Modelling A.I. in Economics

SFE Safeguard Scientifics Inc. Common Stock

Outlook: Safeguard Scientifics Inc. Common Stock assigned short-term Baa2 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 12 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. (Pang, X., Zhou, Y., Wang, P., Lin, W. and Chang, V., 2020. An innovative neural network approach for stock market prediction. The Journal of Supercomputing, 76(3), pp.2098-2118.) We evaluate Safeguard Scientifics Inc. Common Stock prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Factor1,2,3,4 and conclude that the SFE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Key Points

  1. Can machine learning predict?
  2. Prediction Modeling
  3. Why do we need predictive models?

SFE Target Price Prediction Modeling Methodology

We consider Safeguard Scientifics Inc. Common Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of SFE 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= 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 (News Feed Sentiment Analysis)) X S(n):→ (n+6 month) i = 1 n r i

n:Time series to forecast

p:Price signals of SFE 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?

SFE Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: SFE Safeguard Scientifics Inc. Common Stock
Time series to forecast n: 12 Dec 2022 for (n+6 month)

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

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 Safeguard Scientifics Inc. Common Stock

  1. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
  2. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
  3. An entity that first applies these amendments at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.31–7.2.34.
  4. For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness

*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

Safeguard Scientifics Inc. Common Stock assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Factor1,2,3,4 and conclude that the SFE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Financial State Forecast for SFE Safeguard Scientifics Inc. Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B1
Operational Risk 7839
Market Risk8475
Technical Analysis8080
Fundamental Analysis6376
Risk Unsystematic6031

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 563 signals.

References

  1. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
  2. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
  3. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
  4. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
  5. 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.
  6. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
  7. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
Frequently Asked QuestionsQ: What is the prediction methodology for SFE stock?
A: SFE stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Factor
Q: Is SFE stock a buy or sell?
A: The dominant strategy among neural network is to Hold SFE Stock.
Q: Is Safeguard Scientifics Inc. Common Stock stock a good investment?
A: The consensus rating for Safeguard Scientifics Inc. Common Stock is Hold and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of SFE stock?
A: The consensus rating for SFE is Hold.
Q: What is the prediction period for SFE stock?
A: The prediction period for SFE is (n+6 month)



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