Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach.** We evaluate HONEYCOMB INVESTMENT TRUST PLC prediction models with Modular Neural Network (DNN Layer) and Factor ^{1,2,3,4} and conclude that the LON:HONY stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:HONY stock.**

**LON:HONY, HONEYCOMB INVESTMENT TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Nash Equilibria
- Stock Rating
- How can neural networks improve predictions?

## LON:HONY Target Price Prediction Modeling Methodology

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We consider HONEYCOMB INVESTMENT TRUST PLC Stock Decision Process with Factor where A is the set of discrete actions of LON:HONY 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(Factor)

^{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 (DNN Layer)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:HONY 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:HONY Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:HONY HONEYCOMB INVESTMENT TRUST PLC

**Time series to forecast n: 18 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:HONY 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

HONEYCOMB INVESTMENT TRUST PLC assigned short-term Ba2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Factor ^{1,2,3,4} and conclude that the LON:HONY stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:HONY stock.**

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

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

Outlook* | Ba2 | B1 |

Operational Risk | 37 | 49 |

Market Risk | 85 | 60 |

Technical Analysis | 73 | 84 |

Fundamental Analysis | 66 | 47 |

Risk Unsystematic | 78 | 62 |

### Prediction Confidence Score

## References

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- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley

## Frequently Asked Questions

Q: What is the prediction methodology for LON:HONY stock?A: LON:HONY stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Factor

Q: Is LON:HONY stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:HONY Stock.

Q: Is HONEYCOMB INVESTMENT TRUST PLC stock a good investment?

A: The consensus rating for HONEYCOMB INVESTMENT TRUST PLC is Hold and assigned short-term Ba2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of LON:HONY stock?

A: The consensus rating for LON:HONY is Hold.

Q: What is the prediction period for LON:HONY stock?

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