ISSN 2809-929X (Prin.
ISSN 2809-9303(Onlin.
Journal of Social Commerce Vol.
5 No.
3, 2025 (Page: 349-.
DOI: https://doi.
org/10.
56209/jommerce.
Investor Sensitivity to Liquidity and Systematic Risk in Indonesia's Real Estate Market Teguh Prakoso1.
Rina Apriliani1.
Yulistina2.
Donny Dharmawan3.
Nurnaningsih Utiarahman4 1Management Study Program.
STIE Manajemen Bisnis Indonesia Management Study Program Universitas Mitra Indonesia.
Indonesia Management Study Program.
Faculty of Economy.
Universitas Krisnadwipayana.
Indonesia 4Management Study Program.
Universitas Gorontalo.
Indonesia Article History Keywords Liquidity Systematic Risk Stock Returns JEL Classification M41.
M42.
M48.
C83 Abstract The current paper analyzes whether the liquidity and systematic risk can predict stock returns not only as a typical financial indicator but as a kind of a descriptive tip in modern market environments.
Using panel data of Indonesian firms listed in the property-sector in the Indonesia Stock Exchange, the research makes use of the random effects model of statistical analysis to assess how firms-level liquidity statistics, beta, and Return results are connected to one another.
The findings show that the systematic risk has a strong and positive effect on the stock returns but this is not the case with liquidity, which does not have a similar type of effect.
This result is not an indicator of the reduced role of economy but it is a significant rejuvenation concerning the methods in which financial information is being assimilated, ranked and acted upon by the investors.
The lack of the effect of liquidity is said not to be irrelevant but invisible in platforms, stories, and investor attention schemes that are more and more dominating the interpretive access.
By highlighting a changing ecology of valuation where the salience of financial metrics is mediated by visibility and volatility, as well as digital resonance, the findings help to understand that salience in this context has become contested, and the measured episodically as they become visible and volatile on the temporal shelf of valuation.
Introduction The stock market of today portrays a significant disjunction between the canonical financial theory which assumes a rationality of investor choice derived out of full information and crisp risk-return calibration, and the reality seen through the lens of empirical observation, characterized as it is, by an increasingly dominant role played by perception-based heuristics.
Actors in the current digital environment of immediacy, platform-mediated attention, and algorithmic news filtering no longer just judge firms based on their structural properties, but 1Corresponding Author: Teguh Prakoso.
Email: teguh.
prakoso@stiembi.
Address: Tugu.
Kec.
Cimanggis.
Kota Depok.
Jawa Barat 16451 Copyright A 2025.
Journal of Social Commerce is licensed under Creative Commons Attribution-ShareAlike 0 International License .
ttp://creativecommons.
org/licenses/by-sa/4.
Celebes Scholar pg Journal of Social Commerce apply to them interpretive frames in which volatility, fast responsiveness, and visibility are elevated in favour of solid foundations (Nguyen & Hekman, 2.
This cognitive reframing is proved by measurable change in how the market signals in its signals are taken in and responded to, especially in the liquidity and systematic risk.
This is supported by the fact that the preference pattern of even the high-frequency trading systems still tends to run off base .
s surveyed by Sasaki & Ravasi .
, thus reinforcing the increased importance of interpretive frames, as opposed to inherent attributes of the firms.
In the line of this, liquidity and systematic risk remain central variables in asset-pricing empirics, but their information value is again seeming to be be conditioned rather than Liquidity has historically been interpreted as an indicator of the company-level operating efficiency, and it has described the ability of companies to meet obligations, which help insulate those companies against exogenous shocks thus providing confidence to the However, recent empirical studies, including El Touki .
, demonstrate that the projected positive relationship between liquidity and stock returns may weaken when attention is paid to sensational or turbulent cues in a certain sector .
r marke.
Their study of new Asian equity markets ends up showing that when liquidity was not analyzed within times of extreme market correction, it shows an absence of well-explanatory strength, a definite blind spot in interpretation perhaps.
As Acharya & Rajan .
argue, although liquidity has always been a structural requirement of businesses, its market implications are becoming ever more contingent on whether the liquidity is augmented with complementary signs of expansion or when accompanied only by passive indications that liquidity is low the liquidity levels are more often to be interpreted as sign of non-reassuring passivity than a sign of The component of returns traditionally measured using the market beta of a firm is systematic risk which anchors the model of returns like CAPM.
It quantifies the responsiveness of a firm to the overall market movements, with the theoretical implication in it being a larger exposure to the macroeconomic fluctuations being accompanied by a larger premium on the return (Horvey & Odei-Mensah, 2.
However, there is the development of empirical bias.
In crosector analysis carried out in the study by Koestinger .
of such financial market environments as Asian ones, the predisposition toward greater volatility even in respect of the same market entities understandably refers not only to the prediction power of beta but also to the emergence of its erosion within the scope of, in this environment, a positive sentiment that naturally interprets volatility as a sign of possible opportunity or an irrational noise that is a core predilection of a certain investor community.
This degree of interpretive elasticity makes beta a volatile signal eclipsed on one side and neglected on the other, to market tale.
The phenomenon is more apparent in the Indonesian case, where it is possible to find it better explained with the help of the integrative analysis of the explanatory power of beta in industry sectors provided by Anjani & Prasetyo .
No matter how the market fundamentals change, they only consider beta significant in cases where volatility is in line with speculative cycles or when the media coverage surges.
Indonesian market empirical literature has brought another complexity.
Whereas certain studies prove that there exists a positive correlation between liquidity and returns, i.
Nugroho & Sukhemi .
, others have reported no unanimity, especially in asset-intensive industries like property because liquidity ratios cannot be used as indicators of short-term investor confidence (Wijaya & Hamri, 2017.
Putra & Darmawan, 2.
As the evidence of among the latest, the findings of Galatiasinaga et al.
and Kurnia .
tend to prove that beta continues its positive correlation with stock returns within classical models.
