**Outlook:**TITOMIC LIMITED assigned short-term Ba3 & long-term Baa2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 12 Dec 2022**for (n+3 month)

**Methodology :**Inductive Learning (ML)

## Abstract

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market.(Cheng, L.C., Huang, Y.H. and Wu, M.E., 2018, December. Applied attention-based LSTM neural networks in stock prediction. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4716-4718). IEEE.)** We evaluate TITOMIC LIMITED prediction models with Inductive Learning (ML) and Pearson Correlation ^{1,2,3,4} and conclude that the TTT stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

## Key Points

- Buy, Sell and Hold Signals
- Can we predict stock market using machine learning?
- What statistical methods are used to analyze data?

## TTT Target Price Prediction Modeling Methodology

We consider TITOMIC LIMITED Decision Process with Inductive Learning (ML) where A is the set of discrete actions of TTT 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(Inductive Learning (ML)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of TTT 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?

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

**Sample Set:**Neural Network

**Stock/Index:**TTT TITOMIC LIMITED

**Time series to forecast n: 12 Dec 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

**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%**

## Adjusted IFRS* Prediction Methods for TITOMIC LIMITED

- The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
- Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
- 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.
- Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.

*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

TITOMIC LIMITED assigned short-term Ba3 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Inductive Learning (ML) with Pearson Correlation ^{1,2,3,4} and conclude that the TTT stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for TTT TITOMIC LIMITED Options & Futures

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

Outlook* | Ba3 | Baa2 |

Operational Risk | 78 | 70 |

Market Risk | 54 | 87 |

Technical Analysis | 50 | 54 |

Fundamental Analysis | 67 | 75 |

Risk Unsystematic | 80 | 81 |

### Prediction Confidence Score

## References

- Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- 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.
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press

## Frequently Asked Questions

Q: What is the prediction methodology for TTT stock?A: TTT stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Pearson Correlation

Q: Is TTT stock a buy or sell?

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

Q: Is TITOMIC LIMITED stock a good investment?

A: The consensus rating for TITOMIC LIMITED is Buy and assigned short-term Ba3 & long-term Baa2 forecasted stock rating.

Q: What is the consensus rating of TTT stock?

A: The consensus rating for TTT is Buy.

Q: What is the prediction period for TTT stock?

A: The prediction period for TTT is (n+3 month)