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A financial early warning system for financial intermediary institutions by Neural networks
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2010., 2010.) Demircioğlu, M. K. Tahir.; Akgiray, Vedat.
The 2008 economic crises revealed that the existing financial system requires better monitoring and more effective regulations of the financial institutions. Straightforward implementation of tighter regulations will increase the costs of the financial system which will eventually hurt economic development. In order to minimize the effects of tighter regulations on the costs, regulators shall also consider taking advantage of new methods which are more complicated than existing ones. This dissertation proposes a financial early warning system for broker dealers in Turkey. Discriminant Analysis and Neural Networks are used comparatively and cooperatively to develop the model tailored for broker dealers. An extensive database is formed by Capital Adequacy Reports that were collected by Capital Markets Board for the period between 1999 and 2009. Access to this database contributed to this study in many ways through its tailored structure truly reflecting the financial standings of this industry. Popular independent variables in the literature are used and new ones are also proposed in order to take advantage of the details in the extensive database. Discriminant Analysis is used to elect the important independent variables that formed the backbone of the model, although most of the important a priori assumptions were violated. Neural Networks picked up from where Discriminant Analysis left and final model provided approximately 75% classification accuracy. Such a figure may seem low compared to similar studies. However the model predicts the deficiency in the capital adequacy, a pre-default event, which is obviously more difficult to predict than default itself.
A study on Turkish mutual funds: value creation, performance persistance and survivorship bias of actively managed Turkish equity mutual funds and the supplementary value of sell-side research to mutual fund management, 2000-2007
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2009., 2009.) Altuğ, Elif.; Akgiray, Vedat.
This study examines the value creation for investors in actively managed Turkish equity mutual funds during the eight year period between 2000 and 2007 using weekly returns. In the first and primary stage of this study, a survivorship free database is constructed and survivorship bias is calculated for different fund classes using raw fund returns, risk-adjusted fund returns, equal-weighted and size-weighted raw and risk-adjusted fund class portfolio returns. Then value creation by fund managers is evaluated using Jensen’s alpha equation for not only individual fund returns but also equal-weighted and size weighted fund portfolio returns. In addition, persistence tests are executed using parametric and nonparametric approaches. In the second and supplementary stage of this study, value creation for asset managers by analyst research is evaluated using qualitative research methods. Survivorship bias is calculated as 0.23%-1.53% for variable funds, 0.58%-6.58% for equity funds and 0.45%-3.49% for balanced funds. The magnitude of survivorship bias is higher than developed countries and the highest bias is in equity fund class. The average value of alphas calculated using different methodologies are found to be mostly negative indicating that on average funds earned less per year than they should have earned given their level of systematic risk during the study period. When fund class portfolio returns are used, there are barely positive alphas for sizeweighted equity (0.08%) and size-weighted balanced fund (0.03%) returns showing that large sized equity and balanced funds perform better than fund indices. In persistence analyses, all three fund classes are found to be persistent in the short-term and the source of persistence is persistent winners. Variable funds are significantly persistent for quarterly, annual and bi-annual periods. Equity funds are significantly persistent only for quarterly periods. Balanced funds are significantly persistent for quarterly and bi-annual periods but not for annual periods. As for the value of analyst research, it is found that buy-side professionals value sell-side research higher as long as it is well-regulated, independent and freed from motivational issues. Surprisingly, Analysis Dimension which is the first and foremost aspiration of research process is the last one in rank order showing that asset managers in Turkey do not grant value to sell-side research at face value, but selectively appreciate certain elements of it. All in all, there is partial and limited value creation in equity mutual funds in Turkey again with little supplemental value from equity research.
Analysis of financial information requirements and implementation of a financial data management system
(Thesis (M.A.)- Bogazici University. Institute for Graduate Studies in Social Sciences, 1994., 1994.) Olgun, Murat.; Akgiray, Vedat.
