Price Linkages in Asian Equity Markets and the Asian Economic, Currency and Financial Crises

This paper examines the short and long-term price linkages among Asian equity markets in the period surrounding the recent Asian economic, financial and currency crises. Three developed markets (Hong Kong, Japan and Singapore) and six emerging markets (Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand) are included in the analysis. Multivariate cointegration procedures, Granger-causality tests and generalised variance decomposition analyses based on error-correction and vector autoregressive models are conducted to examine long and short-run relationships among these markets. The results indicate that there is a stationary long-run relationship and significant short-run causal linkages between the Asian equity markets. Furthermore, the long-run interrelationships have strengthened since the onset of the Asian crises. Nevertheless, lower causal relationships that exist between the developed and emerging equity markets suggest that opportunities for international portfolio diversification in Asian equity markets still exist.Financial integration, international portfolio diversification, market efficiency.

A behavioral interpretation of the nav discount puzzle in listed real estate companies

The NAV discount is a long standing puzzle in the listed real estate context. In this paper we extend the existing literature’s rational and noise trader explanations by exploring the influence of specific irrational behaviors. Based on behavioral biases identified in the stock and real estate markets, we hypothesize the existence of a relationship between lagged NAV growth and the NAV discount. The findings provide initial evidence of trend-chasing behavior between the dual real estate markets. The results have broader implications for the perception of the relationship between public and private real estate markets

Assessing the Dynamic Relationship Between Macroeconomic Factors and Stock Market Movement: Evidence from China

This paper examines short-run and long-run dynamic relationships between selected macroeconomic variables and stock prices in the China Stock Exchange proxy by Shanghai Composite Index. The data is restricted to the period for which quarterly data are available from 1992 Q1 to 2019 Q4 (112 observations) retrieved from the Federal Reserve Bank of Saint Louis, GTA-CSMAR database, and CEIC Database. The study employs unit root test, cointegration test, vector error correction estimates, and Innovation Accounting (impulse response test). A Johansen-Juselius cointegration test indicates a positive long run relationship between the Chinese stock price index and exchange rate, and a negative long run relationship with the gross domestic product, and M2 money supply. An estimated vector error correction model (VECM) suggests significant unidirectional short run causal relationships between Chinese stock market returns and money supply but not for inflation. The VECM also finds a significant long run causal relationship among the macroeconomic variables in the system. The estimated speed of adjustment indicates that the Chinese stock market converges to the equilibrium within half a year. Impulse response function analysis shows no significant relationship between China stock market returns and the macroeconomic variables. Forecast error variance decompositions suggest that 76% of the variation in Chinese stock market returns is attributable to its own shock, which implies that Chinese stock market returns are relatively independent of the macroeconomic variables in the system. Keywords: Stock Prices, Macroeconomic Variables, Cointegration, Innovation Accounting, China. JEL Classification: G15, E44, C58, O53 DOI: 10.7176/RJFA/12-6-02 Publication date:March 31st 2021

NETpred: Network-based modeling and prediction of multiple connected market indices

Market prediction plays a major role in supporting financial decisions. An emerging approach in this domain is to use graphical modeling and analysis to for prediction of next market index fluctuations. One important question in this domain is how to construct an appropriate graphical model of the data that can be effectively used by a semi-supervised GNN to predict index fluctuations. In this paper, we introduce a framework called NETpred that generates a novel heterogeneous graph representing multiple related indices and their stocks by using several stock-stock and stock-index relation measures. It then thoroughly selects a diverse set of representative nodes that cover different parts of the state space and whose price movements are accurately predictable. By assigning initial predicted labels to such a set of nodes, NETpred makes sure that the subsequent GCN model can be successfully trained using a semi-supervised learning process. The resulting model is then used to predict the stock labels which are finally aggregated to infer the labels for all the index nodes in the graph. Our comprehensive set of experiments shows that NETpred improves the performance of the state-of-the-art baselines by 3%-5% in terms of F-score measure on different well-known data sets

