Visualization and Forecasting Stocks

Abstract

Forecasting stock prices is very difficult because of noises in stock prices. This paper presents a stock ranking model to select stocks that perform well in the stock market. Unlike traditional stock price forecasting methods, this paper uses a simple stock ranking model. The model is based on transparent financial features rather than complex stock price forecasting techniques. Three financial features based on 12-to-1 momentum, short-term reversal, and 4-week volatility are used. These features are normalized to ensure comparability. The normalized features are combined to obtain a simple ranking model. The model is used to select stocks on a weekly basis. To ensure realistic performance, the model strictly follows data leakage. The model’s ranking scores at time ?are compared with stock returns from time ?to ? + 1. The weekly stock selection model is simulated by selecting the top 10 stocks based on ranking scores. An equally weighted portfolio is created. Experimental results show that the model performs well by achieving an annual return of 21.81%, Sharpe ratio of 1.32, and maximum drawdown of -16.91. The Information Coefficient is 0.0299 on average. The proposed approach demonstrates the ease, clarity, and ranking-based nature of feature models, which can effectively compete with the complexity of machine learning models while maintaining their usability and simplicity.. This work offers a useful methodology for academic research as well as real-world portfolio construction.

Introduction

Instead of forecasting individual stock prices, the system focuses on ranking stocks based on economic and financial indicators that are known to have predictive power. The main idea is to build an interpretable scoring system using three key features: momentum (medium-term trend), short-term reversal, and volatility (risk measure). These features are normalized across stocks and combined into a single score, where higher momentum increases the score while high reversal and volatility reduce it.

Stocks are ranked weekly, and the top 10 stocks are selected to form an equal-weight portfolio, which is rebalanced every week. Performance is evaluated using both predictive and financial metrics such as Information Coefficient, annual return, Sharpe ratio, volatility, and drawdown, with strict rules to avoid look-ahead bias.

The system is designed as a multi-layer pipeline including data acquisition, preprocessing, feature engineering, normalization, ranking, portfolio construction, and evaluation. Unlike black-box AI models, it emphasizes transparency, simplicity, and interpretability, allowing investors to clearly understand how each feature influences stock selection while still achieving competitive results.

Conclusion

The present study has proposed a more interpretable framework for ranking stocks across the cross-section. The framework is more focused on ranking stocks using more financially meaningful features. This would provide more practicality and accuracy to the framework. The focus of every investor is to invest in relatively stronger stocks rather than making accurate price predictions. The proposed framework uses three prominent financial indicators: medium-term momentum, short-term reversal, and short-term volatility. The framework uses these standardized financial indicators to provide a simple ranking score for each stock. A weekly portfolio strategy is proposed using the ranked stocks. The performance of the ranked stocks is evaluated using more realistic and bias-free techniques. The methodology is designed to avoid data leakages using only the historical information available until t to predict the return from t to t+1. The experiment results show that the framework provides excellent performance with simplicity and transparency. The portfolio created using the ranking model achieves a high return per year, a high Sharpe ratio, and a controlled amount of drawdown. In addition to that, the ranking score continues to maintain a constant association with the future return, as shown by the positive Information Coefficient value. The results of all these experiments show that the ranking method can perform exceptionally well without the need for complicated machine learning techniques. The research\'s interpretability is another contribution of the research. One can easily understand the financial importance of each component of the system. When compared to other machine learning techniques that lack interpretability, it is more helpful for practical use. For the sake of teaching, it is also helpful that the framework is simple. This method can also be used by other researchers to design more complicated models while retaining the characteristics of interpretability. It needs to be noted that there are some chances of enhancing the framework in the future. This includes the possibility of expanding the framework for use in other stock markets, the addition of more financial variables such as value and profitability, and the investigation of adaptive feature weighting techniques in more depth. In order to improve the performance of the framework even more, it is also possible to use a combination of machine learning techniques along with the advantages of feature-based ranking\'s interpretability. The research has shown that a simple and interpretable stock ranking system is able to perform well in locating high-performing stocks and creating substantial returns on investments.

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Copyright

Copyright © 2026 Manoj Kumar Sandhupatla , Bhargava Rama Sai Pranith Adapa, Anil Pathri, Dr. S. Sreekanth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.