The requirements regarding the collection and use of financial information have been analyzed from a practical implementation perspective. Standard definitions and formulations 'of the theory of investment management constitute the basis for the study. The most important output of the study is a "Financial Data Management System" with functions for storing, processing, and reporting financial information for four main types of financial assets (common stocks, commodities, foreign currencies and bonds).
Contribution of Turkish stock market to global portfolios
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2004., 2004.) Onay, Ceylan.; Akgiray, Vedat.
In this research, the long term international diversification benefit of Turkish Stock market is investigated among globally and regionally constructed portfolios. The global portfolios are Developed Markets, Emerging Markets and World portfolios. The regional portfolios are Developed Europe, Emerging Europe, Asia, North America, Latin America, Pacific Rim, Middle East and G7 portfolios. In the research, mean-variance portfolio theory is employed using the dollar denominated monthly MSCI country stock index data and diversification benefit is explored in the full period as well as in the crises periods. Furthermore, due to the limitations of the mean-variance framework, Stein estimation is used to verify the findings of the study free of estimation bias. Under each optimization, the statistical significance of the findings are explored with the asset set spanning and asset set intersection tests of the Jobson and Korkie, respectively for the efficient portfolios constructed in the absence and in the presence of a riskless asset. This study specifically focused on the diversification potential of Turkish stock market. In this respect, the findings of the study reveal whether or not investment in it had been beneficial for an international investor for risk reduction purposes. It is found that despite its relatively lower correlations, over the investigation period Turkish stock market's contribution to reduce the risk of a global portfolio is negligible.``PA@`P`P`@@`@Pp@0` Pp@P`P0@P`Pp`@@`P @0` Ppp@``pP@P@`P`Pp@@@`pP@0`Ppp@P`PP0@P`Pp@@`P @@0`0Pp@``PA@`P`P`@@`@Pp@0` Pp@P`P0p@P`PpP@@`P @0` Pp@``pP@@`P`Pp@@`pPp@0`Pp@P`PP0@P`Ppp@@`P @0`0Pp@``PA@@`P`P`@@`@Pp@0` Pp@P`PA@P`Pp`@@`P @0` Ppp@``pPRp@`P`Pp@@@`pP@0`Ppp@P`PP0@P`Pp@@`P P@0`0Pp@``PA@`P`P`@@`@Pp@0` Pp@P`P0@P`PpP@@`P @0` Pp@``pP@@`P`Pp@@`pPp@0`Pp@P`PPA@P`Ppp@@`P @0`0Pp@``PA @`P`P`@@`@Pp@0` Pp@P`P0@P`Pp`@@`P @0` Ppp@``pP@@`P`Pp@@@`pP@0`Ppp@P`PP0@P`Pp@@`P @@0`0Pp@``PA@`P`P`@@`@Pp@0` Pp@P`P0p@P`PpP@@`P @0` Pp@``pP@@`P`Pp@@`pPp@0`Pp@P`PP0@P`Ppp@@`P @0`0Pp@``PA`@`P`P`
Developing a neural network system for financial prediction and an application on ISE
(Thesis (M.A.) - Bogazici University. Institute of Social Sciences, 1997., 1997.) Çalışkan, Oğuz.; Akgiray, Vedat.
After a number of successful applications in image processmg and recognition, neural networks have gained a wide-spread use and an admirable place in data processing field. Their ability in data mining, classification, generalisation and trend prediction is the key factor of which they have been extensively used in finance. As neural networks are good at learning non-linear relationships and at predicting non-random movements, they have been utilised especially in forecasting stock price movements. Even though most of the researches and the models gave unsatisfactory results, there are a few successful applications that encourage the use of neural networks in the prediction of stock price movements. In this study, it's explained how artificial neural networks are used as a method for financial forecasting. After giving a brief description of neural networks, every step of developing a neural network forecasting system is explained in detail. In this study, four models have been developed for the prediction of stock price movements, using multi-layer feed-forward neural networks. By presenting the long and short term trends of the end-of-week closing prices, weekly trade volume and the end-of-week market index to the network, the models are expected to predict the future price movements by analysing the past trends. At last, a model has been developed for the prediction of market index.
