Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We evaluate ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC prediction models with Inductive Learning (ML) and Logistic Regression1,2,3,4 and conclude that the LON:APEO stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:APEO stock.

Keywords: LON:APEO, ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Prediction Modeling
2. How useful are statistical predictions?
3. Dominated Move ## LON:APEO Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:APEO 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(Inductive Learning (ML)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:APEO 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:APEO Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:APEO ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC
Time series to forecast n: 18 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:APEO 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

ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Logistic Regression1,2,3,4 and conclude that the LON:APEO stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:APEO stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 8066
Market Risk6140
Technical Analysis8075
Fundamental Analysis3949
Risk Unsystematic4858

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 517 signals.

## References

1. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
2. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
3. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
4. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
5. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
6. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
7. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:APEO stock?
A: LON:APEO stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Logistic Regression
Q: Is LON:APEO stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:APEO Stock.
Q: Is ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC stock a good investment?
A: The consensus rating for ABRDN PRIVATE EQUITY OPPORTUNITIES TRUST PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:APEO stock?
A: The consensus rating for LON:APEO is Hold.
Q: What is the prediction period for LON:APEO stock?
A: The prediction period for LON:APEO is (n+6 month)