389 American Journal of Economic and Management Business e-ISSN: 2835-5199 Vol. 4 No. 5 Mei 2025 Market Reactions to Sovereign Wealth Fund Formation in An Emerging Market: Price Inertia and Microstructure Volatility in Indonesia’s Danantara Reni Lestari1, Lindsay Carnelian Emily2 Universitas Trisakti, Indonesia1 Universitas Bina Nusantara, Indonesia2 Email: rereni12@gmail.com1, lindsay.emily@binus.ac.id2 Abstract This study investigates the short-term market reaction to the announcement of Indonesia’s sovereign wealth fund (SWF), Danantara, using a dual-framework approach that combines price-based and microstructure- based measures. Although sovereign wealth funds are intended to boost investor confidence and attract long-term capital, the Indonesian equity market—dominated by retail investors and characterized by institutional opacity—offers a unique testing ground. The research aims to assess whether the announcement of Danantara triggered abnormal returns or changes in trading behavior and liquidity among related state-owned enterprises (SOEs) and the broader market index (IHSG). Utilizing an event study methodology, this study measured abnormal returns (AR), cumulative abnormal returns (CAR), trading volume activity (TVA), abnormal volume (AV), and Amihud illiquidity (ILLIQ) across a ±5-day window around the event date. The findings show no significant price reactions but reveal temporary spikes in trading activity and liquidity disruption, suggesting behavioral responses driven by policy uncertainty. The implications indicate that market participants in emerging economies process reforms not through valuation changes but via speculative and liquidity-driven behavior. This emphasizes the importance of transparency and follow-through in institutional reform. The study provides a replicable analytical model for emerging markets responding to top-down economic policy shifts. Keywords: Sovereign Wealth Fund, Event Study, Market Microstructure, Emerging Market, Indonesia, Behavioral Finance INTRODUCTION As instruments for macroeconomic stabilization, intergenerational wealth management, and foreign investment attraction, sovereign wealth funds (SWFs) are now critical especially in emerging markets ((IMF), 2023). Three SWFs in Southeast Asia have gained traction in their establishment, and Sweden just finished the confirmed establishment of their SWF. In 2025, Indonesia announced the establishment of its own SWF, Danantara, after the examples of its less populous neighbors; Malaysia's Khazanah Nasional and Thailand's Thailand Future Fund have, both been mentioned to bolster governance and insulate state investments from the political process (Siregar & others, 2022). With expected assets nearing $900 billion, Danantara has the potential to rival the world's two and three largest SWF's. We did note an Australian SWF proposed, but it failed to launch, succumbing to small, competing local interests. While size may afford the center for SWF's in Danantara a chance of success, actual success depends less on those benefits and more on addressing Indonesia's complex and opaque institutional environment which continues to allow around 65% of equity market turnover to come from retail investors in Asia American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 390 (Indonesia Stock Exchange (IDX), 2023). While not indicative of genuine success, the experiences of long-established SWFs including Norway's Government Pension Fund Global, Singapore's GIC, and Abu Dhabi Investment Authority (ADIA), suggest that establishing credibility, transparency and good governance are paramount in developing long-term investor confidence ((IMF), 2022; Bortolotti et al., 2015). Earlier research has yielded mixed results regarding the impact of SWFs on capital markets. Kotter & Lel, 2011 revealed that while SWF investments themselves—rather than SWF announcements—provided positive abnormal returns all on a specific dimension of announcement study (e.g. an SWF investment means the SWF is commercial); Fernandes (2014) concluded that generic press releases devoid of short-term operational-level clarity usually fail to make a meaningful impact on valuations. Also, within emerging markets, Gupta & Sharma (2023) noted that while India’s sovereign fund announcements did not have significant observable impacts on stock prices, they did prompt short-term bursts of trading and increased liquidity variations. The Efficient Market Hypotheses (EMH) suggests that stock prices rapidly adjust to all available information (Fama, 1970). Consequently, if Danantara’s announcement is deemed value relevant, then the Indonesian market should respond with immediately observable adjustments in asset prices and changes in trading behaviors. Event-based studies (an innovation of the work by Fama, Fisher, Jensen, and Roll (Fama et al., 1969) detailed in Brown and Warner 1980, 1985) provide a robust framework for identifying stock price or trading behavior when events are unexpected and untainted (i.e. influenced) by additional news (MacKinlay, 1997; Martins & Cró, 2022). Research using event methodology on sovereign wealth funds (SWFs) has primarily examined developed markets. For example, Bortolotti et al. (2015) found that it's easier for developed markets to resolve information asymmetry with SWF investments through improved transparency, and developed markets are much more likely to find real value for all firms with SWF investments. This might not be the case for less developed markets, where there is more information asymmetry, liquidity constraints, and retail trader dominance. Fernandes and Ferreira (2013), and Morck et al., (2000) have noted, in emerging economies, we may see more significant market dislocation in microstructure measures (trading volume and illiquidity) than we do in stock returns. Overall, while studies examining SWFs are becoming more popular, little research examines the responding Southeast Asian funds available, especially to develop some market- adjusted short-term market reaction and price/microstructure behaviour. Prior studies explored either price effects (abnormal returns) or trading behaviour (volume, liquidity) without considering price and microstructure together (Amihud, 2002; Chordia et al., 2005), and none significantly examined abnormal returns and trading behaviour in emergent markets like Indonesia. This paper intends to help fill those gaps. This study will add additional knowledge to the literature in three ways. First, by studying the stock price and trading behavior around Danantara's announcement, the paper adds empirical evidence to the stock market literature in an under- explored region. Second, the research study uses event study analysis, but differs from existing studies in integrating price measures with market microstructure factors. Third, the study provides evidence of reactions at both the firm level (SOEs) and market level (IHSG) and helps to identify potential systemic effects of major institutional reforms. While existing literature on sovereign wealth funds (SWFs) has explored price reactions and trading behavior separately, there is a notable lack of research integrating both price-based and microstructure-based measures, especially within the context of Southeast Asian emerging markets. Most prior studies have concentrated on developed economies or on SWF investment Reni Lestari, Lindsay Carnelian Emily 391 actions rather than announcements. Moreover, Indonesia’s unique investor composition— dominated by retail traders—and opaque institutional environment are underexamined in empirical capital market research, leaving a critical gap in understanding how policy announcements like Danantara are processed by such markets. This study introduces a dual-dimensional framework combining price metrics (AR/CAR) with market microstructure indicators (AV, TVA, and ILLIQ) to analyze the announcement of Indonesia's Danantara SWF. Unlike previous research that focuses exclusively on pricing or volume, this research reveals that while prices remained largely inert, behavioral responses emerged strongly through trading activity and liquidity shifts. Additionally, this study is among the first to provide empirical evidence at both the firm (SOE) and aggregate (IHSG) levels, contributing to a more systemic understanding of institutional reform reactions in Southeast Asia. The purpose of addressing these questions is to shed light as to how investors in emerging markets process institutional reform undertaken by the government in the form of significant changes to SOE management. This will provide useful information to policymakers, investors, and scholars interested in the management of sovereign wealth and financial market development. This study provides practical insights for policymakers, financial analysts, and investors by demonstrating that policy credibility and implementation clarity are more influential in emerging markets than symbolic announcements. The findings underscore the role of investor behavior, particularly in retail-dominated markets, and emphasize the value of using microstructure indicators to assess market sentiment. For researchers, this dual-metric approach offers a replicable model for evaluating institutional reforms in similar emerging market contexts. RESEARCH METHODS This study implemented an event study method to investigate the effect of the announcement of Danantara, Indonesia's sovereign wealth fund, on the domestic equity market. The event study method is well suited to see how fast and efficiently a market reacts to new public information (as stipulated under the Efficient Market Hypotheses - EMH). The study furthermore incorporates a market microstructure approach of measuring alterations in trading behavior and liquidity post- announcement. By using return-based and microstructure-based measures, we provide a more complete view of how a market reacts. The event date (t = 0) is defined as 24 February 2025, the date that Danantara’s formation was officially announced. An event window of 11 trading days (t–5 to t+5) is created to capture the immediate market reaction before and after the announcement and to avoid other overlapping events. Reason of choosing 5 days to assess normal returns, an estimation window of 120 trading days (t–125 to t–6) prior to the event window is used when estimating normal returns as per basic practice (Brown & Warner, 1985). Figure 1. Estimation Period and Event Window American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 392 The data utilized in this study comprises ten stocks from state-owned companies that are part of the Danantara formation. The objective of this analysis is to examine how these stocks respond to market fluctuations. It is important to note that there are twelve state-owned companies listed on the Indonesia Stock Exchange (IDX) within the Danantara formation; however, two of these stocks are currently suspended and have therefore been excluded from this study. In addition to the selected stocks, the analysis incorporates the Indonesia Stock Market Index (IHSG) as a market proxy, along with the S&P 500 for the calculation of the IHSG return market model. The Alpha and Beta values for each stock, as well as for the market index, are calculated using daily historical data in the estimation period. Daily stock price data has been sourced from Investing.com to ensure accuracy and reliability in the analysis. Abnormal Return (AR) measurement is done to assess market reaction to new information. This study uses several steps to calculate the abnormal return variable (Rofiah et al., 2019). Abnormal returns estimated with the following equation: 𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝐸(𝑅𝑖𝑡) Where ARit is abnormal return, Rit is realized return of stock i during the period and E(Rit) is expected return of stock i during the event period t. The expected return estimated using market model with the following equation: 𝐸(𝑅)𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽𝑖𝑡 𝑅𝑚 + 𝜀𝑖 Where αi (alpha) is the intercept of the regression line for stock i, βi (beta) is the sensitivity of stock i to the market index, Rm is the return of market in aggregate, and Ɛi is the error term. Cumulative Abnormal Return (CAR) provides an overview of the accumulated impact over a period of time. A study by Brown & Warner (1985) showed that CAR is often more stable as a measure of total impact than daily AR. Cumulative Abnormal Return (CAR) estimated with the following formula: 𝐶𝐴𝑅𝑖 = ∑ 𝐴𝑅𝑖,𝑡 Where ARi,t is the Abnormal return stock i during period t. To capture changes in trading behavior and liquidity, three main metrics are used namely Abnormal Trading Volume (ATV), Trading Volume Activity (TVA), and Amihud Illiquidity Ratio (ILLIQ). Abnormal Trading Volume (AV) is used to measure how much attention or reaction investors give to new information. 𝐴𝑉𝑖,𝑡 = 𝑉𝑖,𝑡 − �̅�𝑖 �̅�𝑖 Where 𝑉𝑖,𝑡 is actual volume stock i during period t and �̅�𝑖 is average volume stock i in the estimation period. Trading Volume Activity (TVA) is used to measure the intensity of trading activity of a stock compared to the total shares outstanding. This ratio reflects the interest or attention of investors towards a particular stock during a certain period. The higher the TVA, the more actively the stock is traded (Chordia et al., 2005). 