However, they conclude that in many cases, this effect is dulled by biases in the investors and /or the attention changes caused by the platform.
The input of Saptono et al.
in the analysis of Indonesian Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce listed firms over the period 2020-2022 reveals that the implications of liquidity on the return are frequently industry-specific and channelled through the explanatory power of narrative coherence in a financial statement, hence displaying the increasing importance of aspect how indicators are framed and brought to the spotlight.
This conclusion here is that the nature of the financial signal has changed wherein they are no longer valued based on the statistics of correlation, rather, interpretive availability.
In other words, liquidity, systemic risk, and other concepts do not automatically manifest in the financial statements.
to become visible to investor minds, which are more and more shaped by information graphics, the rhythm of visibility, and symbolic prominence.
In this regard, the Indonesian property and real estate business comes out as a very important empirical location.
It is defined by long-horizon value structures, sluggish liquidity cycles, and heavy reliance on macro-political environment issuesall of which give a run to the communicative effectiveness of classical financial reports at realtime investment conduct.
Apriliani & Prakoso .
also note that in this industry, the performance of their stock prices is frequently volatile and sentiment-based, even though the fundamentals of many companies in the industry are strong, implying that structural quality is dissociated with perceived value.
To this end, this paper will readdress the issue of the liquidity and systematic risk impact on the determinants of stock returns on the Indonesian property and real estate companies during the period of 2022-2024.
A strong panel data methodology is used to assess whether such signals maintain explanatory power, but the outcomes of the analysis do not assume that their effects are simply related to the presence of the data.
Rather, it is more in line with the growing body of literature stressing the mediated, perception driven understanding of financial According to scholars like Ragazou et al.
research on asset pricing needs to consider the infrastructural and cognitive aspect of what makes investors interpret data in the first place, rather than the data.
Daxhammer et al.
also indicate that based on cognitive shortcuts, platform conditioning and visibility hierarchies today, financial behavior is then influenced in ways that usually exaggerates some signals such as that of volatility indicators, and flattens others that are equally or more relevant to long-run value.
With the re-introduction of liquidity and systematic risk into this re-contextualized conceptual arena, the goal of this research is to contribute to a more holistic interpretation of financial signal reception.
It attempts to explain when or under which conditions the variables still continue to matter in terms of affecting investor choices, and when or under what conditions such variables do not belong to the epistemic space even though they still possess economic By so doing, it provides a contribution to an emerging interdisciplinary understanding that market performance is as much a result of the signal intelligibility and mediated meaning making activities as it is of firm performance.
Karanasos et al.
indicate that valuation estimation in emerging markets needs to be increasingly focused on interpretation conditions, particularly in sectors with volatility cycles, when regulating the speculation and asymmetrical perception of investor expected returns.
This paper is in direct answer to that cry.
Literature Review The Influence of Liquidity and Stock Returns Wijayanti .
, liquidity is a description of a company's ability to meet its short-term obligations smoothly and on time, so liquidity is often referred to as short-term equity.
company is said to be liquid if the company is able to meet its obligations on time, but if the company is unable to meet these obligations that have matured on time, it can be said that the Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce company is not liquid, (Ma et al.
, 2.
Company management always tries to maintain a healthy liquidity condition for the company and is met on time.
This is done with the intention of giving a reaction to prospective investors and shareholders in particular that the company's condition is always in a safe and stable condition, which automatically means that the company's stock price will also tend to be stable and is even expected to continue to increase.
Companies that have high liquidity will be in demand by investors and will also have an impact on stock prices which tend to increase due to high demand.
This increase in stock prices indicates an increase in the company's performance in this case will also have an impact on investors because they will get a high rate of return on their investment.
The results of research conducted by Nugroho & Sukhemi .
Putra & Darmawan .
provide the conclusion that Liquidity has an effect on Stock Returns.
H1: Liquidity has an effect on Stock Returns The Effect of Systematic Risk on Stock Returns Systematic Risk (Bet.
is a measure of the volatility of security returns or portfolio returns against market returns.
If systematic risk appears and occurs, all types of stocks will be affected so that investment in one or more types of stocks cannot reduce losses.
Return and risk have a positive relationship, the greater the risk that must be borne, the greater the return that must be This is in accordance with the concept of high risk high return, namely if investors are willing to bear high risks, then the returns obtained are also high.
High systematic risk is considered unable to provide high stock returns or vice versa.
The higher the systematic risk, the more it will affect stock returns.
This encourages companies to perform increasingly leading to increased stock returns.
The results of research conducted by Galatiasinaga, et al.
concluded that Systematic Risk affects Stock Returns.
H2: Systematic Risk affects Stock Returns.
Methods In this research, a quantitative explanatory research model is being used, a panel data regression analysis is being applied to analyse the effect of liquidity and systematic risks on the stock returns at Indonesian capital market.
The study targets Property and Real Estate enterprises that are listed on the Indonesia Stock Exchange (IDX) between the year 2022 and 2024.
The goal is to determine how sensitivity of investors to signs of finance underlying a firm-such as liquidity levels and exposure to the system-wide risk-reflective behavior of returns in an industry that is volatile and often capital-intensive.
Population This research will cover all companies which are registered under the Property and Real Estate sector as per the official IDX registry.
The purposive sampling method was used to select the firms in terms of eligibility that are consistent with four inclusion strategies including: .
the firm has to be continuously listed on the IDX between the 2022 and 2024 .
the firm has to issue audited annual financial statements in each year being .
daily or periodically stock prices should be accessible and complete to recalculate the returns.
the firm cannot be subjected to mergers, delisted, or other structural changes during the observation period.
Out of such screening, thirteen of the firms met the criteria and hence the final result included a dataset of thirty-nine firm-year observations.
The study bases itself on the use of secondary data which is verified and publicly available.
Other financial data as the total current assets and current liabilities were collected by taking the values directly as reported in the audited financial statements of the companies same as published by the Indonesia stock exchange at its official web site .
Stock price Teguh Prakoso et al.
Journal of Social Commerce Celebes Scholar pg data were obtained on IDX and also cross-referenced with the financial details websites like Yahoo Finance and Investing.
com and this made analysis of annual returns and their calculations precise enough.
Beta coefficients, used to represent systematic risk, were either collected from financial reports or calculated manually based on historical return data using standard market regression techniques against the IDX Composite Index (IHSG) as the market The following variables were measured and operationalized:
Stock Return (Dependent Variabl.
Measured as the annual rate of return, calculated from the change in the firmAos closing stock price from the beginning to the end of each fiscal year.
ycE ycE Return = 1 ycEOe 0 where P1 = ending stock price and P0 = beginning stock price.
Liquidity (Independent Variabl.
Operationalized using the current ratio, calculated as current assets divided by current This ratio reflects the firmAos short-term financial health and ability to meet its immediate obligations.
Systematic Risk (Independent Variabl.
Measured using the beta coefficient () of each firm's stock, which quantifies the stock's sensitivity to market movements.
A beta above 1 indicates higher volatility relative to the a beta below 1 suggests lower volatility.
The data were analyzed through EViews version 11, the multi-stage process of the analysis.
started by using the descriptive statistics to give an overview of the distribution and the level of variability of each variable.
Three sequential diagnostic tests were carried out to determine the most adequate panel regression model.
The two tests were initially used in the difference between the Common Effect Model and the Fixed Effect Model.
This has been succeeded by the Hausman test which compared the consistency of coefficients in order to find out which was the better model (Fixed or Random Effects Mode.
In the end, the Breusch-Pagan Lagrange Multiplier (LM) test was used in order to confirm the suitability of the Random Effects Model.
The Random Effects Model has been chosen as the most solid specification based on the aggregation of the results of these tests since it well represents the unobserved heterogeneity between firms and at the same time the model is efficient.
The last model was fitted by the Panel EGLS (Random Effec.
approach that considers both cross-sectional and time-series variation, which is in any circumstance not directly observable.
Specification of the estimation model is as given below:
Returnit = 1 Liquidityit 2 SystematicRiskit Ait Where:
Returnit = Stock return of firm i in year t = Intercept term Liquidityit = Current ratio of firm i in year t 1, 2 = Coefficients for liquidity and SystematicRiskit = Beta coefficient of firm i in year t systematic risk Ait = Error term capturing unobserved influences Thereafter, a classical test of assumption was run on the dataset to check the validity of the regression model.
The Jarque-Bera test was used in checking the normality of residuals.
Multicollinearity among the independent variables was verified through the Variance Inflation Teguh Prakoso et al.
Journal of Social Commerce Celebes Scholar pg Factor (VIF) with the scores being less than 10.
The heteroscedasticity was verified with the help of the residual plot and probability and since the DW statistic did not show the The diagnostic checks all indicated that the dataset was in line with the assumptions made to achieve valid panel regression analysis.
Results and Discussion Data Diagnostic and Classical Assumption Testing It is urgent to test the validity of the dataset regarding the statistical assumptions underlying the correct interpretation of the inferences in the panel data modelling before going to the stage of regression analysis.
The validity of any linear estimation especially in the application of a Random Effects Model requires that the residuals be normally distributed, multicollinearity is absent, the errors have constant variance .
and lastly that the residuals are independent of one another .
ot autocorrelate.
The breach of any of these assumptions may cause the existence of biased coefficients, inefficient estimators or erroneous p-values, which eventually reduce the durability of the explanatory power of the model.
This led to the implementation of the following tests to the purpose of conducting rigorous examination as to the statistical sufficiency of the information and assurance that results made empirically are based upon a robust and objective framework of estimations.
Table 1.
Jarque-Bera Normality Test Result Statistic Value Data are normally distributed Jarque-Bera Probability Conclusion To guarantee reliability of regression inference, we used a test of normality that was done based on Jarque-Bera (JB) statistic in EViews environment.
The test is to check the assumptions that the residuals of the model are normally distributed and this is the basic requirement in making valid hypothesis testing in the context of linear regression.
The test significance stipulates that in the event that the JB probability is more than 0.
05, then there is normal distribution of the In the present research.
JB test yielded the probability of 0.
083 which is more than The finding serves to show that the distribution residual does not severely violate the assumption of normality and therefore the analysis can continue without any fear to other classical assumption-testing stages.
Table 2.
Multicollinearity Test Result (VIF) Variable Liquidity Systematic Risk VIF Value Conclusion No multicollinearity No multicollinearity The presence of multicollinearity was also checked and it means that the relationship between the independent variables and systematic risk or liquidity is not too strong and one with another that it will give a distorted estimate of coefficient and interpretability of regression.
The test was conducted using the Variance Inflation Factor (VIF) whereby a value of 10 was the cutoff in highlighting problematic collinearity in this test.
All of the VIF values of all the variables in the predictor repose were well within this limit, thus assuring that there is no multicollinearity and each variable in the model makes a distinctive contribution.
This helps the model to be internally consistent and that the observed relationships are independent and consistent statements of the effect of each of the explanatory values on the stocks returns.
Teguh Prakoso et al.
Journal of Social Commerce Celebes Scholar pg Table 3.
Heteroscedasticity Test Result Variable Probability Value Liquidity Systematic Risk Conclusion No heteroscedasticity No heteroscedasticity To confirm the condition that error terms in a panel regression model are homoscedastic, the condition that residual variance is homoscedastic in the same model was tested.
The test conducted was a probability based test where the level of significance chosen was 0.
When the p-value is greater than this value then it means that the variance of residuals is not changing and therefore, no heteroscedasticity.
In the interpretation, all the predictors had results that were more than 0.
05 indicating that there is no existence of heteroscedasticity.
This gives more credibility to the calculated standard errors and weight supporting the viability of the model in respect to reflecting the exact relationships amongst the variables.
Table 4.
