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ÖgeEmbedded information content in bonus and rights issue announcements for selected stock exchange markets(Graduate School, 2021-07-14) Işıker, Murat ; Taş, Oktay ; 403132004 ; Management ; İşletmeThe purpose of the dissertation is fourfold: First, to measure market anomaly around the bonus and rights issue announcements for selected stock exchange markets for 2010-2019. Second, to identify the motives behind the market anomaly by using issue characteristics and firm-specific factors. Third, to analyse the long-run operational performance of capital-raising firms via bonus and rights issues. Fourth, to provide policy implications to the regulatory bodies and market participants that will help to reduce the level of market inefficiency. Market reactions are calculated using event study methodology in three aspects. We analyse post and pre-announcement periods to measure event-induced anomaly and possible information leakage, respectively. Moreover, we aim to compute the magnitude of the total market anomaly around the event days by covering both periods. The market model is applied as the primary tool for expected return calculations. Since a daily time frame is preferred, the estimation period covers a trading year return data prior to the event. The main analysis surrounds the period ten days before and after the event. We also use shorter periods to control possible confounding event effects. For the multi-country level analysis, after measuring the market reaction for each country, we perform a multivariate linear regression analysis to explore the possible factors that cause the market anomaly. We use firm-specific variables as well as issue type characteristics for this purpose. Finally, we investigate the long-run operational performance of the issuers, which covers three years before and after the event. For Turkey specific analysis, we use hand-collected data derived from the Public Disclosure Platform of Turkey (PDP) to obtain announcement dates as well as other details specified within the announcement. These details convey embedded information content that is not available on other platforms. Thus, we create sub-groups using these details to reveal if any hidden factor can explain the variations on stock returns during bonus and rights issue announcements. These sub-groups are compared using one-way ANOVA and independent two-sample t-tests. We also use post-hoc tests for further examination. Our findings provide evidence regarding a positive market anomaly around bonus issue announcements in different magnitudes for all sample countries. The information leakage effect, which occurs before the announcement, is detected for all countries except Taiwan. However, the post-announcement market anomaly is valid only for India, Pakistan and Turkey. Regression findings show that issue size is the primary determinant of the abnormal returns, while the firm size and dividend yield are found significant in some cases. Profitability and leverage level indicators cannot explain the variation in abnormal returns. Moreover, long-run performance analysis results indicate that profitability after the event quarter has a downward trend, which means that the signalling assumption does not hold for the sample countries. Investment expenditures are stable generally, while there is a slightly positive trend after the event quarter for Thailand and Turkey. Turkey-specific analysis results show that when internal resources are distributed as bonus shares, the market reaction is higher than those distributed from last year's net income. In addition, internal resources sub-groups also have different characteristics. The market reaction is higher for inflation adjustment on equity group than other in-ternal resource groups. Nonetheless, the largest sized issue group, which contains issues over 75%, cause the highest abnormal returns, while a market anomaly is not detected for the smallest sized issues. Last but not least, the announcement effect regarding the initial bonus issues differs from the subsequent ones. We cannot docu-ment any significant differences among the groups that are formed according to in-dustrial classification, the timing of the issue and market sentiment. Finally, although we observe a liquidity improvement after the bonus issue, the change is not signifi-cant. The fourth chapter includes analyses on the effect of rights issue announcements. Our findings signify the presence of information asymmetry and agency problem for rights issuer firms. Also, the pecking order theory holds for most countries. Unlike the bonus issue case, we show that shareholders do not perceive these announce-ments as favourable in all countries except for Brazil and the UK. The worst perfor-mance is detected in Turkey by -5% within two days. The information leakage effect is weak for the rights issue case, which is found only in the UK. Regression findings indicate that discount rate, idiosyncratic risk level and low-growth indicator are nega-tively, and market sentiment is positively related to abnormal returns for the pooled data. Similarly, the country-specific regressions show that the discount rate is nega-tively associated with abnormal returns in all countries except for