𝑇𝑉 𝐴𝑖,𝑡 = 𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑡 𝑆ℎ𝑎𝑟𝑒 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔𝑖,𝑡 To measure the impact of relative price changes on trading volume, the Amihud Illiquidity Ratio (ILLIQ) is used. The higher the ILLIQ value, the less liquid the stock is (Amihud, 2002). Reni Lestari, Lindsay Carnelian Emily 393 𝐼𝐿𝐿𝐼𝑄𝑖,𝑡 = |𝑅𝑖,𝑡| 𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑡/𝑃𝑟𝑖𝑐𝑒𝑖,𝑡 Where Ri,t is absolut value of daily return of stock i during period t. The purpose of using absolute value is to capture the intensity of price changes, not the direction of the changes. Volumei,t is trading value of stock i during period t. The collected data will be analyzed in stages through descriptive statistics and then statistical tests will be carried out through a normal distribution test using the Kolmogorov-Smirnov test. Furthermore, If the data is normally distributed, variables of this research will be tested by using one sample t-test and paired sample t-test analysis. One sample t-test is conducted to determine whether a certain variable deviates significantly from its normal level, represented by zero. While paired sample t-test conducted to compares the values of a variable before and after the event. However, if the collected data is not normally distributed, then the data is processed using non-parametric test called the Wilcoxon Signed Rank analysis test model in the robustness test section. This study employs SPSS 26 and Ms. Excel as analysis tools. RESULT AND DISCUSSION To better illustrate the characteristics of the data in the event window (t-5 to t+5), Table 1 presents descriptive statistics for the principal variables examined in this study: Abnormal Return (AR), Cumulative Abnormal Return (CAR), Abnormal Volume (AV), Trading Volume Activity (TVA), and Amihud Illiquidity Ratio (ILLIQ). These indicators capture both price precedence and market microstructure possibilities around Danantara's announcement. Table 1. Statistic Descriptive Result Variabel Mean Median Std. Dev Min Max AR -0.00196 -0.00062 0.02375 -0.08623 0.05205 CAR -0.01965 -0.01567 0.04369 -0.09952 0.03870 AV 0.06709 -0.11324 0.78045 -0.85950 3.08363 TVA 0.16154 0.14102 0.12835 0.00755 0.66401 ILLIQ 3.59756 2.84637 2.58465 1.11747 9.82390 Table 1 shows the behavior of some important variables through the event window (t-5 to t + 5). The near-zero mean Abnormal Return (AR) (−0.00196) and Cumulative Abnormal Return (CAR) (−0.01965) show little in the way of price-based reactions. This finding is consistent with some studies which showed that announcements relating to sovereign wealth funds show no effect on valuations in the short term, especially when there is no direct financial impact of the announcement (Bartram et al., 2021; Fernandes, 2014). The limited moves in prices demonstrate semi-strong efficiency in this market (Fama, 1970), where non-material information gets rapidly American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 394 priced in, and the asymmetric credibility of policy signals in developing markets has investors sitting tight and waiting to see proof of institutional follow through (Morck et al., 2000; Truman, 2008). Trading Volume Activity (TVA) demonstrated significant jumps in activity (mean = 0.1615) whereas Amihud Illiquidity (ILLIQ) (mean = 3.60) suggested a temporary deterioration to liquidity. These action points reflect emerging market analysis which have found that microstructure variables react more sharply to policy uncertainty than prices (Amihud, 2002; Bekaert et al., 2003). The asymmetric reactions to initial impact of pricing and trading raise the possibility of a proposed two-channel response to institutional announcements: while the price remained stable, the trading raised relative uncertainty levels and speculative positioning (Barber & Odean, 2008; Chordia et al., 2005). Taken together, these descriptive insights suggest the market response was behavioral and trended toward normal, rather than an actual change in asset valuations. This frames the context for the inferential tests that are to follow as it says the need for price and microstructure responses to be considered in policy-initiated event studies. Table 2. Event study result – Firm Level Variable One-sample t-stat p-value Paired t-stat p-value AR_stocks –0.7735 0.4571 –0.4554 0.6725 CAR_stocks –1.4915 0.1667 – – AV_stocks 0.4781 0.6429 –1.1181 0.3261 TVA_stocks 7.5161 0.0000* –0.9670 0.3883 ILLIQ_stocks 24.6473 0.0001* 1.2231 0.2884 After conducting the descriptive analysis, the research implemented several inferential tests with one-sample t-tests and paired sample t-tests to assess the significance of price-based and microstructure-based reactions around the announcement of Danantara. The test results are shown in Table 2. Price-Based Response: Abnormal Return (AR) and Cumulative Abnormal Return (CAR) For the 10 state-owned enterprises (SOEs) selected for Danantara, we tested H1 (significant abnormal returns) and H2 (significant cumulative abnormal returns). The results showed that no significant AR at the 5% significance level (p=0.457) during the event window. No significant CAR over the event window (p=0.167) indicates that no cumulative revaluation effect. Supported by below figure, daily AR around the event date (t=0). Points cluster near zero, with no clear spike on t=0, and CAR showing minor fluctuation during the event window (t-5 to t+5). Reni Lestari, Lindsay Carnelian Emily 395 Figure 2. Abnormal Return (AR) and Cumulative Abnormal Return (CAR) These findings coincide with Kotter & Lel’s (2011) delineation between SWF investments (which provide observable reactions in price) as compared to SWF announcements (which at that time had no effect as it assumed no immediate financial materiality). On account of the relative pedigree of emerging markets, investors will typically dismiss policy signals until there are concerned operational details about the policy, for example when funds will be deployed or governance is intended to be improved (Bartram et al., 2021). The insignificant price reaction can be supplemented through the lens of signaling theory (Spence, 1973). In this case, Danantara's announcement was a "cheap talk" or no commitment mechanism (for example, a binding commitment to fund or some explicit non-retractable call). Without the credible commitment mechanism (and Geoffrey's third-party operational acknowledgement), markets did not seem to reserve a review or signaling of change in value. This is contrary to the Norwegian Government Pension Fund Government Pension Fund Global, where testimonial operational frameworks publicly established credible non-identity representation from inception (Clark & Monk, 2017). Trading Behavior: Abnormal Volume (AV) and Trading Volume Activity (TVA) H3 : Abnormal volume (AV) was not statistically significant at the firm level (p = 0.6429), meaning that the attention by the investors did not consistently shift over the individual SOEs. The absence of AV significance at the firm level is consistent with an asymmetric information perspective relating to emerging markets (Morck et al., 2000). Retail investors dominate trade (Kalev et al., 2004), and should probably allocated their attention to selected SOE's (e.g., Pertamina, Telkom) who are more pronounced in the market. As a rational result, the correlation between trading price and AV shifts appear in spikes (Figure 3) rather than shifts in an orderly fashion. H4 (TVA): Trading volume activity (TVA) was significantly higher during the event window (p < 0.0001), representing a short-term increase in trading pressure when compared to outstanding shares. For the event window, trading volume activity was abnormal, and returned to levels similar to the control condition after the event (p = 0.3883). American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 396 Trading Volume Activity (TVA) for SOEs in Danantara. Short-lived spike at t = 0 reflects speculative trading, but returns quickly to long-term trading levels. TVA Temporality. The short- lived surge in TVA (Figure Y) parallels attention-driven trading (Barber & Odean, 2008) where investors have exposure to headlines but there will not be a change to long-term valuations. This effect is magnified in inefficient markets where investors employ heuristic decision-making and institutional traders have little role to play (Megginson & Fotak, 2015). Figure 3. Abnormal Trading Volume (AV) Figure 4. Trading Volume Activity (TVA) Trading dynamics observed can be explained by the interaction of retail investor-driven momentum and institutional investor hesitancy in emerging markets. Retail investors still tend to make decisions based on heuristics and herd behavior (Kalev et al., 2004), so it is plausible that retail stimulus drove the momentary spike in TVA at t=0, representing a conditioned response to the Danantara announcement as a speculative trade opportunity. This also correlates with Barber & Odean’s (2008) “attention hypothesis” in which media impressions provoke trading activity but do not alter valuation, because retail investors respond the same during directly transacted timeframes. Institutional investors may also possess a “wait-and-see” approach toward Danantara as a result of worry about policy uncertainty and untested institutional arrangements (Bartram et al., 2021), so institutional investors did not sustain continuous flow, waiting for Danantara to provide production real-world credibility (Megginson & Fotak, 2015). The pattern of retail stimulus-driven speculative spikes and commercial specter in action represents different levels of trust in the partners in schedule execution and asymmetric market information exposure unique to emerging markets (Morck et al., 2000). The commercial focus of institutional actors layered individual volition retail trade reactions, suggesting that processes will have the potential to mature into a flow when observable policies are met. Liquidity Response: Amihud Illiquidity (ILLIQ) H5 posited that the announcement of the Danantara stock would affect market liquidity, using the Amihud Illiquidity Ratio (ILLIQ) as a proxy. From the one-sample t-test results, it is evident that at the stock level ILLIQ was significant (p < 0.0001) suggesting temporarily reduced liquidity. The paired sample t-tests were not significant suggesting any effect did not persist. The two levels of impact indicates that liquidity had a temporary event and did not remain in effect after the event. ILLIQ spike sharply at t=0, indicates 25% increase from pre-event levels (baseline ~3.00), this is aligned with the significant one-sample t-test results. By t+3, ILLIQ reverted to baseline levels, corroborating insignificant paired t-test results (p = 0.288). Reni Lestari, Lindsay Carnelian Emily 397 Figure 5. Amihud Illiquidity Ratio (ILLIQ) This supports Amihud (2002) who described liquidity shocks in emerging markets as a likely signature of informational uncertainty and execution risk (risk from asymmetric information). Liquidity is reacting to uncertainty and being without doubt its naturally moving. The relations of our results and Amihud for emerging markets, Fernandes & Ferreira (2013) have established that liquidity reacted to institutional signals instead of prices owing to stronger information asymmetry and lesser trust of the investor compared to a developed market. The results support H5, but only for the very short-term; market liquidity was disrupted temporarily, due to the uncertainty regarding the institutional event. Overall, the Danantara announcement did not have a material impact on the stock prices; however, it did create short-lived behavioral responses, as evidenced by increased trading volume and some perturbation in liquidity. These behavioral responses imply that investors considered the announcement institutionally important, however they were careful not to change valuations pending concrete signals about the implementation or economic effects. This has implications for future study of emerging markets, as these indicators demonstrate that, when no immediate economic consequences of an announcement exist, reactions to announcements are likely to appear in microstructure indicators (volume, liquidity) more so than in price. Conversely, this result aligns with the suggestion that in emerging markets investors react with caution to institutional declarations and react more to liquidity and volume indicators than prices when considering uncertain reforms (Megginson & Fotak, 2015). Market Level Reaction Beyond analyzing firm-specific reactions, this study also investigates the reaction of the whole market via the Jakarta Composite Index (IHSG). This is important to consider since we need to determine if the creation of Danantara affected market-wide expectations systemically. The analysis uses the same strategy by applying the same event study method with the same variables - abnormal return (AR), abnormal volume (AV), and Amihud illiquidity (ILLIQ) for IHSG. Table 3. Event study result – Market Level Variable One-sample t-stat p-value Paired t-stat p-value AR -0.0234 0.9818 0.7590 0.4901 AV -3.0020 0.0133* -1.3441 0.2501 ILLIQ 5.1068 0.0005* 1.2231 0.2884 American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 398 The findings indicate that Abnormal return (AR) in the market level was not statistically significant at the market level (p > 0.90). The lack of price reactions (H1/H2) at both market and firm levels strengthens the semi-strong form of the Efficient Market Hypothesis (Fama, 1970), where non-material announcements do not affiliate with any immediate changes in valuation. While, the AV and ILLIQ were statistically significant at the market level (p < 0.05). The measured AV and ILLIQ shifts, however, allude to a behavioral reaction based on the idiosyncrasies of a structural nature that are unique to Indonesia. Retail investors account for approximately 65% of the Indonesian equity marketplace (Indonesia Stock Exchange (IDX), 2023), and as other authors have suggested, there is a likelihood that AV increased by retail investors as a function of attention- based trading (Barber & Odean, 2008). Moreover, AMHUD's (2002) research on liquidity demonstrates institutional investors are more risk-averse particularly when uncertainty surrounds policy. Though institutional investors may not actually possess asymmetric information with respect to the event announcement, their behavior is characterized as risk-averse and resulted in negative reactions to a value-enhancing announcement considering their unwillingness to provide liquidity. Figure 