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This paper explores the association between a variety of trade and government policy measures of both home and trade partners introduced during the Covid-19 pandemic within the gravity-like framework by using the Turkish bilateral trade statistics at the six-digit product level from February 2020 to March 2022.

The empirical model is estimated by employing the two-way fixed effects (TWFE) estimation technique to get rid of the effects of unobserved time-invariant idiosyncratic country-product effects in the model and to evade the possible autocorrelation problem of trade measures.

Our empirical analysis suggests that lower Turkish GVC participation during this period can be attributed to lower mobilities and the lack of vaccines globally. Our analysis utilizing the different income groups of countries and technology group of sectors suggests that Turkish high-tech backward participation with developed countries can be more sensitive to any changes in Covid-19-related policy measures, whereas Turkish forward participation with both developed and developing markets can be more volatile during the pandemic because of the containment measures.

Sustaining mobility in the global production process is the key finding to sustain both backward and forward GVC linkages. In addition, enhancing a variety of partner countries is crucial for sustaining the flows of imported intermediates of the Turkish manufacturing sectors. Moreover, the sophistication of Turkish exported products can be the solution to continue the forward GVC participation even in the shock times. Given the product and partner country-level heterogeneities regarding contingency measures implemented by the governments, policymakers should carefully monitor each sub-sample separately and focus especially on enhancement in information, communication and transportation infrastructures to mitigate the contagious effect of any external shocks.

The unique monthly six-digit bilateral product-level trade dataset enables us to observe and utilize heterogeneous effects at the product, sector and partner country levels.

Introduction: Financial failure is a concept that may arise from many internal and external factors such as operational, financial, and economic items and may incur serious losses. Over-indebtedness arising from managerial misjudgments may cause high financial distress, insufficiency, and bankruptcy. In this regard, determination of effects of capital structure decisions on financial failure risk is crucial.

Aim: The main purpose of this study is to explore the relationship between capital structure decisions and financial failure risk. For this purpose, data from Borsa İstanbul (BIST) for listed food and beverage companies for the period from 2004 to 2019 is used. Another purpose of this study is to compare the financial failure models considering capital structure theories.

Method: In the study, capital structure decisions are associated with five different financial ratios; while the financial failure risk is proxied by financial failure scores of Altman (1968), Springate (1978), Ohlson (1980), Taffler (1983), and Zmijewski (1984). Therefore, five different panel data models are used for testing these hypotheses.

Findings: The results of panel data analysis reveal that capital structure decisions have statistically significant effects on financial failure risk for all models; however, those effects vary from one financial failure model to another. Also, the results show that in the models in which financial failure risk is proxied by the Altman (1968) and Taffler (1983) scores, the aggressive financial policies increase the financial failure risk. However, regarding the models in which financial failure risk is proxied by the Springate (1978), Ohlson (1980), and Zmijewski (1984) scores, aggressive financial policies decrease the financial failure risk.

Originality of the Study: To the best of our knowledge, this chapter is original and important in terms of revealing the effects of capital structure decisions on the financial failure risk and comparing the financial failure models.

Implications: The results revealed that the risk of financial failure models represented by Altman (1968) and Taffler (1983) scores are found to be statistically stronger and more successful in meeting theoretical expectations compared to other models. Therefore, it would be more appropriate to refer Altman’s (1968) and Taffler’s (1983) financial failure models in financial failure risk measurements.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

Borsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.

This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.

The most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.

Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.