Outlook: Lakeland Financial Corporation Common Stock assigned short-term B3 & long-term B3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 16 Dec 2022 for (n+1 year)
Methodology : Ensemble Learning (ML)

## Abstract

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy.(Goel, S.K., Poovathingal, B. and Kumari, N., 2016. Applications of neural networks to stock market prediction. Int Res J Eng Technol: IRJET, 3(05), pp.2192-2197.) We evaluate Lakeland Financial Corporation Common Stock prediction models with Ensemble Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the LKFN stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

## Key Points

1. Fundemental Analysis with Algorithmic Trading
2. What is a prediction confidence?
3. Nash Equilibria

## LKFN Target Price Prediction Modeling Methodology

We consider Lakeland Financial Corporation Common Stock Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LKFN 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+1 year) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LKFN 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?

## LKFN Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LKFN Lakeland Financial Corporation Common Stock
Time series to forecast n: 16 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

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 Lakeland Financial Corporation Common Stock

1. For example, when the critical terms (such as the nominal amount, maturity and underlying) of the hedging instrument and the hedged item match or are closely aligned, it might be possible for an entity to conclude on the basis of a qualitative assessment of those critical terms that the hedging instrument and the hedged item have values that will generally move in the opposite direction because of the same risk and hence that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6).
2. When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)
3. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
4. An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.

*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

Lakeland Financial Corporation Common Stock assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the LKFN stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

### Financial State Forecast for LKFN Lakeland Financial Corporation Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B3
Operational Risk 5951
Market Risk5545
Technical Analysis3236
Fundamental Analysis3848
Risk Unsystematic5845

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 657 signals.

## References

1. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
2. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
3. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
4. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
5. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
6. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
7. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
Frequently Asked QuestionsQ: What is the prediction methodology for LKFN stock?
A: LKFN stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Pearson Correlation
Q: Is LKFN stock a buy or sell?
A: The dominant strategy among neural network is to Sell LKFN Stock.
Q: Is Lakeland Financial Corporation Common Stock stock a good investment?
A: The consensus rating for Lakeland Financial Corporation Common Stock is Sell and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LKFN stock?
A: The consensus rating for LKFN is Sell.
Q: What is the prediction period for LKFN stock?
A: The prediction period for LKFN is (n+1 year)