticNet Regression

Q: Is CIDM stock a buy or sell?

A: The dominant strategy among neural network is to Buy CIDM Stock.

Q: Is Cinedigm Corp. Class A Common Stock stock a good investment?

A: The consensus rating for Cinedigm Corp. Class A Common Stock is Buy and is assigned short-term B3 & long-term B1 estimated rating.

Q: What is the consensus rating of CIDM stock?

A: The consensus rating for CIDM is Buy.

Q: What is the prediction period for CIDM stock?

A: The prediction period for CIDM is 16 Weeks

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