Extreme value approach in analyzing stock returns in Istanbul Stock Exchange
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2006., 2006.) Ünal, Gözde Erhan.; Akgiray, Vedat.
The study aims to model the tails of daily returns of securities being traded in ISE via techniques developed by Extreme Value Theory and compute VaR. The performances of classical VaR forecasting methods of Historical Simulation and RiskMetrics are compared with the models estimated using Peaks over Threshold(POT) approach, which is put forward by Extreme Value Theory. POT approach incorporates estimating the tail index of Generalized Pareto distributions (GPD). Aswell as having used nonparametric Hill and Dekkers estimators, also parametric Maximum Likelihood Estimate approach is applied in estimating the tail index ofGPD. VaR has been computed with these various approaches mentioned for sixstocks being traded in ISE, the ISE National 100 index, and an artificial priceweighted index. The models are classified as successful if they satisfy both criteria of unconditional and conditional coverage. Those VaR models that satisfy both criteriaof success have also been tested in terms of a Quantile Loss function. The modelsthat gave lowest loss values are preferred. Among the approaches used in the study, the models that fit Genaralized Pareto distributions to the lower tail are found to outperform the classical Historical Simulation and RiskMetrics approaches.
Factors affecting the Turkish accounting practices during the Republcian era
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2008., 2008.) Uman, Onur.; Akgiray, Vedat.
This thesis is based on the assumption that the development of Turkish accounting practices do differ from the West European and North American ones. In the countries possessing a mercantilist past, followed by an industrial stage, generally the existance of stock markets, the nature and the size of their industry, past economic crisis, and independent accounting organizations can be identified as the main factors for the development of accounting practices. Since Turkey had not possessed most of these factors (starting from 19th century till 1980s) the evolution of the above mentioned practices had to take a different route. The thesis’ hypothesis is that the Turkish accounting practices were mainly shaped by the laws instead of market forces. To support the hypothesis an analysis of the market forces in different periods of the last century has be done and it is concluded that none of them showed enough economic sophistication nor a significant size to become a force to push the development of accounting in Turkey. Instead the laws, mainly tax laws, tried to require double-entry bookkeeping from an underdeveloped economy. The emerging private sector started to challenge this model by depending more and more to modern bookkeeping and reporting, starting 1980s, and the international factors of 1990s added momentum to that change. Still, accounting practices are heavily influenced by legal procedures but their evolution is getting more and more affected by the market forces.
Financial liquidity, financing constraints and financing patterns
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2019., 2019.) Karakoç, Bahadır.; Akgiray, Vedat.
Separating firms into groups based on level of financing constraint proxied by size and stock market trading status, changes in financing patterns are investigated in times of high foreign capital flow and expansionary monetary policy. The study fulfills the need for analyzing the consequences of foreign capital flow at firm level and documenting its significance in addition to assessing the efficacy of contemporary monetary policy. Recent economic conditions significantly facilitated lending process, increasing credit supply and strengthening the access to conventional credit, and resulted in excessive borrowing both in the form of foreign and domestic currency. With such heavy burden of debt, the sector has become dependent on continuance of foreign capital entrance to maintain profitability and liquidity, while facing both exchange rate and the liquidity risks. The more severely a firm was previously challenged by financing limitations, the more it has borrowed once the limitations are removed, contributing to excessive debt burden of the economy in proportion to its previous financing constraints. Furthermore, significant changes in trade credit financing decisions are documented; as their access to bank loans is facilitated they reduced portion of interfirm credit, and increased bank financing and the supply of trade credit to smaller firms. Recent expansion in consumption and corporate sales may have motivated firms to supply more trade credit to promote sales and increase market share. Finally, monetary policy is found to be more effective on conventional credit channels than trade credit.
In search of chaos: the case of Istanbul Stock Exchange
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2009., 2009.) Kiraz, Fatih.; Akgiray, Vedat.
