Çapraz Sermaye Akımları ile Borsa Endeksi Arasındaki İlişkinin Makine Öğrenmesi ile Analizi

Year 2024, Volume: 24 Issue: 1, 244 - 263, 25.03.2024

Ahmet Akusta

https://doi.org/10.11616/asbi.1393570

Abstract

Bu çalışma, çapraz sermaye akımları analizini kullanarak BIST100 hisse senedi endeksinin tahminini araştırmaktadır. Finansal zaman serilerinin tahminindeki karmaşıklıkları ele almak için öznitelik mühendisliği ve Orthogonal Matching Pursuit (OMP) modeli kullanılmıştır. Önyargısız bir model sağlamak için gecikmeli değerler, hareketli ortalamalar ve volatilite ölçümleri gibi öznitelikler titizlikle seçilmiş ve normalize edilmiştir. OMP modeli, finansal verilerin çok boyutluluğu sorununu çözmek için optimize edilmiş ve seyreklik kısıtı aracılığıyla aşırı uyumdan kaçınılmıştır. Bu yaklaşımla, endeks varyansını yakalama yeteneğini gösteren 0.88 R-kare puanı elde edilmiştir. Gerçek ve tahmin edilen değerler arasındaki görsel karşılaştırmalar, modelin doğruluğunu teyit etmektedir. Bu makale, karmaşık örüntüleri ayırt edebilen ve yatırım stratejileri için değerli içgörüler sunan modeller geliştirmede metodolojik hassasiyetin önemini vurgulamaktadır. Çalışmanın sonuçları, sermaye hareketleri ve makroekonomik değişkenlerin, finansal piyasaların karmaşıklığına rağmen Borsa Endeksi tahmini için makine öğrenimi ile iyi bir uyumlu olduğunu göstermektedir.

Keywords

Çapraz Sermaye Akımları, Hisse Senedi Endeksi Tahmini, Finansal Ekonometri, Zaman Serisi Analizi, Tahmine Dayalı Modelleme

Analysis of the Relationship Between Cross Capital Flows and Stock Exchange Index with Machine Learning

Abstract

This paper investigates forecasting the BIST100 stock index using cross-capital flow analysis. It employs feature engineering and the Orthogonal Matching Pursuit (OMP) model to navigate the intricacies of financial time series prediction. The study meticulously selects features such as lagged values, moving averages, and volatility metrics, normalized to ensure unbiased model impact. The OMP model is carefully optimized to handle the dimensionality of financial data, avoiding overfitting through a sparsity constraint. This approach yields an R-squared score of 0.88, indicating a solid capability to capture index variance. Visual comparisons between actual and predicted values further validate the model's accuracy. The paper highlights the importance of methodological precision in developing models capable of discerning complex patterns, offering valuable insights for investment strategies. Implications of the study show that cross-capital movements and macroeconomic variables are a good fit with ML to predict the Stock Market despite the complexity of financial markets.

Keywords

Cross-Capital Flows, Stock Index Forecasting, Financial Econometrics, Time Series Analysis, Predictive Modeling