6. Abnormal Volume (AV) Figure 7. Amihud Illiquidity Ratio (ILLIQ) This juxtaposition between retail- and institutional-level response reinforces the effects of asymmetric information pervasive in emerging markets (Morck et al., 2000). Consistent with India's NIIF (Gupta & Sharma, 2023), Indonesia's market-level microstructure volatility exceeded the price responses; unlike in developed markets (e.g., Norway) where SWF announcements generate immediate institutional rebalancing (Clark & Monk, 2017). This raises questions about the extent to which governance credibility in Indonesia is in need of moderation with respect to investors. Hypothesis Testing Summary The hypothesis testing outcomes provide a complete picture of the capital market's response to the announcement of Danantara. All hypotheses were tested using a combination of one sample and paired sample t-tests (or non-parametric alternatives where applicable) based on the different indicators for our abnormal return, trading activity, and liquidity. The summary of the hypotheses results is provided in Table below. Table 4. Hypothesis Testing Summary Hypothesis Description Test Result Support H1 The Danantara announcement generated significant abnormal return (AR). AR not Significant Not Supported Reni Lestari, Lindsay Carnelian Emily 399 H2 The announcement generated significant cumulative abnormal return (CAR). CAR not significant Not Supported H3 The announcement affected abnormal trading volume (AV). AV significant at market level only Partially Supported H4 The announcement affected trading volume activity (TVA). TVA significant (1- sample) but not sustained Supported (short-term) H5 The announcement affected market liquidity (ILLIQ). ILLIQ significant (1- sample) but not sustained Supported (short-term) Price-based indicators (H1 and H2) indicated no statistically significant change, indicating the market did not revise firm or market valuations in light of the announcement. Microstructure variables (H3–H5) showed behavioral responses, such as an increase in trading intensity and temporary liquidity disruptions at the aggregate (market) level. These findings affirmed that the capital market response was short-lived, and rather behavioral as opposed to a fundamental revaluation The mixed support for the hypotheses suggests it is important to examine both price and structural dimensions when assessing market response to policy announcements; and the following section confirms these findings in robustness checks. Robustness Checks To examine potential violations of normality assumptions, we used non-parametric Wilcoxon Signed Rank Tests as a substitute to the one-sample and paired sample t-tests for AV, TVA and ILLIQ, as the Shapiro-Wilk tests suggested those variables were not normal. The non-parametric results were quite similar to the original results: • TVA and ILLIQ were significantly different from zero within the event window (p < 0.05) • However, there were no significant differences in AR, CAR, or post-event TVA/ILLIQ. This assures that the statistical significance of the microstructure effects is robust to the choice of test statistic. CONCLUSION The absence of meaningful abnormal returns in response to the announcement of Danantara aligns with the pattern observed in many emerging markets, where investors tend to delay revaluation until concrete institutional actions—such as capital deployment or governance reform—take place. The market's muted price reaction reflects a broader skepticism toward symbolic policy announcements that lack operational clarity, especially within environments characterized by limited transparency and institutional credibility. However, the significant spikes in trading volume activity and temporary illiquidity suggest that investors did react behaviorally, not through price adjustments but through short-term trading activity and liquidity shifts. This indicates that in contexts with high information asymmetry and dominant retail participation, market participants process policy uncertainty more through microstructure channels than valuation channels. It underscores the importance of credible communication and visible policy execution in building market confidence. For future research, it is recommended to explore longer- term market responses once SWF policies move from announcement to implementation. Additionally, comparative studies across different emerging markets can offer insights into how institutional trust, investor composition, and governance quality influence market sensitivity to sovereign wealth fund formation and other macro-institutional reforms. 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