There is increasing interest and an ongoing debate on the return behavior of financial markets. The problem is that there is no sufficient evidence about successful modeling of stock returns yet. In fact, the possibility of a successful prediction model itself is still open for discussion. Financial time series are complex, noisy, and randomlooking. Linear modeling attempts have always failed whereas nonlinear ones have achieved only little. At this point, ‘chaos theory’ may provide some, if not all, answers we have been looking for. Finding a deterministic structure in a system implies that a successful prediction model is theoretically possible. Furthermore, being able to identify that structure’s characteristics, e.g. fractal dimension, means that such a model is practically possible as well. This thesis examines the return behavior of Istanbul Stock Exchange index (ISE100) in the light of ‘chaos theory’, which is almost totally missing in the current literature. The time period covered is the last eleven years, from 01.01.1998 to 16.12.2008. The main return series were created by adding one index level at every tenth second and then by calculating the logarithmic differences of the consecutive values. As a summary of the findings, there is yet no reason that prevents us from imagining the stock returns as different weather conditions. Successful short term predictions are theoretically possible but it becomes impossible to speak thoroughly about the long term. However, to become the true ‘meteorologist’ of the financial markets, one first has to develop an effective nonlinear noise filtering method which does not distort the original data and is still capable of thoroughly capturing the hidden signal in it. In the absence of such a good filtering method, the true ‘meteorologist’ becomes an ordinary ‘fisherman’ who has to rely on his/her luck at some point!
Long term volatility of stock returns in Turkey :|unconditional and predictive variance analyses
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2021., 2021.) Akyüz, Alp Eren.; Akgiray, Vedat.
Long term volatility of stock returns plays a major role in determining the weight of stocks in forward looking portfolios. This thesis investigates the long run stock return volatility in Turkey in two parts: i) unconditional statistics derived from rolling windows samples, ii) conditional statistics derived from a predictive variance analysis using a Bayesian Markov Chain Monte Carlo approach. Unconditional variance decreases with the investment horizon and it becomes more likely for stock returns to beat fixed interest returns. The predictive variance analysis also suggests that return volatility decreases with time. The risk-inducing effect of momentum is dominated by the risk-reducing effect of the negative correlation between the error terms of the current and expected return equations. Assuming a time-varying covariance matrix reduces the portion of predictive variance attributable to the identical and independently distributed risks. Having fewer observations increases the predictive variance estimate. Overall results suggest that it is more preferable from an investor’s perspective to make long-term investments in Borsa Istanbul.
Modelling stock market via fuzzy rule based systems
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2004., 2004.) Aksoy, Hakan.; Akgiray, Vedat.
A rule based fuzzy logic model is implemented to forecast the monthly return of the Istanbul Securities Exchange 100 (ISE100) Index by combining common stock market data analysis techniques. These are technical analysis, financial analysis and macroeconomic analysis.Starting with the technical analysis, an index level observation by using classifier systems is used as a long term input for the rule based modelling of the fuzzy logic. The negative correlation of the level of the ISE100 Index and the daily returns is obtained. The observation is statistically tested and found to be significant by using bootstrap method. A basic technical analysis rule using moving average is also modified as a short period input for the model.In the financial analysis that borrows from the methodology of the Altman's bankruptcy prediction analysis, a model for predicting next period's stock return is developed with the logistic regression by using 81 financial ratios before the factor analysis. It is shown in the ANOVA analysis that the output of the analysis is statistically significant as good companies perform better than bad companies. Thus, the weighted average of the logit score of the stocks is used to forecast the next period return of the index in the fuzzy logic model.After the calculation of the technical and financial inputs for the model, macroeconomic data is gathered in three main groups: real economy, FX market and TL market. The data is ruled and modeled within the period from 1996 to 2002 and optimized with steepest descent learning algorithm. The R-Square of the model is obtained to be better than those of other stock market return estimation models in the literature. The reasons are: a) the ISE is not a developed and an efficient market, thus, predicting future prizes are easier than the other developed markets; b) rule based fuzzy logic modelling with large enough data set improves the explanation of the future movements. Furthermore, the model is also tested for optimal investment decision in 2003. The algorithm is as follows: If the next month's predicted return of the index is positive, the model suppests to invest all of the existing money in the stock market. Otherwise, it is optimal to invest in repo only. The performance of the investor is compared according to the returns of the ISE100 Index, USD, repo and TL in the same period. Consequently the statistical comparison of the results by using the bootstrap method is promising and the model's suggestion performs better than the return of the repo and the ISE100 Index in 2003.
