The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. We evaluate TAYLOR MARITIME INVESTMENTS LIMITED prediction models with Reinforcement Machine Learning (ML) and Logistic Regression1,2,3,4 and conclude that the LON:TMI stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:TMI stock.

Keywords: LON:TMI, TAYLOR MARITIME INVESTMENTS LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Is it better to buy and sell or hold?
2. Operational Risk
3. How accurate is machine learning in stock market?

## LON:TMI Target Price Prediction Modeling Methodology

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. We consider TAYLOR MARITIME INVESTMENTS LIMITED Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:TMI 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(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:TMI stock

j:Nash equilibria

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?

## LON:TMI Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:TMI TAYLOR MARITIME INVESTMENTS LIMITED
Time series to forecast n: 11 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:TMI stock.

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 (Yellow to Green): *Technical Analysis%

## Conclusions

TAYLOR MARITIME INVESTMENTS LIMITED assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Logistic Regression1,2,3,4 and conclude that the LON:TMI stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:TMI stock.

### Financial State Forecast for LON:TMI Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B2
Operational Risk 3074
Market Risk5840
Technical Analysis7171
Fundamental Analysis3749
Risk Unsystematic3841

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 834 signals.

## References

1. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
2. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
3. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
4. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
5. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
6. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
7. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
Frequently Asked QuestionsQ: What is the prediction methodology for LON:TMI stock?
A: LON:TMI stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Logistic Regression
Q: Is LON:TMI stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:TMI Stock.
Q: Is TAYLOR MARITIME INVESTMENTS LIMITED stock a good investment?
A: The consensus rating for TAYLOR MARITIME INVESTMENTS LIMITED is Hold and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:TMI stock?
A: The consensus rating for LON:TMI is Hold.
Q: What is the prediction period for LON:TMI stock?
A: The prediction period for LON:TMI is (n+16 weeks)