Modelling A.I. in Economics

GS^D Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg (Forecast)

Outlook: Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg assigned short-term B3 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 16 Dec 2022 for (n+3 month)
Methodology : Multi-Task Learning (ML)

Abstract

Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI).(Verma, J.P., Tanwar, S., Garg, S., Gandhi, I. and Bachani, N.H., 2019. Evaluation of pattern based customized approach for stock market trend prediction with big data and machine learning techniques. International Journal of Business Analytics (IJBAN), 6(3), pp.1-15.) We evaluate Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg prediction models with Multi-Task Learning (ML) and Ridge Regression1,2,3,4 and conclude that the GS^D stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Key Points

  1. Trust metric by Neural Network
  2. Is now good time to invest?
  3. Is it better to buy and sell or hold?

GS^D Target Price Prediction Modeling Methodology

We consider Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of GS^D 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(Ridge 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(Multi-Task Learning (ML)) X S(n):→ (n+3 month) i = 1 n a i

n:Time series to forecast

p:Price signals of GS^D 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?

GS^D Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: GS^D Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg
Time series to forecast n: 16 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 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 Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg

  1. For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
  2. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).
  3. In almost every lending transaction the creditor's instrument is ranked relative to the instruments of the debtor's other creditors. An instrument that is subordinated to other instruments may have contractual cash flows that are payments of principal and interest on the principal amount outstanding if the debtor's non-payment is a breach of contract and the holder has a contractual right to unpaid amounts of principal and interest on the principal amount outstanding even in the event of the debtor's bankruptcy. For example, a trade receivable that ranks its creditor as a general creditor would qualify as having payments of principal and interest on the principal amount outstanding. This is the case even if the debtor issued loans that are collateralised, which in the event of bankruptcy would give that loan holder priority over the claims of the general creditor in respect of the collateral but does not affect the contractual right of the general creditor to unpaid principal and other amounts due.
  4. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.

*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

Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Ridge Regression1,2,3,4 and conclude that the GS^D stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Financial State Forecast for GS^D Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B1
Operational Risk 3133
Market Risk5678
Technical Analysis5587
Fundamental Analysis3059
Risk Unsystematic7834

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 866 signals.

References

  1. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
  2. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
  3. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
  4. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
  5. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
  6. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
  7. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
Frequently Asked QuestionsQ: What is the prediction methodology for GS^D stock?
A: GS^D stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Ridge Regression
Q: Is GS^D stock a buy or sell?
A: The dominant strategy among neural network is to Hold GS^D Stock.
Q: Is Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg stock a good investment?
A: The consensus rating for Goldman Sachs Group Inc. (The) Dep Shs repstg 1/1000 Pfd Ser D Fltg is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of GS^D stock?
A: The consensus rating for GS^D is Hold.
Q: What is the prediction period for GS^D stock?
A: The prediction period for GS^D is (n+3 month)

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