Durbin-Watson Autocorrelation Test Result Statistic Durbin-Watson (DW) Lower Bound .
U) Upper Bound .
Oe dU) Conclusion Value No serious autocorrelation detected Autocorrelation is defined as a correlation between residuals over time and usually results in biased standard errors and unreliable statistical inference in case it exists.
One way to check this was the use of Durbin-Watson (DW) statistic.
Based on the set standards, autocorrelation can be said to be non-existent when the DW lies between the upper and lower limits that are set by dUdU and 4 - dU4 - dU.
In current research, the DW statistic was 1.
818198 and this value in the Durbin-Watson table was 1.
859 and 4- dU4-d U4 -dU was 2.
The DW value is a little bit lower than the lower limit, yet close to the appropriate range, which is why there is an indication that the autocorrelation is not a critical concern.
Consequently, the residuals appear to be independent across time, allowing for reliable estimation and interpretation of the regression coefficients.
Descriptive Analysis The following table presents the descriptive statistics for all variables used in the study, covering 39 firm-year observations from 13 Property and Real Estate companies listed on the Indonesia Stock Exchange during the 2022Ae2024 period.
Table 5.
Descriptive Statistics Variable Stock Return Systematic Risk () Liquidity (Current Rati.
Minimum Maximum Mean Standard Deviation The descriptive findings are the early description of the firm-level return pattern and exposure to market risk signals.
There is a significant dispersion in stock returns in the sample as it shades between -0.
597 and 0.
875 with a median of 0.
115 and shows that there is a considerable variation in the landing of the firms.
The implication is that the investors are responding to a wide range of expectations and signals on information, which is captured in price variations.
The fact that beta-value has an average of 0.
02546 as well as the relatively low standard deviation suggests that the stocks within the sector are less volatile in general relative to the market as a whole, though some of the firms have a considerable degree of exposure.
In the Teguh Prakoso et al.
Journal of Social Commerce Celebes Scholar pg meantime, the liquidity ratio is between 0.
719 and 8.
ith the mean 2.
which means that the firms have a healthy liquidity in the short-term perspective, though it also implies that some firms are viewed as considerably more financially stable than others.
Such numbers highlight the dissimilar nature of processing and pricing of the financial fundamentals across the financial base, and the investor responsiveness differs across firms according to the perceived risks and overall firm stability-implying that stock returns behavior is not only based on the overall state of the macro economy but also on the resonance of specific financial characteristics with the expectations of investors in a competitive and dynamic financial Model Feasibility Analysis Before the classical assumption testing and hypothesis testing stages, measurement using Eviews requires determining the analysis model used, namely the Common Effect Model (CEM).
Fixed Effect Model (FEM), and Random Effect Model (REM) through three types of tests, namely the Chow Test.
Hausman Test, and Lagrange Multiplier Test.
Table 6.
Chow Test Result Test Type Redundant Fixed Effects Statistic F = 5.
p-Value Model Decision Use Fixed Effect Model The Chow happens when the significance of a hypothesis that Fixed Effect Model (FEM) fits better than the Pooled Least Squares (Common Effect Mode.
is tested.
The resulting p-value after testing is 0.
0002 that is much lower than the level of 0.
05 in the significance level.
This finding states that cross-sectional heterogeneity is present, in other words, firm-level heterogeneity affects the dependent variable to such an extent that the impacts could not be estimated with the help of a pooled model.
Hence the Fixed Effect Model is supported in the initial stage and hence these may suggest that firm-specific characteristics which are unobserved, might influence investor reactions of liquidity and systematic risks across firms.
Table 7.
Hausman Test Result Test Type Hausman Test Statistic NA = 2.
p-Value Model Decision Use Random Effect Model The Hausman test evaluates the similarity of coefficients of both models.
Fixed and Random Effects.
Since the p-value is not significant .
= 0.
299 > 0.
, it shows that Random Effect Model (REM) is the right choice because under the null, the estimators of interest are efficient and consistent.
In behavioural terms, this implies that the impact of changes in liquidity, as well as the impact of changes in systematic risk on stock returns is not dictated by firm-specific features that are invariant over time, but by more global and randomly dispersed effects to the sampled firms- hence REM model is more statistically and substantively desirable.
Table 8.
Lagrange Multiplier Test Result Test Type Breusch-Pagan LM Statistic NA = 15.
p-Value Model Decision Reject Pooled Model.
Use REM In order to determine which is better, the Pooled Least Squares model or the Random Effect Model.
Breusch-Pagan Lagrange Multiplier (LM) test is to be used.
The test implies that the pvalue obtained is 0.
0001 and this means that it is significant to improve the model fit in terms of the unobserved heterogeneity which is modelled by random effects.
This also re-assures the appropriateness of the REM, in line with the Hausman test and the belief that the dispersions of investor sensitivity to liquidity and systematic risk variables are more likely to be attributed to inclusion of firm-specific random elements in the model.
Teguh Prakoso et al.
Journal of Social Commerce Celebes Scholar pg Hypothesis Testing Table 9.
Regression Test Results (Panel EGLS Random Effec.
Variable Constant () Liquidity (LK) Systematic Risk (RS) Coefficient t-Statistic p-Value Conclusion 022 Statistically significant 098 Not statistically significant 000 Statistically significant The regression analysis gives us an idea of the manner in which the value of firm expressed by investors .
he stock return.
is influenced by the issues of liquidity and systematic risk.
The constant is statistically significant and it shows the foundation upon which the other factors taper down when the explanation variables are without any preference.
The coefficient of liquidity is a negative coefficient that is not statistically significant .
= 0.
, indicating that in this sample, the fluctuations of the short-term solvency do not have any significant impact on the perception of returns by any degree in any way significant, perhaps because investors are more interested in the external market signals or long-term risk as opposed to short-term Conversely, systematic risk .
displays an enormous and tightly significant positive quantity .
539, p < 0.
, meaning that the companies that are more responsive to market fluctuations are likely to provide more gives in accordance with the high risk, high reward law.
This reflects a risk-sensitive investment climate, where volatility is priced in favorably by market participants.
Table 10.
Coefficient of Determination (RA) Test Model Metric R-squared (RA) Value Since R-squared is 0.
5702, this shows that the combined factor of liquidity and systematic risk explains about 57.
02 percent of the variance in stock returns and this is among the sampled This indicates a moderately strong model which points to the fact that though the financial fundamentals namely liquidity and exposure to market risk explain a significant part of the behavioral aspect of returns, yet the remaining 42.
98 percent of the variation is explained by other latent or situational factors.
These can be investor sentiment, macroeconomic environment, or qualitative firm characteristics that are not included in the existing model, which have some effect on perceived firm value in the market.
Reloading of Financial Signal Relevancy in Relating to investor choices The fact, as an empirical evidence, that not the liquidity but the systematic risk in particular shows a substantial amount of effect on stocks returns directly lie the basis to profound evaluation of how the investors apprehend the financial signals in the modern market The assumption, long present, that the liquidity as a proxy of operational solvency and short-run financial stability is imbued with some material influence on the behavior of returns, needs to be questioned now because of shifts in depositor focus of attention and imaginative schemes.
Although traditional liquidity metrics .
ncluding current rati.
still play a central role in traditional valuation models (Nassirzadeh et al.
, 2.
, the reduced explanatory force they have in that respect implies that these internal indicators are increasingly being discounted by investors in favor of variables that they consider a better reference to market-wide volatility and the capability of potential returns (Moodley et al.
, 2.
Such a transition suggests a reconfiguration of what is seen as financially relevant and less via underlying fundamentals and far more within the impact and perceptions of external market activity expressed in the manner in which it will occur as well as how it will be managed and incorporated into the expectation of valuations.
Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce When it comes to systematic risk, its past impact is positive and statistically strong, indicating a modern spirit of investor who no longer vilifies volatility as a threat to be avoided but values and treats it as a risk, to be championed, benefit out of and charged.
The present observation yields to a trend in literature that highlights the performative side of market expectations when in a state of uncertainty (De Lima et al.
, 2.
In the capital-intensive industries like real estate, where the illiquidity of assets is inherent in the structure, the ability of the equity of a firm to reflect the general movement of the market witnesses a proxy that is defined to represent the investability.
It seems that investors are providing awards to companies where stocks behave in unison with macro-level indicators, demonstrating a logical acceptance of risk as an asset and not a disqualifying fact.
This school of interpretation is in line with the observations of Gupta & Chaudhary .
, who record that such market actors prefer to mix up high-beta equities with opportunity spaces expecting price momentum to rise, especially in emerging What is being painted is a picture of the actions of investors who become more and more directed not by the fixed interpretation of financial indicators but by dynamic sensitivity to the amplification of signals along market outlets.
the recession in the correlation between liquidity and payback shows that there is an increasingly general epistemic shift in investment procedures in which there is prioritization of signal resonance over signal source.
Instead of independently reviewing the strength of a balance sheet, investors seem to evaluate the relevance of a variable only in the context of whether it conforms or is added to overall narratives of market sentiment and structural risk (Chen et al.
, 2.
It aligns with the recently announced findings of Batra et al.
who hold that investors are increasingly sensitive to external informational cascades and volatility measures to the extent that it dwarfs level stability aspects of firms.
In this kind of environment, liquidity can be seen less in terms of a signal of business health and more in terms of a neutral or peripheral state of affairs a state of affairs that is needed but which does not make a difference in the formation of investor belief one way or another.
The lack of the impact of liquidity also draws a neutral thought towards the asset horizons within which investment decision is being made.
Compression of interpretive windows, in which policy decisions are becoming more determined by the responsiveness in the short-term signals than the defensibility in the long-term asset, has permeated contemporary markets (Hazen, 1.
Systematic risk here plays the role of a more perceptible and comprehensible indicator, which is strictly linked to the activity at the index level and the overall state of the This enables the investors to make other expectations swiftly and with a degree of more referential vitality.
This argument has been corroborated in a study by Nikolaou .
, which points out that the beta-linked risk was seen to act as a proxy to be in accord with institutional trading tendencies and aggregate flows of funds.
In the case of liquidity, on the other hand, financial statements continue to keep the matter in the grave, and the forecast about the readiness of the operations to be carried out with liquidity is weak or insignificant.
Notably, the dynamics are not constrained to cognitive shortcuts, or informational myopia.
Through this hierarchization of risks, systematic risk will have been privileged as a structural recalibration of the construction of meaning in investments.
As Adewale et al.
remark, the contemporary investment environment implies platforms, algorithms, and curated analytics through which financial decision-making occurs.
Such systems tend to give prominence to market-timed data items .
, beta coefficients or volatility indice.
and be inequitable to firmspecific measures of balance sheets, but these have interpretive work demanded.
What matters is thus made to depend on visibility, communicative friction rather than sort of inherent financial importance in investment.
The fact that the variable of liquidity is not significant Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce statistically, therefore, does not imply that it is irrelevant, but a symptom of epistemic displacement in the order of priority in the legibility of signals.
The latter is further supported by the empirical evidence of Hanson & Oprea .
, who reveal that investors are becoming more inclined to rely on meta-signals data about data in consideration of stock value.
Strategically packaged as part of broader stories of geopolitical turmoil or instability of the market, the systematic risk can give investors the scaffold to hang on to, but liquidity can act only as an empty scaffold unless it comes along with a crisis Likewise the result on Ferraro et al.
shows that the systematic risk is being interpreted performatively, where their volatility establishes interpretive lead as to how other participants will act hence forming a self-fulfilling characteristic of predictive alignment.
this context, the solid grounds are not abandoned, but they are made secondary to variables which present a relational context to the market symbol system.
Given that systematic risk is playing the role of a communicative bridge between the value of the firm and the market expectation now, the language of investment has evolved.
