Working Papers

WP420. From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement

Algorithmic trading has been a central theme in numerous research papers, combining knowledge from the fields of Finance and Mathematics. This thesis aimed to apply basic Technical Analysis indicators for predicting price movement of three noble metals: Gold, Silver, and Platinum in a form of multi-class classification. That task was performed using four algorithms: Logistic Regression, k-Nearest Neighbors, Random Forest and XGBoost. The study incorporated feature filtering methods such as Kendall-tau filtering and PCA, as well as five different data frequencies: 1, 5, 10, 15 and 20 trading days. From a total of 40 potential models for each metal, the best one was selected and evaluated using data from period 2018-2022. The result revealed that models utilizing only Technical Analysis indicators were able to predict price movements to a significant extent, leading to investment strategies that outperformed the market in two out of three cases.

WP352. Concept of peer-to-peer lending and application of machine learning in credit scoring

Numerous applications of AI are found in the banking sector. Starting from front-office, enhancing customer recognition and personalized services, continuing in middle-office with automated fraud-detection systems, ending with back-office and internal processes automatization. In this paper we provide comprehensive information on the phenomenon of peer-to-peer lending in the modern view of alternative finance and crowdfunding from several perspectives. The aim of this research is to explore the phenomenon of peer-to-peer lending market model. We apply and check the suitability and effectiveness of credit scorecards in the marketplace lending along with determining the appropriate cut-off point.
We conducted this research by exploring recent studies and open-source data on marketplace lending. The scorecard development is based on the P2P loans open dataset that contains repayments record along with both hard and soft features of each loan. The quantitative part consists of applying a machine learning algorithm in building a credit scorecard, namely logistic regression.

WP336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the prediction of companies bankruptcy. Some authors of scientific papers claim that the process of evaluation of the companies situation requires comparison of its characteristics defined as financial ratio with situation of whole sector in order to obtain reliable conclusions. In this paper, a hypothesis that usage of the industry benchmarks (transformation of raw financial ratios values into sectoral deciles groups numbers) improves results of bankruptcy prediction logistic regression model is verified. Based on empirical results for Polish market, it turns out that although models estimated on different types of data have similar discriminatory power, logistic regression using raw financial ratios obtained a bit better results than its industry equivalents defined as sectoral deciles groups numbers. It is worth emphasizing that empirical part of paper uses information about 109K companies what is the rarity in bankruptcy prediction papers – researchers usually use small datasets that include less than several hundred records.

WP301. A comparison of credit scoring techniques in Peer-to-Peer lending

Credit scoring has become an important issue as the competition among financial institutions becomes very intense and even a slight improvement in accuracy of prediction might translate into significant savings. Financial institutions are seeking optimal strategies through the help of credit scoring models. Therefore credit scoring tools are widely studied. As a result different parametric statistical methods, non-parametric statistical tools and soft-computing approaches have been developed in order to increase the accuracy of credit scoring models. 

In this paper different approaches to classify customers as those who pay back loan and those who default on a loan will be employed. The purpose of this study is to explore the performance of two credit scoring techniques, the logistic regression model and neural networks. In order to evaluate the feasibility and effectiveness of these methods analysis is performed on Ledning Club data. Peer-to-Peer lending, also called social lending are investigated. On the basis of the results, we can conclude that logistic regression model can provide better performance than neutral nets.