References

• Aayale, J. (2017). Foreign Direct Investment and the Development of the Casablanca Stock Exchange. Proceedings of the 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017), 26, p.477–482.
• Anayochukwu, Dr. O. B. (2012). The Impact of Stock Market Returns on Foreign Portfolio Investment in Nigeria. IOSR Journal of Business and Management, 2(4), p.10–19.
• Aremo, A. G., Olabisi, O. E., & Adeboye, O. O. (2020). Effects of Selected Macroeconomic Variables on Stock Market Returns in Nigeria. Asian Journal of Economics, Business and Accounting, 16(3), 56–70.
• Asab, N. A., & Al-Tarawneh, A. (2019). Inflation Thresholds and Stock Market Development: Evidence of the Non-linear Nexus From an Emerging Economy. International Journal of Financial Research, 11(1), p.447.
• Athari, S. A., Shaeri, K., Kirikkaleli, D., Ertugrul, H. M., & Ozun, A. (2020). Global Competitiveness And Capital Flows: Does Stage Of Economic Development And Risk Rating Matter?. Asia-Pacific Journal of Accounting & Economics, 27(4), p.426–450.
• Bal, H. (2021). Determinants of Stock Market Development in Eurasian Countries. International Conference on Eurasian Economies 2021, p.130–136.
• Barakat, M. R., Elgazzar, S. H., & Hanafy, K. M. (2015). Impact of Macroeconomic Variables on Stock Markets: Evidence from Emerging Markets. International Journal of Economics and Finance, 8(1), p.195.
• Beck, R., Scheubel, B., Brüggemann, A., Moder, I., Fuentes, A., Alves, J. G., Kreitz, L., Sánchez, L. M., Den, F. Van,
• Eijking, C., Eller, M., Marsilli, C., Naef, A., Nispi Landi, V., Theofilakou, A., Wesolowski, G., Berganza, J. C., & Cezar, R. (2023). Recent Advances in the Literature on Capital Flow Management. SSRN Electronic Journal.
• Bednarek, P., Kaat, D. M. Te, Ma, C., & Rebucci, A. (2021). Capital Flows, Real Estate, and Local Cycles: Evidence from German Cities, Banks, and Firms. The Review of Financial Studies, 34(10), p.5077–5134.
• Benhima, K., & Cordonier, R. (2022). News, Sentiment And Capital Flows. Journal of International Economics, 137(7), p.103621–103654.
• Bhatt, I., Kartik, R., & Vij, N. (2020). Predicting the Uptrend and Downtrend in Stock Markets for Intraday Trading Using Machine Learning Algorithms. International Journal of Engineering Technology and Management Sciences, 6(4), p.37–40.
• Bing, T., Hu, F., & Ma, H. (2021). The Cross-Correlations Between Foreign Flows in Chinese a-Share Markets and Uncertainties in Home Markets. Discrete Dynamics in Nature and Society, p.1–16.
• Carvalho, D., & De Portugal, B. (2021). Revisiting The Relationship Between Cross-Border Capital Flows And Credit. International Finance, 24(2), p.179–218.
• Chari, A., Dilts Stedman, K., & Lundblad, C. (2020). Capital Flows in Risky Times: Risk-on/Risk-off and Emerging Market Tail Risk. National Bureau of Economic Research Working Paper Series, p.1–73.
• Chari, A., Dilts Stedman, K., & Lundblad, C. (2021). Taper Tantrums: Quantitative Easing, Its Aftermath, and Emerging Market Capital Flows. The Review of Financial Studies, 34(3), p.1445–1508.
• Dahlhaus, T., & Vasishtha, G. (2020). Monetary Policy News İn The US: Effects On Emerging Market Capital Flows. Journal of International Money and Finance, 109, p.102251–102267.
• Dai, W., & Milenković, O. (2009). Subspace Pursuit for Compressive Sensing Signal Reconstruction. Ieee Transactions on Information Theory, 55(5), p.2230–2249
• Das, D., Safa Sadiq, A., Mirjalili, S., & Noraziah, A. (2017). Hybrid Clustering-GWO-NARX neural network technique in predicting stock price. Journal of Physics: Conference Series, 892(1).