전이 엔트로피와 기계학습에 기반한 금융투자 실증 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 장우진.Stock markets have been studied extensively as one of the crucial fields of economy. In particular, research has been actively conducted to analyze and predict the stock market based on relationships among the dynamics of stock prices and returns. In this context, transfer entropy is a non-parametric indicator in analyzing relationships between components of a system, and has a more flexible analytical ability than correlation or Granger-causality. The study of stock price prediction is also being studied from traditional linear models to the latest machine learning models, and research on the optimal asset allocation strategy based on these studies are conducted. The purpose of this dissertation is to derive ETE based network indicator with a market explanatory power for the US stock market by using effective transfer entropy, which is mainly used in econophysics and information theory. The improvement of the performance of the stock price direction prediction through various machine learning algorithms by ETE based network indicator is also analyzed. Furthermore, we apply the prediction result of the stock price through the machine learning algorithm with ETE based network indicator to optimal portfolio strategy through the Black-Litterman model to study the practical use of the investment strategy. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and statistical explanatory power among the stocks. We found that 3 and 6 months moving windows ETEs increase in major financial crises, and that the sectors related to the financial crises have a statistical explanatory power to other sectors through the time-varying analysis of the ETE network indicators. Then, we discover that the prediction performance on the stock price direction can be improved when the ETE driven variable is integrated as a new feature in the logistic regression, multilayer perceptron, random forest, XGBoost, and long short-term memory network. Meanwhile, we suggest utilizing the adjusted accuracy derived from the risk-adjusted return in finance as a prediction performance measure. Notably, we confirm that the multilayer perceptron and long short-term memory network are more suitable for stock price prediction. Lastly, we examined the possibility for investors to develop an investment strategy that maximizes profits through the Black-Litterman model using ETE and machine learning. The characteristics of the inflow and outflow ETE network indicators with market explanatory power and the stock price direction prediction results using machine learning algorithms are applied to the investor's view of the Black-Literman model. The Black-Litterman portfolio, which applies the results of the stock price direction prediction using machine learning algorithms to the investor's view, provides a better return on risk than the market portfolio and market index, and the Black-Litterman portfolio with the ETE network indicator has the highest yield. The use of ETE and stock price prediction leads to improved return on investment, and improving predictive performance increases the return on investment. This dissertation is the first study on the optimal portfolio establishment strategy through the Black-Litterman model and stock price direction prediction using machine learning algorithm to apply ETE of information theory to the financial investment field.주식 시장은 경제 분야의 중요한 부분으로 광범위하게 연구되고 있다. 특히, 주식 시장의 구성 요소들인 주식 가격과 그 수익률의 관계를 예측하고 분석하는 연구는 투자자들이 최적 투자 전략을 세우기 위해 중요한 과업 중 하나이다. 이러한 맥락에서, 어떠한 시스템의 구성 요소들 간의 관계를 분석하는 데 있어 전이 엔트로피(Transfer entropy)는 비모수 지표로써 상관 관계나 그레인저-인과관계에 비해 요소 간 통계적 설명력을 확인하기에 용이하다. 주식 가격의 예측과 이를 통한 최적 자산 배분 전략에 대한 연구 또한 전통적인 선형 모델부터 최신의 머신 러닝 모델의 적용까지 다양하게 연구되고 있다. 본 학위논문의 목적은 경제물리학과 정보이론 분야에서 사용되는 효율적 전이 엔트로피(Effective transfer entropy, ETE)를 이용하여 미국 주식 시장에서 시장 구성 요소 간 발생하는 정보 흐름의 특징을 파악하여 시장의 특성을 나타낼 수 있는 시장 설명력 있는 ETE 기반의 네트워크 지표를 도출하고, 이 네트워크 지표의 사용이 다양한 머신 러닝 알고리즘을 통한 주가 방향 예측에서 성능 향상을 가져다 주는 지에 대해 연구한다. 