How accurate is machine learning in stock market? (LON:RICO Stock Forecast)

Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We evaluate RICOH CO LTD prediction models with Supervised Machine Learning (ML) and Lasso Regression^1,2,3,4 and conclude that the LON:RICO stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:RICO stock.

Keywords: LON:RICO, RICOH CO LTD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Dominated Move
2. Game Theory
3. Investment Risk

LON:RICO Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider RICOH CO LTD Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:RICO 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(Lasso 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(Supervised Machine Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:RICO 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:RICO Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:RICO RICOH CO LTD
Time series to forecast n: 23 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:RICO 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

RICOH CO LTD assigned short-term Caa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Lasso Regression^1,2,3,4 and conclude that the LON:RICO stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:RICO stock.

Financial State Forecast for LON:RICO Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	B2
Operational Risk	40	31
Market Risk	55	65
Technical Analysis	40	54
Fundamental Analysis	36	63
Risk Unsystematic	38	56

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 605 signals.

References

1. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
2. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
3. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
4. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
5. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
6. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
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 Questions

Q: What is the prediction methodology for LON:RICO stock?
A: LON:RICO stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Lasso Regression
Q: Is LON:RICO stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:RICO Stock.
Q: Is RICOH CO LTD stock a good investment?
A: The consensus rating for RICOH CO LTD is Hold and assigned short-term Caa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:RICO stock?
A: The consensus rating for LON:RICO is Hold.
Q: What is the prediction period for LON:RICO stock?
A: The prediction period for LON:RICO is (n+3 month)

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