Aclaris Expected to See Growth, Analysts Project

Outlook: ACRS is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

¹Short-term revised.

²Time series is updated based on short-term trends.

Key Points

Aclaris faces a challenging landscape, with predictions leaning towards potential volatility. The company's success hinges on the progress of its clinical trials and regulatory approvals for its dermatological treatments. Aclaris could experience significant stock price swings based on trial outcomes. The risk associated with these predictions includes clinical trial failures, delays in drug development, and increased competition within the dermatology market. Furthermore, any setbacks in securing partnerships or funding could negatively impact the company's financial stability and, consequently, the stock performance.

About ACRS

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ACRS Stock Forecasting Model

Our data science and economics team has developed a machine learning model to forecast the performance of Aclaris Therapeutics, Inc. (ACRS) common stock. The model leverages a comprehensive dataset, including historical price movements, trading volume, relevant macroeconomic indicators (such as inflation rates, interest rates, and overall market sentiment), and company-specific financial data. The financial data incorporated encompasses key metrics like revenue, earnings per share (EPS), debt levels, cash flow, and research & development expenditure. Furthermore, we integrate data from industry-specific reports, competitor analysis, and news sentiment analysis to capture external factors influencing ACRS's prospects. These factors are critical, especially considering the pharmaceutical industry's volatility and dependence on clinical trial outcomes and regulatory approvals.

The model employs a combination of advanced machine learning techniques. Initially, time series analysis is used to assess the underlying trends and seasonality in the stock's historical performance. We then apply ensemble methods, specifically Random Forests and Gradient Boosting Machines, known for their robust handling of complex data and their ability to mitigate overfitting. These algorithms are trained on the preprocessed dataset, after which various features are engineered, including technical indicators like moving averages, relative strength index (RSI), and MACD. The model's performance is rigorously evaluated using a validation dataset and a hold-out dataset, utilizing metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), to ensure its predictive accuracy. Furthermore, feature importance analysis is conducted to understand the influence of individual factors on the model's predictions.

The output of the model provides a probabilistic forecast for ACRS stock performance over a specific time horizon. This includes not only a predicted direction (up, down, or sideways) but also a confidence interval, allowing stakeholders to understand the range of possible outcomes. Econometric analysis complements the machine learning approach by evaluating the impact of macroeconomic variables on the company's performance. This is crucial for generating scenarios and understanding the potential risks and opportunities linked to economic conditions. The forecasting model is designed to be updated continuously with new data, and to allow for adaptation as new trends emerge. In addition, we regularly incorporate feedback from industry experts and financial analysts to improve the model's accuracy and robustness.

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ML Model Testing

F(Wilcoxon Sign-Rank Test)^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(Statistical Inference (ML))^3,4,5 X S(n):→ 1 Year $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of ACRS stock

j:Nash equilibria (Neural Network)

k:Dominated move of ACRS stock holders

a:Best response for ACRS target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do KappaSignal algorithms actually work?

ACRS Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

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%

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Rating	Short-Term	Long-Term Senior
Outlook	Ba3	Ba3
Income Statement	Baa2	Baa2
Balance Sheet	B2	Baa2
Leverage Ratios	Baa2	Caa2
Cash Flow	Ba3	Caa2
Rates of Return and Profitability	C	B2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

References

1. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
2. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
3. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
4. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
5. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
6. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
7. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA