Araştırma & geliştirme (Ar-Ge) yatırımlarının hisse fiyatları üzerine etkisi
Impact of R&D investments on stock market share price
Yazar: ÜVEYS ARIK
Danışman: DOÇ. DR. AHMET GÖNCÜ
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / İşletme Mühendisliği Ana Bilim Dalı / İşletme Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering ; İşletme = Business Administration

Özet:
Şirketlerin gerçekleştirmiş oldukları Araştırma ve Geliştirme (Ar-Ge) yatırımlarının hisse senedi fiyatları üzerindeki etkisine odaklanan çalışma, şirketlerin Ar-Ge harcamaları ile hisse senedi fiyatları arasındaki ilişkiyi inceleyecektir. Çalışmada, Ar-Ge harcamalarının şirketlerin hisse senedi fiyatları üzerindeki etkisini değerlendirmek için Borsa İstanbul'da işlem gören çeşitli sektörlerden ve iş alanlarından firmaların Ar-Ge yatırım ve hisse senedi verileri kullanılacaktır. Çalışmada farklı sektör ve iş alanlarından firmaların nicel verilerinin kullanılması, çalışma sonucunda elde edilecek bulguların güvenilirliğinin ve tutarlılığının artmasına olanak sağlayacak ve aynı zamanda farklı sektörlerdeki firmaların sektörel bazda Ar-Ge yatırımlarının hisse senedi fiyat performansı üzerindeki etkilerinin anlaşılmasına yardımcı olacaktır. Araştırma nicel bir bakış açısı ile gerçekleştirilecek olup yapılacak olan analizler panel veri analizi yöntemi kullanılarak gerçekleştirilecektir. Çalışmada Ar-Ge harcamalarının şirketlerin finansal performansı üzerindeki etkisi, hisse senedi değerlemelerindeki rolü, yatırımcı davranışları üzerindeki etkileri ve şirket imajı ve müşteri güveni üzerine etkisi  gibi konulara odaklanılacaktır. Ayrıca, Ar-Ge harcamalarının hisse senedi fiyatları üzerindeki etkisinin sektörel farklılıkları da firmalar bazında detaylı bir şekilde incelenecektir. Veri analizi sürecinde, bulguların güvenilirliğini ve anlamlılığını değerlendirmek için panel veri analizi yöntemi kullanılacaktır. Elde edilen sonuçlar, literatürdeki mevcut bilginin derinleşmesine katkıda bulunacak ve Ar-Ge harcamalarının şirketlerin hisse senedi fiyatları üzerindeki etkisinin de daha ayrıntılı bir şekilde anlaşılmasına katkı sağlayacaktır. 

Bu çalışma, Ar-Ge harcamalarının firmaların hisse senedi fiyatları üzerindeki etkisinin derinlemesine bir analizini ortaya koymaya amaçlamaktadır. Literatürde mevcut olan sınırlı ve kısıtlı bilgiyi daha da derinleştirmek ve Ar-Ge harcamalarının hisse senedi fiyatları üzerindeki etkisini daha iyi anlamak için 2016-2023 yılları arasında Borsa İstanbul'da işlem gören farklı sektör ve iş alanlarından firmaların verileri kullanılacaktır. Yapılacak olan analizlerden elde edilecek bulgular, şirketlerin Ar-Ge stratejilerini şekillendirmede ve yatırımcıların yatırım kararı alma süreçlerine ışık tutma açısından da önemli bir rol oynayacaktır. Bu çalışma, Ar-Ge harcamalarının şirketlerin hisse senedi fiyatları üzerindeki etkisine dair daha derinlemesine ve detaylı bir çalışma geliştirmek isteyen araştırmacılar, iş dünyası profesyonelleri ve yatırımcılar için değerli bir kaynak olacaktır.

Abstract:
Focusing on the impact of R&D investments on stock market share prices, the study will examine the relationship between companies' R&D expenditures and share prices. The study will use data from firms from various sectors and business areas to assess the impact of R&D expenditures on companies' share prices. The use of data of firms from different sectors and business areas in the study will help to increase the reliability of the results to be obtained and to understand the effects of R&D investments in different industries on stock share price performance. The research will be conducted from a quantitative perspective and data analysis will be conducted using panel data analysis method. The study will focus on topics such as the impact of R&D expenditures on companies' financial performance, its role in stock valuations and its effects on investor behavior. In addition, sectoral differences in the impact of R&D expenditures on stock prices will also be analyzed. In the data analysis process, panel data analysis method will be used to assess the reliability and significance of the findings. The results obtained will contribute to deepen the existing knowledge in the literature and provide a more detailed understanding of the impact of R&D expenditures on stock market share prices. 
This study aims to provide an in-depth analysis of the impact of R&D expenditures on firms' stock market share prices. In order to deepen the limited information available in the literature and to better understand the impact of R&D expenditures on share prices, data from firms from various sectors and business areas between 2016 and 2023 will be used. The findings may be important in shaping companies' R&D strategies and shedding light on investors' evaluation processes. This study can be a valuable resource for researchers, business professionals and investors who want to develop a more in-depth and detailed understanding of the impact of R&D spending on stock market share prices. Building on this foundation, the study will also explore how the relationship between R&D expenditures and stock market share prices may vary across different industries and economic conditions. 
The analysis will consider factors such as firm size, market capitalization, and industry-specific characteristics to provide a nuanced understanding of the impact. Additionally, the study will examine the long-term effects of sustained R&D investments on stock performance, offering insights into whether consistent R&D spending leads to greater market valuation over time. By incorporating these aspects, the research aims to contribute a comprehensive perspective on the strategic importance of R&D investments in enhancing shareholder value. By integrating these various dimensions, the research seeks to fill gaps in the existing literature and provide a robust framework for understanding how R&D investments influence market dynamics. The insights gained from this study could help firms make informed decisions regarding their innovation strategies and guide investors in assessing the long-term value of R&D-intensive companies. Ultimately, the findings may contribute to a deeper understanding of the interplay between innovation and financial performance.
The fact that the data used in the research differ in both time and unit dimensions has led to the choice of panel data analysis method. This method is used to accurately model these changes when the data vary over time and units. At the beginning of the study, cross-sectional dependence tests developed by Pesaran were applied to examine the cross-sectional dependence of the variables used. In time series analyses, it is of great importance whether the data are stationary or not. Violation of the stationarity assumption may lead to spurious regression problems in the analysis results, which may jeopardise the reliability of the findings. 
In order to avoid this risk, CADF (Cross-sectional Augmented Dickey-Fuller), IPS (Im, Pesaran and Shin) and LLC (Levin, Lin & Chu) unit root tests were performed to determine the stationarity of the variables used in the study. CADF (Cross-sectional Augmented Dickey-Fuller), IPS (Im, Pesaran and Shin) and LLC (Levin, Lin & Chu) tests are unit root tests for panel data and are used to assess the stationarity of time series. 
The CADF (Cross-sectional Augmented Dickey-Fuller) test is a panel version of the ADF (Augmented Dickey-Fuller) test and assesses the stationarity of each panel unit separately, taking into account dependencies between observations. The IPS (Im, Pesaran and Shin) test performs separate unit root tests for each panel unit and averages these results, taking into account heterogeneity in the panel. The LLC (Levin, Lin & Chu) test, on the other hand, calculates a single test statistic by assuming a common unit root process for all units in the panel and is mostly used in cases where homogeneity is assumed.
In addition, cointegration analyses were conducted to examine the long-run equilibrium relationships between non-stationary variables. Co-integration analysis aims to determine the existence of long-term stable relationships between different variables. These analyses play a critical role in revealing the long-run equilibrium between non-stationary series and the economic or theoretical implications of these equilibrium relationships. In this way, the results of the research become more reliable and meaningful.
In this study, Panel ARDL (Autoregressive Distributed Lag) Co-integration analysis, which is a dynamic estimation method, was preferred in order to overcome the difficulties of working with small sample data and to accurately estimate both short and long run coefficients.  In the first stage, Panel ARDL analyses are used to determine the long-run relationships between variables and to estimate the long-run coefficients. This stage plays a critical role in revealing the basic dynamics of the model and long-term equilibrium relationships. Accurate estimation of the long-run coefficients is crucial for the overall performance and validity of the model. Then, the error correction model is introduced. This model handles short-run fluctuations and deviations and analyses how the long-run equilibrium relationships are adjusted. The Error Correction Mechanism (Coefficient of Adjustment) shows how fast the system recovers from imbalances and returns to long-run equilibrium. Using the Panel ARDL (Autoregressive Distributed Lag) model, it is aimed to analyse both long and short run dynamics on different data groups. 
In this analysis, three different estimators, namely the Pooled Mean Group (PMG), Mean Group (MG) and Dynamic Fixed Effects (DFE) estimators are used. The Pooled Mean Group (PMG) Estimator is used to analyse long-run relationships in panel data, while allowing for short-run effects. This estimator assumes that long-run coefficients are common across all cross-sections, but recognises that short-run dynamics may differ in each cross-section. This approach takes into account short-run variations while ensuring the long-run stability of the model. The Mean Group (MG) Estimator frees the long-run coefficients for each unit and assumes that these coefficients may differ across cross-sections. This approach estimates long-run dynamics independently for each data group and can better reflect the heterogeneity in panel data. The MG estimator allows the long-run coefficients to be determined separately for each unit within the panel. Dynamic Fixed Effects (DFE) Estimator deals with both long-run and short-run dynamics in panel data within the framework of fixed effects. This method analyses short-run changes and long-run trends by considering fixed effects for each unit. This approach aims to obtain more accurate results by considering the fixed effects of each unit.
Hausman test is applied to evaluate the performance differences between these estimators in terms of efficiency. The Hausman test is used to determine how appropriate these estimators are and which estimator gives better results. The test reveals which model is more reliable and valid by choosing between fixed effects and random effects. This assessment plays a critical role in increasing the reliability and validity of the findings of the research.