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

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We evaluate ASGN prediction models with Active Learning (ML) and Multiple Regression^1,2,3,4 and conclude that the ASGN 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 ASGN stock.

Keywords: ASGN, ASGN, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Is now good time to invest?
2. Decision Making
3. What is prediction model?

ASGN Target Price Prediction Modeling Methodology

Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We consider ASGN Stock Decision Process with Multiple Regression where A is the set of discrete actions of ASGN 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(Multiple Regression)^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(Active Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of ASGN 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?

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

Sample Set: Neural Network
Stock/Index: ASGN ASGN
Time series to forecast n: 17 Oct 2022 for (n+8 weeks)

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

ASGN assigned short-term Ba2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Multiple Regression^1,2,3,4 and conclude that the ASGN 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 ASGN stock.

Financial State Forecast for ASGN Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	Ba1
Operational Risk	81	75
Market Risk	72	90
Technical Analysis	62	67
Fundamental Analysis	42	67
Risk Unsystematic	82	58

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 832 signals.

References

1. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
2. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
3. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
4. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
5. 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.
6. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
7. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012

Frequently Asked Questions

Q: What is the prediction methodology for ASGN stock?
A: ASGN stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is ASGN stock a buy or sell?
A: The dominant strategy among neural network is to Hold ASGN Stock.
Q: Is ASGN stock a good investment?
A: The consensus rating for ASGN is Hold and assigned short-term Ba2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of ASGN stock?
A: The consensus rating for ASGN is Hold.
Q: What is the prediction period for ASGN stock?
A: The prediction period for ASGN is (n+8 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login