The market has stopped talking the grammar of permanent ratios and it is talking the vocabulary of reflexive risk matching.
It can be seen in the article by Ghoshal .
, in which the authors claim that the intimations of investment decisions are both obfuscated by the filtering tactics of affect and algorithm, increasing volatility as a proxy metric of relevance.
What that your results indicate, in other words, is not a step outside rationality but a redrawing of lines of rationality- a redesigning of economic logics that rearranges everyone in a chain of interpretive legitimacy of contradictory signals.
Market Behavior Held in Historical Context This usage of risk, value, and investment relevance has had its meaning reconfigured in contemporary markets and it cannot be divorced of the infrastructural shifts occasioned by the digital intermediation of markets or markets that have been digitalised.
Financial data are not experienced in isolation by the investors anymore, and they do not communicate directly to the firms they are analyzing.
They do not do so, however, with representations of those firms itself, but via a dense mesh of data aggregators, brokerage platforms, fintech applications, ranking algorithms, and social signal raisers.
In this new world of knowledge structure, certain financial indicators become eminent and other fall into the state of interpretive insignificance.
This mediated financial cognition needs to be read against the empirical finding of this study that the systematic risk that greatly impacts stock returns is different to the liquidity that does not have the same impact.
Liquidity has not lost its economic significance, just that its transparency, reverberation, and availability in these channels have received reduced exposure in contrast to the comparatively more market-related guidelines such as the systematic risk.
This is what Webster .
call transformation of intrinsic valuation into interpretive immediacy when investors achieve contact not merely on the basis of content with signals, but on the basis of their ability to plug into the digital and collective attention systems.
The risk built-in is systematic and is measured by beta.
it is enhanced excessively with epistemic amplification in this structure.
It is automated in calculation, its results standardized and incorporated into most digital investment interface - ranging between institutional quality terminals and mobile investment applications offering investors with retail accounts.
It is this automated legibility at work that grants this new position of systematic risk an edifice of interpretive power so much greater than its conventional risk-assessment one.
Beta is hardly It is scoring algorithmically, is graphically displayed and is frequently accompanied by predictive overlays which predict volatility windows or portfolio exposure scenarios.
Conversely, liquidity as represented by current ratio or other working capital ratios is still interned in the financial statements and has to be marshalled in a manual and laborious way.
Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce Financial data are not experienced in isolation by the investors anymore, and they do not communicate directly to the firms they are analyzing.
They do not do so, however, with representations of those firms itself, but via a dense mesh of data aggregators, brokerage platforms, fintech applications, ranking algorithms, and social signal raisers.
In this new world of knowledge structure, certain financial indicators become eminent and other fall into the state of interpretive insignificance.
This mediated financial cognition needs to be read against the empirical finding of this study that the systematic risk that greatly impacts stock returns is different to the liquidity that does not have the same impact.
Liquidity has not lost its economic significance, just that its transparency, reverberation, and availability in these channels have received reduced exposure in contrast to the comparatively more market-related guidelines such as the systematic risk.
This is what Citton .
call transformation of intrinsic valuation into interpretive immediacy when investors achieve contact not merely on the basis of content with signals, but on the basis of their ability to plug into the digital and collective attention The risk built-in is systematic and is measured by beta.
it is enhanced excessively with epistemic amplification in this structure.
It is automated in calculation, its results standardized and incorporated into most digital investment interface - ranging between institutional quality terminals and mobile investment applications offering investors with retail accounts.
It is this automated legibility at work that grants this new position of systematic risk an edifice of interpretive power so much greater than its conventional risk-assessment one.
Beta is hardly It is scoring algorithmically, is graphically displayed and is frequently accompanied by predictive overlays which predict volatility windows or portfolio exposure scenarios.
Conversely, liquidity as represented by current ratio or other working capital ratios is still interned in the financial statements and has to be marshalled in a manual and laborious way.
This disjunction is corroborated by Gatheral .
, as they observed a so-called lack of surface volatility in metrics composed of internal firm stability.
This detail stems to mean that the metrics are not voluminous enough to be ranked, flagged, or otherwise pushed through the algorithmic attention channels.
Liquidity is just not made into action by a host of investors, many of whom become involved with real-time dashboards or social-inspired stock suggestion It can be included in a latent credibility test but that is not how it operates in the affective, symbolic, or tactical senses as systematic risk does.
Consequently, it has led to the absorption within the background epistemology of finance, which is there, but lifeless.
It is this dynamic which is particularly sharp in capital-intensive industries like the property and real estate industry, where the value of assets involved is intrinsically linked to the longhorizon development and operational endurance.
They are companies whose business strategy focuses on sluggish sustainable profitability.
Nevertheless, in a culture where volatility charts, trending tickers, and responsive sentiment analysis determine what traffic areas in an investordriven landscape, these firms now need to convince themselves how to make an appearance in structure of meanings where exasperating by-passes reward the exercise of presence rather than Weinman .
state that in digitally dense markets, what is valued is further tinted by price.
that is, how easily a firm can be tracked within the topographies of dashboards, alerts, and filtered rankings.
It is an outflow of systematic risk that offers itself to such visibility since this risk creates flow of consistent data.
Liquidity cannot and hence does not usually reach the mental stratum of investor systems until it is signalled by such sensational events as solvency crisis or earnings meltdown.
Notably, such migration of investor focus is not restricted to retail speculation or even bubblereliant sentiment.
Even the institutional investors are becoming so reliant on a machine-based filtration method, which favors those variables, which are quantifiable as well as referential to broader market trends.
Orlitzky .
have presented strong arguments to the claim that Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce volatility-related metrics are attaining higher significance among portfolio managers in periods of increased trading volume, doing so not merely because they reflect sensitivity on the part of the market, but rather due to their symptomatic inclination in joint action.