• Davis, J. S., & Zlate, A. (2023). The Global Financial Cycle And Capital Flows During The COVID-19 Pandemic. European Economic Review, 156, p.104477.
• Deng, C., Xie, J., & Zhao, X. (2023). Analysis Of The İmpact Of Global Uncertainty On Abnormal Cross-Border Capital Flows. International Review of Economics & Finance, 87, p.338–346.
• Dito, G. a., Sartono, B., & Annisa, A. (2020). Super Learner for Predicting Stock Market Trends: A Case Study of Jakarta Islamic Index Stock Exchange (ICSA 2019). European Union Digital Library.
• Donoho, D. L., Elad, M., & Temlyakov, V. (2006). Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise. Ieee Transactions on Information Theory, 52(1), p.6–18.
• Fofack, A. D., Aker, A., & Rjoub, H. (2020). Assessing The Post-Quantitative Easing Surge İn Financial Flows To Developing And Emerging Market Economies. Journal of Applied Economics, 23(1), p.89–105.
• Gao, Y., Wang, R., & Zhou, E.-M. (2021). Stock Prediction Based on Optimized LSTM and GRU Models. Scientific Programming, 1724–1734. https://doi.org/10.1155/2021/4055281
• Gazioglu, S. (2008). Stock Market Returns in an Emerging Financial Market: Turkish Case Study. Applied Economics, 40(11), p.1363–1372.
• Graf von Luckner, C., Reinhart, C., & Rogoff, K. (2023). Decrypting New Age International Capital Flows. Journal of Monetary Economics, 138, p.104–122.
• Gupta, R., Nel, J., & Pierdzioch, C. (2021). Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence From Machine Learning. Journal of Behavioral Finance, 24(1), p.111–122.
• Hajilee, M., & Nasser, O. M. A. (2015). The Relationship Between Financial Market Development and Foreign Direct Investment in Latin American Countries. The Journal of Developing Areas, 49(2), p.227–245.
• Hoque, M. E., Akhter, T., & Yakob, N. A. (2018). Revisiting endogeneity among foreign direct investment, economic growth and stock market development: Moderating role of political instability. Cogent Economics and Finance, 6(1), p.1–21.
• Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A Prediction Approach for Stock Market Volatility Based on Time Series Data. IEEE Access, 7, p.17287–17298.
• Inekwe, J. N., & Valenzuela, M. R. (2020). Financial İntegration And Banking Crisis. A Critical Analysis Of Restrictions On Capital Flows. The World Economy, 43(2), p.506–527.
• Jelilov, G., Iorember, P. T., Usman, O., & Yua, P. M. (2020). Testing the nexus between stock market returns and inflation in Nigeria: Does the effect of COVID-19 pandemic matter? Journal of Public Affairs, 20(4), p.2289.
• Kambeu, E. (2019). Trading Volume as a Predictor of Market Movement. International Journal of Finance & Banking Studies (2147-4486), 8(2), p.57–69.
• Khalid, W., & Khan, S. (2017). Effects of Macroeconomic Variables on the Stock Market Volatility: The Pakistan Experience. International Journal of Econometrics and Financial Management, 5(2), 42–59.
• Khatri Chettri, K., Bhattarai, J. K., & Gautam, R. (2023). Foreign direct investment and stock market development in Nepal. Asian Journal of Economics and Banking, 7(2), p.277–292.
• Kumawat, S. K., Bansal, A., & Saini, S. S. (2022). Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique. International Journal on Future Revolution in Computer Science & Communication Engineering, 8(4), p.09–16.
• Lee, P., Huang, Z., & Tang, Y. (2022). Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning. Security and Communication Networks, 2022, p.1–10.
• Li, H., & Huang, S. (2021). Research on the Prediction Method of Stock Price Based on RBF Neural Network Optimization Algorithm. E3S Web of Conferences, 235, 03088.