Thailand. In addi-tion, Pre-announcement returns affect market reaction positively in Turkey. Finally, the long-run analysis indicates that firms mostly used raised funds to cut their debt burden right after the event quarter. However, the debt level continues to deteriorate afterwards in all countries except France and the UK. In general, the right issues do not improve the firm's profitability, while weak and temporary investment expendi-tures are detected in Germany and Thailand. The fifth chapter provides empirical findings of Turkey-specific analysis. Take-up rate is detected 95% on average, while Turkish firms raised more than 25 billion TRY in ten years via rights issues. We document significant different market reactions for private placement (by 3%) and pure rights issue announcements (by -6%) for the (0,5) event window. We assert that the shareholder approval requirement is the major de-terminant for the difference. Also, when bonus shares are used simultaneously with rights issues, the market reaction becomes positive. On the other hand, market reac-tion is more adverse for higher-sized issues than lower-sized ones. Results fail to show a difference among groups formed according to commitment type, the use of proceeds, issue sequence and industrial classification. The dissertation is novel in examining several emerging markets, particularly regard-ing the short-run announcement effect and long-run operational performance of bo-nus issuing firms. Also, originality can be attributed to the rights issue case, where we compare several developed and developing countries together by using multiple fac-tors, including firm and issue-specific characteristics. Finally, it is the first study for Turkey that examines the detailed information retrieved from bonus and rights issue announcements to identify country-specific determinants of market anomaly by using hand-collected data.
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ÖgePortfolio optimization with wavelet analysis and neural fuzzy networks(Graduate School, 2022-12-27) Gürsoy, Ömer Zeki ; Taş, Oktay ; 403142009 ; ManagementRecently, Robo-advisors have come to the fore in the investment management business both in the world and in Turkey. Robo-advisors can be defined as financial algorithms that generate trading signals and optimize financial assets with machine learning. Robo-advisors, who perform asset management by analyzing data without human intervention, have the potential to provide better returns than traditional portfolio management. Assets under management in the Robo-Advisors segment are projected to reach US$3.2 tn in 2027. According to latest surveys, 63% of Americans are open to using a robo-advisor to manage their investments, with millennials being the most open (75%). Financial asset forecasts are of great importance in portfolio management and the performance of forecasts plays a key role in the success of portfolio managers. This situation has led to an increased interest in models. While most of the models were based on statistical techniques in the past, new modeling techniques have been used recently. The most notable of these are artificial intelligence models such as artificial neural networks and fuzzy logic. In this study, the daily values of Borsa İstanbul 30 Index, Gold and USD / TL exchange rate are tried to be estimated by using Wavelet Analysis and Neural Fuzzy Networks method. Buy / Sell signals are generated from the estimates created by the model. The performance of the portfolio was analyzed assuming that the underlying asset was invested on the days when the model predicted an increase and the investment was not made on the days when it predicted a decrease, and it was evaluated in overnight risk-free interest when not invested. In addition, the model has been tested in artificial indices and stock market indices of other developing countries. While the model showed a successful performance in Russia and China, it remained below the index in South Korea stock exchange. Then, the optimal portfolio was created by using wavelet fuzzy network model estimates and the performance of the portfolio was examined. With the return and standard deviation values produced by the model, optimization was made to obtain the largest Sharpe ratio, and the performance of the portfolio of three assets was compared with the assets' own performances and risk-free interest by re-balancing at different time intervals. The results show that the model created by using wavelet analysis and fuzzy neural networks together gives successful results in predicting the future values of financial assets and further research has potential. Wavelet Neural Network method, in which Artificial Neural Networks are used together with wavelet transform, can be used to predict the future price of assets traded in financial markets such as BIST30, Gold and USD/TL exchange rate. In the future, estimates can be made using the model in this study for financial assets other than gold, USD/TRL and BIST-30. The performance of the model in different market conditions can be tested by repeating the study at different time intervals. In the study, wavelet transform is done using Haar wavelet, financial series can be decomposed into its components by using different wavelets.