Such feedback develops an aspect of recursive amplification whereby the variables under observation come to be the variables capable of moving the market which, again, reinforces their integrity as In such a setting, belonging to the environment or being predictive of the environment, as a systematic risk is, is merely one of the determining roles, in that it is a participatory heuristic since the investors enable setting the expectation of the other participants in the market.
The issue of liquidity, sluggish, self-contained and less readily storied, gains little ground in this process of reflexivity.
Further, this tiered interpretive methodology is maintained and fortified by means of the social infrastructure of investment discourse.
Amidst the financial press, online forums, chat services embedded in platforms, and algorithmic-driven watchlists, observations and disseminations are creating what is increasingly imagined to be having value.
As demonstrated by Louis et al.
, the choice of investment is today often scaffolded by some shared source of information on how specific actions are perceived, with price action, volatility clusters, and beta movement being used as foundation to build narratives collectively.
Systematic risk works well in this It has a narrative that the investor can amplify, react to and plan about.
Liquidity does Its countenance or lack cannot simply be tweeted, represented and repackaged into sentiments of forecasting.
Witt .
suggest that variables that are not circulated in these feedback mechanisms become functionally irrelevant insofar as they continue, depending on their structural weight, losing symbolic purchase.
The results of the current research, therefore, do not just indicate an asymmetry at the statistical level but also an ecological change in financial meaning-making.
Investor behavior in the present day can only be analyzed as an environment of platformized cognition, where not merely the question of what is true matters, but that which is legible, sharable, anticipatorily charged.
Dowd .
provide the transformation of data interpretation to signal choreography as the case, and indicators such as beta are used not only to characterise market exposure but does the work of market exposure.
What this means is that the once merely input to financial models is now a currency of visibility, which brings with it a beneficial relevance in an informational area that is currently very dense.
Liquidity, though useful as a matter of economics, does not take such a commanding place in this symbolic economy.
The forms of visibility, temporality and resonance that regulate the conditions of visibility on investor engagement in firm data is taking new forms in the digital domains in which financial meanings are currently negotiated.
It is in this space that systematic risk flourishes not only because exposure is obviously what it is but more importantly this is what it is made to move through, to be viewed, compared, and entred within its digital renderings of what is significant.
Without being rendered into an event or crisis, liquidity does not have the scaffolding of narrative to do the same.
The findings of this paper are thus to be construed as indicative of restructured attention economy in capital markets, where signals of mobility, involvement, and responsiveness are favored over signals of stability, consistency, and structural robustness.
The task which now faces both scholars and practitioners is not how to bewail this change, but how to comprehend it, how to chart how financial truth is currently being reassembled by the infrastructures by which it is being narrated.
Financial Strategy.
Theory, and Future Research Trajectories Implications The digitally mediated ecosystems of valuation that have developed as described do not only reconstruct the process by which investors perceive financial signals.
they also radically transform what companies need to tell, how academics need to theorize the actions of investors.
Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce and where investigators should focus their attention in the future.
The fact that systematic risk is now a significant predictor of stock returns, at the expense of liquidity among minor predictors, means more than a shift of variable weighting, it points to a shift in the nature of mechanisms through which financial meaning is determined and constructed into action.
such a changing field of knowledge, companies can no longer assume that the self-evidence of their financial foundations will exist on their own.
The fact that a balance sheet has liquidity available does not imply any interpretive pertinence anymore to the minds of interested investors used to getting the signals and viscerally perceiving real time in a personally curated algorithmically rendered interface.
Rather, the new landscape requires a different form of finance participation, whereby design of signals, strategic framing, and visibility are among the most essential features of valuation, together with the classical measures of solvency and exposure to risk.
Being aware of such a change, the companies would have to migrate out of the compliance based model of disclosure and transitioning to a proactive reformulation of a representation of the financial wellbeing of the company.
This does not only involve reporting on the liquidity as a quantitative measure but describing its importance in the wider strategic vision that is compatible with the way investors perceive information and evaluate it in reality.
In an environment where firms are subjected to algorithmic sorting, volatility monitoring, and comparative dashboards, companies have to think about how to render stability inside the firm using metrics accessible to the newer information form.
The fact that such liquidity is and remains structurally significant but epistemically silent implies an incompleteness not of that measure but of the organizational form of communication.
According to the arguments presented by Aaker .
, strategically telling financial stories, which involve integrating data into significant curves, is no longer a luxury in communique but a matter of operation.
This is especially important to firms that are dealing in sectors that value stability rather than mobility in the short term such as property and infrastructure.
Even substantial liquidity could be unable to make it into the realm of investor perception without the support of narrative scaffolding.
Such change in strategy in strategic communication comes with an equivalent reconsideration of theory.
Once financial meaning is not objective in the data but is constructed through such activities as mediation, circulation, and interpretability, understandings of conventional theories of investor rationality and informational efficiency need updating.
The assumption made by asset pricing models that everyone has equal access to and ability to process all the relevant information breaks down in a situation in which some kinds of signals prevail due to their construction and communication.
Not what data is available at all but what data is made visible, emotionally compelling, and symbolically powerful is what is at stake.
This change is highlighted in recent efforts on behavioral and mediated finance.
Khmyz .
and Gallagher & Segara .
have demonstrated that good cognition by the investors is becoming dependent on digital forms that operate on values of immediacy, volatility, and traceability.
The presence of systematic risk on all of the interfaces of the investment and thus its privilege as variable is privileged not solely by the fact that it correlates statistically with returns, but the fact that it is designed to be read in a particular way.
Liquidity, in its turn, is still mostly excluded in the cadence of the finance narrative not because it is in fact valueless but rather because it is inaccessible as a narrative site.
Such transformation of theories requires the emergence of a different research agenda, one which questions the epistemology of valuation and the infrastructures, over which the behavior of investors get formed.
The lack of explanation in the variance of the stock returns exhibited in this study signifies that the applicable models are missing vital interpretive variables.
Investor reactions that cannot be viewed as being attributable to fundamental analysis may be explained by sentiment data, social visibility measures, platform ranking effects, and real-time Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce measures of engagement.
