Search

This paper offers a detailed analysis of the evolution of financial decision-making theories, focusing on the shift from classical finance to behavioral finance. Classical finance theories, including the Efficient Market Hypothesis and Modern Portfolio Theory, assume that investors behave rationally and that the market is efficient. However, these theories have faced criticisms highlighting the importance of considering irrational behaviors in financial markets. Behavioral finance addresses this gap by integrating psychological insights into financial decision-making. This study systematically reviews the literature on behavioral biases that affect individual investors, identifying fundamental biases and their impact on investment decisions. The analysis emphasizes the role of cognitive limitations and psychological tendencies in shaping market dynamics, influencing asset pricing, investment strategies, and market returns. The research also notes a shift in focus from market-level outcomes to the behavior of individual investors, with an increase in publications. The paper concludes that understanding investors’ biases is crucial for developing effective risk management strategies and investment recommendations, ultimately leading to improved market performance. The findings underscore the growing importance of behavioral finance in explaining investor behavior and market anomalies, highlighting areas for future research in this evolving field.

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes.

In the scientific literature, there is a lack of a systematic approach to credit risk factors. In addition, insufficient attention is still paid to analysing the macroeconomic factors of consumer loan credit risk. Thus, this research aims to evaluate the macroeconomic factors of consumer loan credit risk in Central and Eastern European countries’ banking systems. The findings of the study can be formulated as follows. After analysing scientific literature on credit risk factors, an improved and detailed (at five different levels) classification of factors influencing banking credit risk is proposed. This classification can be beneficial for more enhanced analysis of the factors influencing banking credit risk for the whole loan portfolio as well as for different types of loans, e.g., consumer loans. For quantitative evaluation of the impact of macroeconomic factors on consumer loan credit risk, the methods of panel data analysis and bivariate and multiple regressions are employed. Eleven CEE countries in the period from 2008 to 2020 are analysed. The results revealed that the aggregate of general macroeconomic condition factors is negatively related to consumer loan NPLs. Moreover, the economic growth, stock market, foreign exchange market, and institutional environment factors proved to be risk-decreasing, while credit market and bond market factors had a risk-increasing impact. The results of this research might help financial institutions manage credit risk more efficiently and also might be relevant to governments and central banks when selecting and applying fiscal and monetary policy measures. This study also makes policy recommendations.

Paprastai finansinėje literatūroje portfelio sudarymas yra traktuojamas griežtai teoriniu lygmeniu. Pagal Markowitz modelį investuotojas turi tam tikras galimybes, ir jis pasirenka geriausią portfelį, remdamasis abejingumo kreivėmis ir efektyvumo riba. Kita vertus, CAPM modelis yra pristatomas kaip išradinga idėja, kuri supaprastina ir padaro efektyvų Markowitz modelį. Daugelis teoretikų ir net praktikų apibendrina šiuos modelius kaip nepritaikomą teoriją. Tai yra tiesiog racionalus elgesys, kurio dažniausiai galima tikėtis, susipažinus su sudėtingais skaičiavimo metodais. Tyrimo tikslas - pritaikyti praktiškai minėtus optimizavimo modelius, kuriuos galėtų pakartoti kiekvienas investuotojas, t. y. pratęsti tai, ką darė tokie autoriai: Slack (1972); Merton (1973) ir vėliau Levy ir Sarnat (1982); Elton ir Gruber (1995) ir Senninga (1997). Jie teigė, kad optimalus portfelis gali būti surastas maksimizuojant nuolydį linijos, kuri jungia nerizikingos palūkanų normos tašką su efektyvia riba. Kai yra pasiekiama maksimali liestinė, tada minėta linija yra vadinama kapitalo rinkos tiese (CML), kuri yra efektyvios ribos liestinė. Tyrimo objektas - vertybinių popierių rinka. Tyrimo metodai - mokslinės literatūros analizė ir sintezė, kiekybiniai matematiniai ir statistiniai duomenų apdorojimo metodai.

This study investigates the impact of Verizon Communications Inc.'s acquisition of Yahoo on the company's stock performance, using a combination of statistical, visual, and multiscale analytical methods. The research analyzes how strategic corporate decisions, particularly acquisitions, influence short-term and long-term stock behavior. Data spanning from 2014 to 2024 was analyzed to capture pre-acquisition, acquisition, and post-acquisition trends. The methodology integrates boxplot visualizations to explore variability and outliers, quantile analysis to assess distributional characteristics, and descriptive statistics to summarize key metrics such as mean, standard deviation, skewness, and kurtosis. Tabulation analysis evaluates associations between stock returns and trading volumes, while wavelet analysis decomposes stock returns into multiple time scales to identify short-term fluctuations and long-term trends. Results reveal significant volatility during key events, such as the Yahoo acquisition in 2017 and the COVID-19 pandemic, with daily fluctuations peaking at ±6. Medium-term adjustments (4–8 days) exhibited moderate changes, and long-term trends (32-day cycles) showed structural shifts, including positive coefficients in 2017 (+0.5) and negative coefficients in 2020 (-0.6). The research highlights the heavy-tailed nature of return distributions and skewed trading volume patterns. This study provides insights into the multifaceted effects of acquisitions on stock performance, offering valuable implications for investors and corporate managers in optimizing strategic decisions.

This study examines the complex emotional and economic consequences of illness, focusing on individuals' subjective experiences and their broader societal impact. Employing a multi-level methodological approach, it combines theoretical insights with wavelet transform analysis to investigate long-term sickness trends in the UK. The findings highlight a significant increase in economically inactive individuals due to long-term sickness, driven by factors such as mental health conditions, chronic diseases, and the COVID-19 pandemic. The emotional aspects of illness are explored through their influence on relationships, personal identity, and societal attitudes, underscoring the necessity of patient-centered care that addresses both psychological and social needs. The economic analysis reveals the substantial burden of illness manifested in decreased labor force participation, rising healthcare costs, and productivity losses. Wavelet transform analysis captures both short-term variations and long-term patterns, offering valuable insights into the dynamic, non-stationary nature of sickness-related economic inactivity. This research emphasizes the critical need for proactive healthcare policies, workplace accommodations, and robust mental health support to reduce the adverse effects of illness on individuals and economies. By integrating advanced analytical methods and interdisciplinary perspectives, the study enhances our understanding of the intricate connections between illness, emotional health, and economic resilience.

This article explores the climate-related financial disclosures of Eurosystem central banks' non-monetary policy portfolios, focusing on adherence to the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD). Through an analysis of disclosures from 20 central banks in the euro area, it investigates governance structures, reporting practices, and metrics used to assess climate risks and opportunities. Findings reveal significant variability in transparency levels, particularly regarding governance and the frequency of climate issue reporting to boards. The mandatory metrics, including Weighted Average Carbon Intensity, Total Carbon Emissions, and Carbon Footprint, were commonly disclosed, while voluntary metrics remained underutilized. The study highlights gaps in standardized reporting and emphasizes the need for enhanced disclosure practices to support effective climate risk management within central banks. Recommendations include more comprehensive reporting aligned with TCFD guidelines, improved transparency in governance disclosures, and adopting additional metrics for a clearer understanding of climate-related risks. These findings contribute to the growing literature on sustainable finance, offering insights that may guide future policy development in central banks' climate risk management practices.