Modelling A.I. in Economics

Should You Buy Now or Wait? (LON:HVPD Stock Forecast) (Forecast)

The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We evaluate HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED prediction models with Modular Neural Network (Financial Sentiment Analysis) and Linear Regression1,2,3,4 and conclude that the LON:HVPD stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:HVPD stock.


Keywords: LON:HVPD, HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. Stock Rating
  2. Is now good time to invest?
  3. Why do we need predictive models?

LON:HVPD Target Price Prediction Modeling Methodology

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 consider HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:HVPD 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(Linear Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+8 weeks) i = 1 n s i

n:Time series to forecast

p:Price signals of LON:HVPD 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:HVPD Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:HVPD HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED
Time series to forecast n: 06 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:HVPD 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

HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Linear Regression1,2,3,4 and conclude that the LON:HVPD stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:HVPD stock.

Financial State Forecast for LON:HVPD Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Baa2
Operational Risk 3964
Market Risk4389
Technical Analysis4979
Fundamental Analysis8588
Risk Unsystematic5483

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 743 signals.

References

  1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
  2. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
  3. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
  4. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
  5. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
  6. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
  7. 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
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HVPD stock?
A: LON:HVPD stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Linear Regression
Q: Is LON:HVPD stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HVPD Stock.
Q: Is HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED stock a good investment?
A: The consensus rating for HARBOURVEST GLOBAL PRIVATE EQUITY LIMITED is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:HVPD stock?
A: The consensus rating for LON:HVPD is Hold.
Q: What is the prediction period for LON:HVPD stock?
A: The prediction period for LON:HVPD is (n+8 weeks)

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