나아가, 시장 설명력 있는 ETE 네트워크 지표의 구조적 특징과 머신 러닝 알고리즘을 통한 주가 방향 예측 결과를 투자자 관점을 고려한 최적 포트폴리오 구성 전략인 블랙-리터만 모형(Black-Litterman model)에 적용하여 결과적으로 정보 이론과 머신 러닝 기법을 이용한 실제 투자 전략 활용성에 대해 연구한다. 먼저, 미국 주식 시장의 주요 금융 위기들과 주식들 간의 통계적 설명력을 ETE를 통해 분석함으로써 3개월과 6개월 이동창을 기반으로 하는 ETE가 미국 주식 시장에 대해 설명력 있는 지표임을 확인했다. 해당 지표가 주요 금융 위기에서 그 값이 커지고, ETE 네트워크 지표의 시계열 분석을 통해 각 금융위기에서 해당 금융 위기와 관련된 섹터들이 다른 섹터들에 통계적 설명력이 있는 것을 확인했다. 다음으로, 로지스틱 회귀(Logistic regression, LR), 다층 퍼셉트론(Multilayer perceptron, MLP), 랜덤 포레스트(Random forest, RF), XGBoost(XGB) 및 Long short-term memory network(LSTM)의 5개 머신 러닝 알고리즘에 대해 ETE 네트워크 지표가 새로운 변수로 추가되었을 때 주가 방향 예측에 대한 예측 성능이 향상되는 것을 확인했다. 한편, 예측 모델의 예측 성능 평가에 대한 지표로 금융 분야에서 쓰이는 위험 조정 수익률로부터 도출한 수정 정확도 활용을 제안했고, 이 평가 지표를 이용한 분석을 통해 해당 5개 모델 중 MLP와 LSTM이 미국 주식 시장에 대한 주가 방향 예측에서 더 적합한 모델임을 확인했다. 마지막으로, 시장 설명력 있는 유입 및 유출 ETE 네트워크 지표의 특징과 머신 러닝 알고리즘을 이용한 주가 방향 예측 결과를 블랙-리터만 모형의 투자자 관점에 적용하여, 머신 러닝 알고리즘을 이용한 주가 방향 예측 결과를 투자자 관점에 적용한 블랙-리터만 포트폴리오는 시장 포트폴리오와 시장 인덱스보다 나은 위험 대비 수익률을 보이고, ETE 네트워크 지표를 적용한 블랙-리터만 포트폴리오는 가장 높은 수익률을 보임을 확인했다. ETE와 주가 방향 예측의 사용이 투자 수익률 향상으로 이어지고, 예측 성능을 향상시키면 투자 수익률도 함께 증가하는 결과를 활용하여 투자자들이 ETE와 머신 러닝을 활용한 블랙-리터만 모형을 통해 수익을 극대화 할 수 있는 투자 전략을 수립할 수 있는 가능성에 대해 확인했다. 본 학위논문은 정보 이론의 ETE를 금융 투자 분야에 적용할 수 있도록, 머신 러닝 알고리즘을 이용한 주가 방향 예측과 블랙-리터만 모형을 통한 최적 포트폴리오 구성 전략에 대한 첫 번째 연구이다.Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Literature Review 9 2.1 Analysis of transfer entropy . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Stock price prediction based on machine learning . . . . . . . . . . . 12 2.3 The Black-Litterman model . . . . . . . . . . . . . . . . . . . . . . . 17 Chapter 3 Effective transfer entropy analysis for the US market 21 3.1 Effective transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Data and experiment set-ups . . . . . . . . . . . . . . . . . . . . . . 24 3.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2.2 Experiment set-ups . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 Overall analysis of Effective transfer entropy . . . . . . . . . 31 3.3.2 Sector analysis of Effective transfer entropy . . . . . . . . . . 33 3.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 4 Predicting the direction of US stock prices using ETE and machine learning techniques 45 4.1 Machine learning algorithms . . . . . . . . . . . . . . . . . . . . . . . 45 4.1.1 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . 45 4.1.2 Multi-layer perceptron . . . . . . . . . . . . . . . . . . . . . . 45 4.1.3 Random forest . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1.4 Extreme gradient boosting . . . . . . . . . . . . . . . . . . . 47 4.1.5 Long short-term memory network . . . . . . . . . . . . . . . 48 4.1.6 Adjusted accuracy . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2 Data and experiment set-ups . . . . . . . . . . . . . . . . . . . . . . 51 4.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.2 Experiment set-ups . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.1 Prediction performance in different models . . . . . . . . . . 57 4.3.2 Prediction performance in different sectors . . . . . . . . . . . 66 4.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 78 Chapter 5 The Black-Litterman model for ETE and machine learning 81 5.1 The Black-Litterman model . . . . . . . . . . . . . . . . . . . . . . . 81 5.2 Data and experiment set-ups . . . . . . . . . . . . . . . . . . . . . . 84 5.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2.2 Experiment set-ups . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3.1 Prediction performance in different models and sectors . . . . 91 5.3.2 Portfolio performances for cumulative return . . . . . . . . . 91 5.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 97 Chapter 6 Conclusion 103 6.1 Contributions and Limitations . . . . . . . . . . . . . . . . . . . . . 103 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Bibliography 107 국문초록 125Docto

