Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate Digital Realty prediction models with Supervised Machine Learning (ML) and Multiple Regression1,2,3,4 and conclude that the DLR 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 DLR stock.

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

## Key Points

1. Buy, Sell and Hold Signals
2. Is Target price a good indicator?
3. Nash Equilibria ## DLR Target Price Prediction Modeling Methodology

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We consider Digital Realty Stock Decision Process with Multiple Regression where A is the set of discrete actions of DLR 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+3 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of DLR 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?

## DLR Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: DLR Digital Realty
Time series to forecast n: 14 Sep 2022 for (n+3 month)

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

Digital Realty assigned short-term Ba2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Multiple Regression1,2,3,4 and conclude that the DLR 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 DLR stock.

### Financial State Forecast for DLR Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba2
Operational Risk 8669
Market Risk3965
Technical Analysis8181
Fundamental Analysis8888
Risk Unsystematic5330

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 879 signals.

## References

1. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
2. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
3. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
4. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
5. 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.
6. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
7. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
Frequently Asked QuestionsQ: What is the prediction methodology for DLR stock?
A: DLR stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Multiple Regression
Q: Is DLR stock a buy or sell?
A: The dominant strategy among neural network is to Hold DLR Stock.
Q: Is Digital Realty stock a good investment?
A: The consensus rating for Digital Realty is Hold and assigned short-term Ba2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of DLR stock?
A: The consensus rating for DLR is Hold.
Q: What is the prediction period for DLR stock?
A: The prediction period for DLR is (n+3 month)