• Lin, W., & Xie, J. (2023). Research On The İnfluence Of Securities Markets On Cross-Border Capital Flows Based On The DCC-MVGARCH Model. 12510, p.318–329.
• Mallat, S. G., & Zhang, Z. (1993). Matching Pursuits With Time-Frequency Dictionaries. IEEE Transactions on Signal Processing, 41(12), p.3397–3415.
• Marcel, D., Kaat, T. E., Catão, A. V, Converse, N., Cycon, L., Dinger, V., Gonenc, H., Jeanne, O., Mann, K., Mayer, A.,
• Ongena, S., Rebucci, A., Westermann, F., & Wilde, J. (2021). Cross-Border Debt Flows and Credit Allocation: Firm-Level Evidence from the Euro Area. Journal of Money, Credit and Banking, 53(7), p.1797–1818.
• Mercado Jr, R. V, Rogelio Mercado, C. V, & East Asian, S. (2023). Bilateral Capital Flows: Gravity, Push And Pull. International Finance, 26(1), p.36–63.
• Moore, T., & Wang, P. (2014). Dynamic linkage between real exchange rates and stock prices: Evidence from developed and emerging Asian markets. International Review of Economics & Finance, 29, p.1–11.
• Nalin, L., & Yajima, G. T. (2021). Commodities Fluctuations, Cross Border Flows And Financial İnnovation: A Stock-Flow Analysis. Metroeconomica, 72(3), p.539–579.
• Phuong, L. C. M., Quynh, T. T., Vi, H. L. T., & Truc, D. T. K. (2023). Impact of Macro Factors on Stock Market Capitalization. Vnu University of Economics and Business, 3(2), p.60–120.
• Raza, S. A., Jawaid, S. T., Afshan, S., & Karim, M. Z. A. (2015). Is stock market sensitive to foreign capital inflows and economic growth?: Evidence from Pakistan. Journal of Chinese Economic and Foreign Trade Studies, 8(3), p.142–164.
• Rubinstein, R., Zibulevsky, M., & Elad, M. (2008). Efficient Implementation of the K-SVD Algorithm Using Batch Orthogonal Matching Pursuit. CS Technion, 8, p.1–15.
• Shahbaz, M., Lean, H. H., & Kalim, R. (2013). The Impact of Foreign Direct Investment on Stock Market Development: Evidence From Pakistan. Economic Research-Ekonomska Istraživanja, 26(1), p.17–32.
• Śmigiel, S. (2022). ECG Classification Using Orthogonal Matching Pursuit and Machine Learning. Sensors, 22(13), p.4960–4983.
• Sreenu, N. (2023). Effect of Exchange Rate volatility and inflation on stock market returns Dynamics - evidence from India. International Journal of System Assurance Engineering and Management, 14(3), p.836–843.
• Thesia, Y., Oza, V., & Thakkar, P. (2022). A Dynamic Scenario‐driven Technique for Stock Price Prediction and Trading. Journal of Forecasting, 41(3), p.653–674.
• Tite, O., Ogundipe, O., Ogundipe, A. A., & Akinde, M. A. (2022). Analysis of Foreign Capital Inflows and Stock Market Performance in Nigeria. Investment Management and Financial Innovations, 19(4), p.51–64.
• Utomo, S. H., Wulandari, D., Narmaditya, B. S., Handayati, P., & Ishak, S. (2019). Macroeconomic factors and LQ45 stock price index: Evidence from Indonesia. Investment Management and Financial Innovations, 16(3), p.251–259.
• Wang, X. (2021). Effect of Foreign Direct Investment on Stock Market Performance in USA. Proceedings of the 1st International Symposium on Innovative Management and Economics (ISIME 2021), 185, p.304–311.
• Xie, X., Zhang, F., Liu, L., Xie, X., & Hu, X. (2023). Assessment of Associated Credit Risk in the Supply Chain Based on Trade Credit Risk Contagion. Plos One, 18(22), p.1–20.
• Xiu, B. (2022). Based on Baidu Index and GBDT Shanghai Index Rise and Fall Forecast. BCP Business & Management, 34, p.1559–1566.
• Yang, C. (2023). Improvements of Stock Price Prediction Based on LSTM by Fama-French Five Factors on Market Capitalization. BCP Business & Management, 38, p.3429–3434.
• Zubair Mumtaz, M. (2021). Predicting Stock Indices Trends using Neuro-fuzzy systems in COVID-19. The Lahore Journal of Economics, 26(2), p.1–18.