Doctoral Dissertation

Essays on the Financial Market Pattern

ANDY ALI MUSTAFA(Summary of Dissertation)

Graduate School for International Development and Cooperation
Hiroshima University
September 2022

The financial market plays a pivotal role in all aspects of human life.
However, it is well known for its non-linearity and complexity. Many experts haveapplied various methods to understand how the financial market works. Getting
such an understanding will provide critical benefits for related authorities andinvestors. It will support the related authorities in maintaining economic stabilityand the development process. On the other side, investors will have a bigger
opportunity to get a higher return on their investments. This study aims to analyse
how financial patterns change over time through machine learning and thewavelet method.
The application of the machine learning approach in finance and economics
fields flourished in recent decades. The ability to capture the nonlinearity andcomplexity of the financial market is one of the machine learning method’s
attractiveness. Nevertheless, most of the existing machine learning applications
focus on developing a better forecasting ability. Amid the huge potency of themachine learning approach, the application of machine learning beyond prediction
purposes is still limited. This study proposes a machine learning approach to get
an insightful understanding of the financial market pattern. This study constructs
an “Error Monitoring Index” to identify the possibility of structural market patternchange (called the change-candidate). Then, this study applies the “Error Profile
Analysis” to determine whether these change candidates come from temporary orpersistent changes and the exact timing of these changes. If the changes come from

persistent changes, we argue that structural changes occur in the change
candidates.
In chapter two, this study focuses on the stock market in the US and someother advanced countries (UK, German, and Japan). Utilizing the “Error
Monitoring Index” and “the Error Profile Analysis”, this study identifies fivesignificant change candidates in the US stock market from 2000 to 2021. Thesechange candidates are persistent and coincide with some critical events such as
the Global Financial Crisis in 2008 and the Covid-19 spread in 2020. But not all
significant events are associated with these changes. This study also shows similar
results for other advanced countries. These markets show some significant changes
that are persistent and coincide with some US/Global and significant domestic
events.
In chapter three, this study applies the "Error Monitoring Index" to analyze
Indonesia's financial market pattern change. This is one of the most attractive
emerging countries compared to its peers. Different from the previous chapter, theanalysis of Indonesia's financial market will focus on the two main markets, i.e.,the stock and bond markets. The examination of the "Error Monitoring Index" forIndonesia's financial market shows some change candidates that coincide withsome US/Global and major domestic events. Both the stock and bond markets
share some similar changes in terms of the timings. It shows the association
between the stock and bond market in Indonesia. Furthermore, this chapter also
discusses the variables that significantly influence the forecasting process. It

provides a clear picture of the differences between before and after a structuralchange occurs. This study focuses on the differences between pre-and post-Great
Financial Crisis (GFC) in 2008. Utilizing Permuted Feature Importance (PFI), thisstudy shows clear differences in variables' importance between pre-and post-GFC
2008.
In the third chapter, this study implements the wavelet analysis to provide
the co-movements between foreign investment flows and the perception of credit
risk (proxied by the Credit Default Swap (CDS) rate) in the Indonesian government
bond (IGB) market. More specifically, this study utilizes the partial wavelet
coherence (PWC) method. This method allows us to obtain the relationshipinformation among two variables after excluding the effect from other variables(control variables). The wavelet examination results find that foreign investment
flow and perception of sovereign credit risk co-move differently across frequencies
and times. Strong co-movements are present in the medium run from 2010 to 2016.
The beginning and the end of this co-movements period correspond to the second
round of the Fed's quantitative easing program and the introduction of a newcentral bank instrument, respectively. Moreover, the analysis also exhibits thatforeign investment flow generally leads perception of sovereign credit risk with
their negative relationship.