The contribution of the analysis of data on search engine trends, financial discourse sentiment, and digital trace data to asset pricing models and their analytical worthiness have been highlighted by scholars like Hassan .
Although such variables are non-traditional, they provide inferences on the conceptions of firms in the context of digital investment ecology.
The difficulty of letting go of the comfort of balance sheet rationalism and confronting the forms of financial cognition that now determine market outcomes applies not just to governments but also to the companies and individuals who make it their business to manage other people as well as corporations.
Such is not a rejection of rigor but a shift in the definition of what is considered explanatory relevance.
The sectoral heterogeneity in these dynamics should also not be ignored in future research, at the same time.
Companies in long cycle businesses, like real estate, are confronted with a unique challenge of seeking to communicate value in systems that are optimised to short range Similar to other institutions, liquidity in these companies is usually misaligned by the rates of interpretation in digital platforms that favour volatility, and responsive positioning.
What is urgently needed, as Bianchi et al.
argue, is context-sensitive communicational models of communication, capable of appealing to both the long-term stability of operation and the short-term tractions of interpretation.
Disaggregated research on how various sectors operate visibility, signal resilience, and construct of the frames of interpretation will serve to give details that reveal how financial meaning is negotiated on the terrain of varied Its implications are not theoretical but guide how companies adjust their investor relations strategy, choose their form of disclosure and train the process of communicating to be legible in necktie-constrained, platform-mediated markets.
Of no less significance is the normative aspect of this interpretive reconfiguration.
Provided that valuation today is more driven by visibility than substance, the companies that are less often preferable through their algorithms or less receptive to volatility indicators can be unfairly excluded in capital allocation proceedings.
This creates a possible system inefficiency in that the attention of investors is systematically skewed to unstable, fragile firms over those which only increase risk visibility.
Economidou et al.
have voiced skepticism concerning the development of a feedback loop where high-risk firms receive more attention as a result of which their activity in the market rises, which also confirms their visibility as a signal.
It is in this case that interpretive asymmetry reduces not only to the perception but also a financial lockout mechanism.
It is this additional role being played by digital intermediation in conditioning both the valuation and the access to the very capital of investment that needs testing critically in future research and a possible rebalancing towards a fairer Capital and recognition distribution.
It is not after all that this was a study that isolates two variables to be statistically evaluated.
this study instead articulates an epistemic shift in the reasoning of financial valuation.
As much as systemic risk acts as a proxy of volatility, it is an indicator of visibility, narrative alignment, and platform legibility.
Still, liquidity, which is economically important, is logistically underproclaimed in the mechanisms that have since come to dictate the impression of investment.
The business implication is, that companies, have to tell the value story, not report the value.
The theoretic implication is that it begins to dictate that market behavior models have to deal with mediated cognition and signal uptake differentials.
The methodological imperative of the research is to create new method tools that map perception, draw interpretive hierarchies, and differentiate what moves, what freezes and why.
Financial meaning is not naturally obvious.
is made, disseminated and it is a matter of contention in a system of systems that equally renders what is visible as well as what is known.
Teguh Prakoso et al.
Celebes Scholar pg Journal of Social Commerce Conclusion The association among liquidity, systematic risk and stock returns has been able to be discussed effectively using this research, paying much attention to both the statistical simplicity and interpretative richness.
The findings show that the systematic risk still has measurable and stable effects on the investor judgment, whereas the liquidity, though having the economic significance, does not have a statistically significant influence on the behavior of returns under the circumstances considered.
Instead of interpreting this as being dismissive of the significance of liquidity, the results imply a changing environment whereby the communicability of a financial signal is becoming even more crucial in deciding its market Not every metric has the equivalent interpretive force, and certain measures, such as that of systematic risk, seem more sensitive to the symbolic logic of visability that has come to determine investment conduct in the digital environment.
This changing pattern of reception of financial signals is one part of more general structural transformations in the circumstances under which investors get hold of and take action on Systematic risk acquires specific salience not just because of its theoretical matchup with volatility exposure, however because as well it is also structured in a format that can be instantly identified in an assortment of interfaces and analytic tools.
However, liquidity, though central to business sustainability, can be buried in the lower tiers of finance reporting and will not have the descriptive hints that are of primary importance to digital systems.
Consequently, its impact is less evident, in immediate market reception, not because it is empty, but because it is not made to become accessible in forms that appeal to the current forms of attention and perception.
The implications of firms are evident.
It is not sufficient anymore to have good liquidity, but this strength is still deep down in the solid reports.
Financial stability requires an increased effort to be presented in a manner that appeals to the forums, and habit of interpretation under which investors are currently accessing firm data.
This does not amount to an appeal to enhance and show off, but to transparency that is conditioned to the information landscapes of contemporary investment practice.
Failure to evolve to this change is a major threat to the visibility of the firm, especially in cases of firms in non-rapid sectors or capital-intensive sectors where visibility to the market is becoming more influential in determining values.
The results highlight the necessity of perfecting the theoretical instruments with the application of which market behavior can be elucidated.
Conventional models tend to assume equal weights of all pertinent information to actors who are rational.
But in the evidence given, such indicators have been shown to gain an exponentially larger power not because of an innate superiority but because of the effectiveness of their placement in the architectures of interpretive uptake.
This raises the question as to how financial meaning should be structured and given out and how concerns are addressed, interface design and frames through communicative structuring to shape the power of what companies report.
Future studies have the possibility to investigate the rest of the variation in return behaviour not attributed to liquidity or systematic risk as well.
This introduces the possibility of variables that indicate how the firms are perceived, discussed, and experienced with the digital route.
They may be trends in internet search, the moods of investors calculated using financial language, or the logic of technological filtering that amplifies certain messages or blocks others.
The role of exploring these dynamics would provide fuller picture of the process of assigning and interpreting value in a digitally mediated marketplace.
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Journal of Social Commerce References