Smart beta approach of index base investing and the factor investing phenomenon :|the Turkish case
(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2022., 2022.) Akyol, Ali Nezih.; Akgiray, Vedat.
Traditional Capital Asset Pricing Model (CAPM) tests use a cap-weighted equity market portfolio as the market proxy for the CAPM market portfolio. A majority of these tests have found that either the CAPM relationship does not hold (a true failing of the model), or the equity market portfolio is not a good proxy of the CAPM market portfolio. Consequently, these empirical findings directly challenge the mean variance optimality of the market portfolio. As cap-weighted indexes bear a natural bias towards large-cap and overpriced stocks, they have relatively limited exposure to underpriced (i.e., value) stocks. Many index-based techniques have been introduced in recent years to overcome this bias and unlock the potential for value investing, like smart(alternative) beta index investing and factor investing. This study challenges CAPM's original conviction that a passive investor/manager can do no better than holding a market portfolio in the Turkish equity market context. According to CAPM, generating a positive alpha (abnormal positive return) through an active investment strategy is not possible, and any such achievement should be attributed to the chance factor. We challenge this conviction and use Arnott, Hsu, and Moore’s (2005) fundamental indexation (also referred to as smart or alternative) beta indexing) methodology and Ang, Goetzmann, and Schaefer’s (2009) Factor Investing approach (adapted from MSCI-Foundations of Factor Investing (2013)) alternatively. Using these alternative methodologies we tested whether a positive Jensen’s alpha generation is possible through the introduction of these new risk factors. This analysis was limited to the Turkish equity market.
Time series analysis of IMKB-30 equity market index returns and the effect of volume and volatility on returns
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2006., 2006.) Dönmez, Çetin Ali.; Akgiray, Vedat.
This thesis is done mainly to explore the time series dynamics and lead lag relationships among the Istanbul Stock Exchange (ISE) Equity Market Index, called ISE30, session to session and daily returns, volume and volatility. In addition to the well known classical definition of the returns, a new definition of return is made, namely, the returns are also calculated by using the average values. Moreover, many variables, some requiring detailed information on individual stock basis were also calculated and included in the analysis. An expectation survey aimed at answering the question of how the market trade variables affect the expectations of brokers was conducted. This survey was found to provide very interesting hints about how the expectations of the market people form in case of different combinations of return, volume and other trade data variables. A very detailed analysis of the survey results are provided in this thesis. Additionally, distributional properties of return series are analysed for the whole period spanning 1997-2005. The period is divided into three sub-periods, namely the pre-crisis period, crisis period and post-crisis period and all the analyses are repeated to see whether the distribution and the sample moments of session to session and daily returns change between different data windows. Return series were mainly modeled by using Autoregressive (AR), Moving Average (MA) and Autoregressive Moving Average (ARMA) techniques. The return series were found to possess the so called "long memory" or "persistency" problem. The long term memory property was explored in detail and the series are transformed by using the fractional integration method (ARFIMA). After an univariate time series analysis of the returns, a multivariate analysis of the returns with the trade variables were conducted by using Vector Autoregressive Model (VAR). In summary AR,MA and ARMA models were found to have little explanatory power for close to close returns. On the other hand, the returns calculated by the average values were found to have significant serial correlations, a fact that makes the AR, MA and ARMA models more useful. ARFIMA method proved to be useful in some cases, while it did not help in some others. Although the inclusion of other variables in the VAR models contributed to the explanatory power, the improvement is generally regarded to be not so prominent. Thus it can be said that changes in volume and volatility were found to have limited explanatory power with regard to the mean return for the next period, a result that is contradictory to what was implied by the expectation survey.

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