Can we predict stock market using machine learning? (LON:FTF Stock Forecast)

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. We evaluate FORESIGHT ENTERPRISE VCT PLC prediction models with Transductive Learning (ML) and Sign Test1,2,3,4 and conclude that the LON:FTF 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:FTF stock.


Keywords: LON:FTF, FORESIGHT ENTERPRISE VCT PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. How do you pick a stock?
  2. Is Target price a good indicator?
  3. What is a prediction confidence?

LON:FTF Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider FORESIGHT ENTERPRISE VCT PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:FTF 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(Sign Test)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(Transductive Learning (ML)) X S(n):→ (n+16 weeks) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of LON:FTF 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:FTF Stock Forecast (Buy or Sell) for (n+16 weeks)


Sample Set: Neural Network
Stock/Index: LON:FTF FORESIGHT ENTERPRISE VCT PLC
Time series to forecast n: 13 Nov 2022 for (n+16 weeks)

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

Adjusted IFRS* Prediction Methods for FORESIGHT ENTERPRISE VCT PLC

  1. Financial assets that are held within a business model whose objective is to hold assets in order to collect contractual cash flows are managed to realise cash flows by collecting contractual payments over the life of the instrument. That is, the entity manages the assets held within the portfolio to collect those particular contractual cash flows (instead of managing the overall return on the portfolio by both holding and selling assets). In determining whether cash flows are going to be realised by collecting the financial assets' contractual cash flows, it is necessary to consider the frequency, value and timing of sales in prior periods, the reasons for those sales and expectations about future sales activity. However sales in themselves do not determine the business model and therefore cannot be considered in isolation. Instead, information about past sales and expectations about future sales provide evidence related to how the entity's stated objective for managing the financial assets is achieved and, specifically, how cash flows are realised. An entity must consider information about past sales within the context of the reasons for those sales and the conditions that existed at that time as compared to current conditions.
  2. When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
  3. To the extent that a transfer of a financial asset does not qualify for derecognition, the transferee does not recognise the transferred asset as its asset. The transferee derecognises the cash or other consideration paid and recognises a receivable from the transferor. If the transferor has both a right and an obligation to reacquire control of the entire transferred asset for a fixed amount (such as under a repurchase agreement), the transferee may measure its receivable at amortised cost if it meets the criteria in paragraph 4.1.2.
  4. Conversely, if changes in the extent of offset indicate that the fluctuation is around a hedge ratio that is different from the hedge ratio that is currently used for that hedging relationship, or that there is a trend leading away from that hedge ratio, hedge ineffectiveness can be reduced by adjusting the hedge ratio, whereas retaining the hedge ratio would increasingly produce hedge ineffectiveness. Hence, in such circumstances, an entity must evaluate whether the hedging relationship reflects an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. If the hedge ratio is adjusted, it also affects the measurement and recognition of hedge ineffectiveness because, on rebalancing, the hedge ineffectiveness of the hedging relationship must be determined and recognised immediately before adjusting the hedging relationship in accordance with paragraph B6.5.8.

*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

FORESIGHT ENTERPRISE VCT PLC assigned short-term B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Sign Test1,2,3,4 and conclude that the LON:FTF 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:FTF stock.

Financial State Forecast for LON:FTF FORESIGHT ENTERPRISE VCT PLC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B3
Operational Risk 3967
Market Risk6031
Technical Analysis4235
Fundamental Analysis4466
Risk Unsystematic9041

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 824 signals.

References

  1. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
  2. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
  3. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
  4. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
  5. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
  6. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
  7. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
Frequently Asked QuestionsQ: What is the prediction methodology for LON:FTF stock?
A: LON:FTF stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Sign Test
Q: Is LON:FTF stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:FTF Stock.
Q: Is FORESIGHT ENTERPRISE VCT PLC stock a good investment?
A: The consensus rating for FORESIGHT ENTERPRISE VCT PLC is Hold and assigned short-term B2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:FTF stock?
A: The consensus rating for LON:FTF is Hold.
Q: What is the prediction period for LON:FTF stock?
A: The prediction period for LON:FTF is (n+16 weeks)

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