How accurate is machine learning in stock market? (IFF Stock Forecast)

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We evaluate International Flavors & Fragrances prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Paired T-Test^1,2,3,4 and conclude that the IFF 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 IFF stock.

Keywords: IFF, International Flavors & Fragrances, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Stock Forecast Based On a Predictive Algorithm
2. Stock Rating
3. What is a prediction confidence?

IFF Target Price Prediction Modeling Methodology

In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. We consider International Flavors & Fragrances Stock Decision Process with Paired T-Test where A is the set of discrete actions of IFF 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(Paired T-Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of IFF 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?

IFF Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: IFF International Flavors & Fragrances
Time series to forecast n: 23 Sep 2022 for (n+8 weeks)

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

International Flavors & Fragrances assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Paired T-Test^1,2,3,4 and conclude that the IFF 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 IFF stock.

Financial State Forecast for IFF Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba3
Operational Risk	64	63
Market Risk	66	50
Technical Analysis	46	85
Fundamental Analysis	44	46
Risk Unsystematic	39	68

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 659 signals.

References

1. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
2. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
3. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
4. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
5. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
6. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
7. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.

Frequently Asked Questions

Q: What is the prediction methodology for IFF stock?
A: IFF stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Paired T-Test
Q: Is IFF stock a buy or sell?
A: The dominant strategy among neural network is to Hold IFF Stock.
Q: Is International Flavors & Fragrances stock a good investment?
A: The consensus rating for International Flavors & Fragrances is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of IFF stock?
A: The consensus rating for IFF is Hold.
Q: What is the prediction period for IFF stock?
A: The prediction period for IFF is (n+8 weeks)

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