Evidence of price discovery on the Indonesian stock exchange
Introduction
Recent studies (see Westerlund et al., 2017 and Narayan et al., 2018) have examined how price discovery in the stock exchanges evolves vis-à-vis the futures market and/or other prices. Understanding price discovery has been a subject of intense research over the last decade or so, and with the advent of good quality datasets the pursuit in understanding price discovery has continued to progress impressively. In this paper, we join recent studies investigating price discovery in emerging markets by considering Indonesia—a market about which very little is known from a price discovery perspective. In this regard, as we review below, Hande et al. (2018) is an exception. We investigate price discovery and start with an acknowledgement that our work is preliminary and will, therefore, set the foundation for additional work on Indonesia’s stock exchanges. We have stock level price data for 342 firms. The dataset is unique because this sample-treatment has not been previously attempted to understand Indonesia’s asset pricing. With our sample, these firms belong to eight sectors. We ask whether these prices contribute to pricing behaviour of the Markit iTraxx Asia index (MIAI, and, in robustness tests, a global CDX high yield (CDX HI) index). The MIAI is an index consisting of 40 (excluding Japan) which are considered the most liquid Asian entities with investment grade credit ratings.
The motivation for considering Indonesian spot prices against the Asian investment grade credit ratings (and global ratings) is as follows. First, the Indonesian economy, currently the 8th largest economy, is expected to grow in size and scale to become the fourth largest economy in 2050, replacing Japan which is presently the fourth largest economy in the world.1 Despite this prediction, the Indonesian stock market is fragile and is at a nascent stage of development (see, Sharma et al., 2019). Given the significant position the country is going to occupy as the fourth largest economy in three decades, deepening the understanding of the equity market representing the asset factor in terms of price discovery with particular emphasis on how it is linked to the regional and global credit series. Second, apart from the theoretical link between stock market and credit risk under the structural models of default, the spot prices and the credit risk series are empirically linked in the long-run. They have been shown to share long-run (cointegrating) relationships (see Section III). This means that one price can influence another. It is this relation which drives the price discovery mechanism. Essentially, it is a question of about which of the two markets contribute to price discovery. The point is that if two markets move together over a long-run period then they should (or at least one of them should) correct any disequilibrium in prices resulting in another market. Third, Narayan et al. (2016) show that price discovery is useful in predicting asset returns. They show that this predictability can be tracked by investors in devising successful investment strategies. The message is that as long as price discovery can be ascertained to be emerging from a market (say A) that market will have information content. Just because market A can contribute to price discovery in say market B, market A immediately becomes a source of information. Investors can thus utilize the information content in market A to predict what will happen in market B. This leads us to our definition of price discovery. It is consistent with the concept of Granger causality—where one market’s price Granger causes the other market’s price—and consistent also with recent literature where the definition of price discovery has been coined to measure evidence of information content (see Narayan et al., 2016). This paper also relates to the recent studies that focus on the Indonesian economy in general and financial markets specifically (see, for example, Risvi et al., 2019; Narayan et al. 2019; Sharma, 2019; Thuraisamy, 2019).
Our dataset is a rich panel of firms having pricing data over time. This dataset is compiled specifically to test the hypothesis that price discovery exists in these two markets. The panel data set requires a panel price discovery methodology. The well-known and widely used price discovery methods of Hasbrouch (1995) and Granger and Gonzalo (GG, 1995) are time-series approaches. Recently, Karabiyik et al. (2018) developed a panel version of the Hasbrouch and GG tests, which are ideal given our research question. This panel price discovery method has been used to study price discovery in spot and futures markets by Hande et al. (2018). We apply the panel versions of the price discovery tests and show that price discovery in the Indonesian spot market is contributed by the MIAI. This is a fresh revelation because Indonesia does not have an active futures market. The closest market that Indonesian investors can access is the Asian market (MIAI). We show this to be the case because whatever happens in that market has price discovery implications for the Indonesian stock exchange. We show that these results are robust to a different measure of credit risk.
Our study contributes to recent attempts to understand price discovery. In this literature, the study that comes closest to our inquiry is Hande et al. (2018), who test for price discovery in 19 countries including Indonesia. They utilize monthly stock level price data and test whether it contributes to price discovery on the corresponding country-specific futures price. In the case of Indonesia, they use the SGX MSCI Indonesian Futures Index and their sample includes 49 stocks. They show that the Indonesian spot market dominates the price discovery process. We extend this analysis to, consistent with the proposal of Narayan et al. (2014), testing whether price discovery exists when the Indonesian spot market is pitched against credit risk markets. Our motivation is strong and rooted in both theory and practice; see Section II.
Our second contribution is to the literature on Indonesia’s stock market. Indonesia is an emerging market about which there is limited research. The literature that exists can be summarized as follows. The co-movement of Indonesia’s stock market has been tested by Jiang et al. (2017). They show strong relation between Indonesian stock prices to those of Thailand and the Philippines. This evidence is corroborated in the work of Korkmaz et al. (2012). Volatility of Indonesia’s stock market is demonstrated by Henker and Husodo (2010). Stock market convergence has been tested by Chien et al. (2015), who show that Indonesia’s stock market adjusts to shocks to its own market and not to other ASEAN markets. Rhee and Wang (2009) show that foreign institutional ownership negatively affects Indonesia’s stock market liquidity. The effect of Ramdan on Indonesia’s stock market is shown to have a positive effect on liquidity by Lai and Windawati (2017). Effects on liquidity from merges have been studied by Yang and Pangastuti (2016), who show that large market capitalized firms and non-financial sector firms are more efficient post-merger. Finally, on the trading front, studies have demonstrated that technical trading rules are successful on the Indonesian stock exchange (see Yu et al., 2013; Ming-Ming and Siok-Hwa, 2006; Hart et al., 2003). Our study adds a fresh insight on Indonesia’s stock exchange by showing how it is related to the credit markets, both regionally and globally. We demonstrate that whatever happens to pricing behaviour on Indonesia’s stock market is dictated by the events in the credit market. Against this background, we proceed with the paper by first discussing the methodology (section II). Our method is based on a new/recent price discovery model proposed by Karabiyik et al. (2018), which allows us model panels of stocks. We then discuss the data and results in Section III. A uniqueness of our study is the new dataset we compile. We have a panel of 342 stocks. It makes for an insightful analysis of price discovery. The results proceed from preliminary statistics—about descriptive statistics of data, unit roots, and cointegration—to price discovery and economic implications. The contents of Section IV reflect concluding remarks.
Section snippets
Further motivation and methodology
This section sets out to achieve two goals. First is to develop the motivation for a credit-risk-equity market analysis of price discovery. Second is to explain our panel data approach to computing price discovery.
Data and results
The aim of this section is to explain our dataset and results. We first explain data.
Preliminary evidence
Some selected descriptive statistics are reported in Table 1. Panel A has mean, standard deviation, skewness, and kurtosis of credit risk variables and sectoral price returns. The CDX HI is more volatility than MIAI, and five times more positively skewed with a more leptokurtic distribution. Both credit risk variables are non-normal. The sectoral returns appear heterogenous consistent with sectoral return-based studies, such as Narayan et al. (2014) and Narayan (2015) for the US data.
Concluding remarks
Indonesia’s stock market is emerging and is at a nascent stage of development compared to other emerging markets. There is limited knowledge on price discovery mechanisms involving the Indonesian market. Motivated by the Merton model of credit risk, and several recent empirical work that test the relation between stock returns and credit risk, we propose to examine price discovery between the Indonesian stock market and the credit risk market which we proxy using the Asian (excluding Japan)
References (34)
- et al.
Insider-trading in credit derivatives
J. Financ. Econ.
(2007) - et al.
Dynamic Asian stock market convergence: evidence from dynamic cointegration analysis among China and ASEAN-5
Econ. Modell.
(2015) - et al.
Noise and efficient variance in the Indonesia stock exchange
Pac. Basin Financ. J.
(2010) - et al.
Co-movement of ASEAN stock markets: new evidence from wavelet and VMD-based copula tests
Econ. Modell.
(2017) - et al.
Limited arbitrage between equity and credit markets
J. Financ. Econ.
(2012) - et al.
Return and volatility spillovers among CIVETS stock markets
Emerg. Mark. Rev.
(2012) - et al.
Risk, return and liquidity during Ramadan: evidence from Indonesian and Malaysian stock markets
Res. Int. Bus. Financ.
(2017) - et al.
The profitability of the simple moving averages and trading range breakout in the Asian stock markets
J. Asian Econ.
(2006) An analysis of sectoral equity and CDS spreads
J. Int. Financ. Mark. Inst. Money
(2015)- et al.
An analysis of price discovery from panel data models of CDS and equity returns
J. Bank. Financ.
(2014)
Price discovery and asset pricing
Pac. Basin Financ. J.
Some preliminary evidence of price discovery in Islamic banks
Pac. Basin Financ. J.
Foreign institutional ownership and stock market liquidity: evidence from Indonesia
J. Bank. Financ.
Stock market efficiency and liquidity: the Indonesian stock exchange merger
Res. Int. Bus. Financ.
Predictive ability and profitability of simple technical trading rules: recent evidence from Southeast Asian stock markets
Int. Rev. Econ. Financ.
The Long View: How Will the Global Economic Order Change by 2050?
Cointegration and error correction: representation, estimation and testing
Econometrica
Cited by (7)
Country and industry factors in tests of Capital Asset Pricing Models for partially integrated emerging markets
2020, Economic ModellingCitation Excerpt :Chiang and Chen (2016) find that US stock returns and stress in the US market dominate in explaining ESM stock return variations. Sharma et al. (2019) find that in the last ten years, the Indonesian equity risk premium is under significant influence of regional and global credit risk factors. Harvey et al. (2016) provides a comprehensive survey on various factor models in the literature.
Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm
2023, Journal of Shanghai Jiaotong University (Science)Value relevance of financial reports and macroeconomic factors in defining stock price: evidence from Indian stock markets
2022, International Journal of Business Innovation and ResearchDoes an exchange-Traded fund converge to its benchmark in the long run? evidence from ishares msci in asia-pacific countries
2021, Humanities and Social Sciences LettersDynamic Relationship between Stock Index and Asset Prices: A Long-run Analysis
2021, Journal of Asian Finance, Economics and BusinessModeling the Smart Education and Coaching to Support 'Let's Save Stocks Program'
2020, 2020 8th International Conference on Cyber and IT Service Management, CITSM 2020