IMPACT OF OIL PRICE SHOCKS AND EXCHANGE RATE VOLATILITY ON STOCK MARKET BEHAVIOR IN NIGERIA

The impact of exchange rate and oil prices fluctuation on the stock market has been a subject of hot debate among researchers. This study examined the impact of both the exchange rate volatility and oil price volatility on stock market volatility in Nigeria, so as to guide policy formulation based on the fact that the nation’s economy was foreign induced and mono-cultured with heavy dependence on oil. EGARCH estimation techniques were employed to examine if either the volatility in exchange rate, oil price volatility or both experts on stock market volatility in Nigeria. The result shows that share price volatility is induced by both the exchange rate volatility and oil price volatility. Thus, it is recommended that policymakers should pursue policies that tend to stabilize the exchange rate regime on the one hand, and guarantee the net oil exporting position for the economy, that market practitioners should formulate portfolio strategies in such a way that volatility in both exchange rates and oil price will be factored in time when investment decisions are being made

Money, Interest Rate and Stock Prices: New Evidence from Singapore and The United States

This paper examines the long-term as well as short-term equilibrium relationships between the major stock indices and selected macroeconomic variables (such as money supply and interest rate) of Singapore and the United States by employing the advanced time series analysis techniques that include cointegration, Johansen multivariate cointegrated system, fractional cointegration and Granger causality. The cointegration results based on data covering the period January 1982 to December 2002 suggest that Singapore’s stock prices generally display a long- run equilibrium relationship with interest rate and money supply (M1) but a similar relationship does not hold for the United States. To capture the short-run dynamics of the relationship, we replicate the same experiments with different subsets of data representing shorter time periods. It is evident that stock markets in Singapore moved in tandem with interest rate and money supply before the Asian Crisis of 1997, but this pattern was not observed after the crisis. In the United States, stock prices were strongly cointegrated with macroeconomic variables before the 1987 equity crisis but the relationship gradually weakened and totally disappeared with the emergence of Asian Crisis that also indirectly affected the United States. The results of fractional cointegration and the Johansen multivariate system are consistent with the earlier cointegration result that both Singapore and US stock markets did possess equilibrium relationship with M1 and interest rate at the early days. However, the stability of the systems was disturbed by a series of well-known financial turbulence in the past two decades and eventually weakened for Singapore and completely disappeared for the U.S. This may imply that monetary authority may take action to respond to the asset price turbulence in order to maintain the stability of monetary economy and thus break the existing equilibrium between stock markets and macroeconomic variables like interest rate and M1. Another possible explanation is that the market became more efficient after 1997 Asian crisis. Finally, the results of Granger causality tests uncover some systematic causal relationships implying That stock market performance might be a good gauge for Central Bank’s monetary policy adjustment.

Relationship between macroeconomic variables and stock market indices: cointegration evidence from stock exchange of Singapore’s all-S sector indices

The relationship between macroeconomic variables and stock market returns is, by now, well-documented in the literature. However, a void in the literature relates to examining the cointegration between macroeconomic variables and stock market’s sector indices rather than the composite index. Thus in this paper we examine the long-term equilibrium relationships between selected macroeconomic variables and the Singapore stock market index (STI), as well as with various Singapore Exchange Sector indices—the finance index, the property index, and the hotel index. The study concludes that the Singapore’s stock market and the property index form cointegrating relationship with changes in the short and long-term interest rates, industrial production, price levels, exchange rate and money supply. Implications of the study and suggestions for future research are provide

Empirical Research on Information Transmission in the Hang Seng Index Markets: Evidence from Index Futures, Flagship Index and Finance Index

This paper investigates the price discovery mechanism in the Hang Seng Index markets. The analysis is based on the cross-market volatility spillover effects by using the daily sets of Hang Seng Index (HSI), Hang Seng Finance Index (HSFIN), and Hang Seng Index futures (HSCIS00). In order to testify the influence of 2007 financial tsunami on the volatility spillover effect, the study employs the vector autoregressive model (VAR) and the bivariate GARCH model based on the BEKK parameterization. The testing period has been divided into the pre-crisis (1 April, 2003 to 31 July, 2007) and the crisis & recovery period (1 August, 2007 to 1 April, 2013). The empirical results depict that there exists bi-directional volatility spillover effect between HSI and HSCIS00 for the whole testing period. In contrast, a strong bi-directional volatility spillover effect between HSFIN and HSCIS00 is only recognized after the outbreak of the 2007 financial crisis