**Outlook:**INTERNATIONAL GRAPHITE LIMITED is assigned short-term B3 & long-term Ba3 estimated rating.

**AUC Score :**

**Short-Term Revised**

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

**Time series to forecast n:** for

^{2}

**Methodology :**Modular Neural Network (Speculative Sentiment Analysis)

**Hypothesis Testing :**Statistical Hypothesis Testing

**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.

## Summary

INTERNATIONAL GRAPHITE LIMITED prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Statistical Hypothesis Testing^{1,2,3,4}and it is concluded that the IG6 stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for speculative sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.

**According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy**

## Key Points

- Reaction Function
- Technical Analysis with Algorithmic Trading
- What is the best way to predict stock prices?

## IG6 Target Price Prediction Modeling Methodology

We consider INTERNATIONAL GRAPHITE LIMITED Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of IG6 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(Statistical Hypothesis Testing)

^{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 (Speculative Sentiment Analysis)) X S(n):→ 3 Month $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of IG6 stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Modular Neural Network (Speculative Sentiment Analysis)

A modular neural network (MNN) is a type of artificial neural network that can be used for speculative sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.### Statistical Hypothesis Testing

Statistical hypothesis testing is a process used to determine whether there is enough evidence to support a claim about a population based on a sample. The process involves making two hypotheses, a null hypothesis and an alternative hypothesis, and then collecting data and using statistical tests to determine which hypothesis is more likely to be true. The null hypothesis is the statement that there is no difference between the population and the sample. The alternative hypothesis is the statement that there is a difference between the population and the sample. The statistical test is used to calculate a p-value, which is the probability of obtaining the observed data or more extreme data if the null hypothesis is true. A p-value of less than 0.05 is typically considered to be statistically significant, which means that there is less than a 5% chance of obtaining the observed data or more extreme data if the null hypothesis is true.

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?

## IG6 Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**IG6 INTERNATIONAL GRAPHITE LIMITED

**Time series to forecast:**3 Month

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

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 Modular Neural Network (Speculative Sentiment Analysis) based IG6 Stock Prediction Model

- However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
- An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
- Sales that occur for other reasons, such as sales made to manage credit concentration risk (without an increase in the assets' credit risk), may also be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows. In particular, such sales may be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows if those sales are infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). If more than an infrequent number of such sales are made out of a portfolio and those sales are more than insignificant in value (either individually or in aggregate), the entity needs to assess whether and how such sales are consistent with an objective of collecting contractual cash flows. Whether a third party imposes the requirement to sell the financial assets, or that activity is at the entity's discretion, is not relevant to this assessment. An increase in the frequency or value of sales in a particular period is not necessarily inconsistent with an objective to hold financial assets in order to collect contractual cash flows, if an entity can explain the reasons for those sales and demonstrate why those sales do not reflect a change in the entity's business model. In addition, sales may be consistent with the objective of holding financial assets in order to collect contractual cash flows if the sales are made close to the maturity of the financial assets and the proceeds from the sales approximate the collection of the remaining contractual cash flows.
- 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.

### IG6 INTERNATIONAL GRAPHITE LIMITED Financial Analysis*

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

Outlook* | B3 | Ba3 |

Income Statement | Baa2 | B3 |

Balance Sheet | B1 | B3 |

Leverage Ratios | Caa2 | Baa2 |

Cash Flow | C | Caa2 |

Rates of Return and Profitability | C | Baa2 |

*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?

## Conclusions

INTERNATIONAL GRAPHITE LIMITED is assigned short-term B3 & long-term Ba3 estimated rating. INTERNATIONAL GRAPHITE LIMITED prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Statistical Hypothesis Testing^{1,2,3,4} and it is concluded that the IG6 stock is predictable in the short/long term. ** According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy**

### Prediction Confidence Score

## References

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- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79

## Frequently Asked Questions

Q: What is the prediction methodology for IG6 stock?A: IG6 stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Statistical Hypothesis Testing

Q: Is IG6 stock a buy or sell?

A: The dominant strategy among neural network is to Buy IG6 Stock.

Q: Is INTERNATIONAL GRAPHITE LIMITED stock a good investment?

A: The consensus rating for INTERNATIONAL GRAPHITE LIMITED is Buy and is assigned short-term B3 & long-term Ba3 estimated rating.

Q: What is the consensus rating of IG6 stock?

A: The consensus rating for IG6 is Buy.

Q: What is the prediction period for IG6 stock?

A: The prediction period for IG6 is 3 Month