Stock Selection Model in the Formation of an Optimal and Adaptable Portfolio in the Indonesian Capital Market

Abstract

This study aims to determine the factors that can influence investors in selecting stocks in the Indonesian capital market to establish an optimal portfolio, and find phenomena that occurred during the COVID-19 pandemic so that buying interest / the number of investors increased in the Indonesian capital market. This study collection technique uses primary data obtained from the survey questionnaire and secondary data which is market data, stock price movement data sourced from the Indonesia Stock Exchange, Indonesian Central Securities Depository, and Bank Indonesia, as well as empirical literature on behavior finance, investment decision, and interest in buying stock. The method used in this research is the survey questionnaire analysis with the SEM (statistical approach). The results of the analysis using SEM show that investor behavior influences the stock-buying interest, investor behavior, and the stock-buying interest influences investor decision-making. However, risk management does not influence investor-decision making. This occurs when the investigator's psychological capacity produces more decision information by decreasing all potential biases, allowing the best stock selection model to be selected. When the investigator's psychological capacity creates more decision information by reducing biases, the optimum stock selection model can be chosen.

1. Introduction

In the era of global integration, investment is a key driver of economic growth. The capital market allows investors to diversify their portfolios based on risk and reward. A capital market helps a country’s economy by increasing investment. Investors must make accurate choices to earn. Traditional finance theory says investors will act rationally and understand information (Ricciardi & Simon, 2000).

Monowar (2013) revealed that the decision-making process has a significant potential for error or is irrational, which may cause investors to make inaccurate decisions or predictions. In general, decision making is a complex phenomenon based on the concept of satisfaction, which might determine an increase or decrease in utility in an effort to promote satisfaction. Similarly, investment decisions are made logically so as to maximize their benefit.

Nevertheless, modern investor strategy isn’t just based on basic analysis. Some stock market investors tend to act irrationally because of psychological factors that go against what most people think (Trinugroho & Roy, 2011). Investor behavior is the study of how investors’ minds work and how that affects the market (Sewell, 2007). Shefrin and Statman (2000) say that behavioral finance is the study of how psychological factors can affect how a person handles money. Investors who are prone to biased behavior make the same mistakes over and over and choose investments that are good but not the best. This is called the behavior of investors. Ton and Dao (2014) say that psychological factors are natural to all people and affect all decisions, including investing decisions. The psychological elements will split investors into three categories based on their risk preferences: risk averse (avoiding risk), risk neutral (indifferent to risk), and risk preference (favor risk). The risk tendencies of investors reveal how small or large their interest is in relation to the investment dangers they experience; thus, this has a significant impact on investment decisions.

Indonesia is populous. Large numbers of citizens can be leveraged to directly and indirectly promote national growth through investment and the financial sector. Investors in Indonesia aren’t increasing as fast as the population. Many Indonesians may not comprehend how to invest in the capital market. The population prefers savings or deposits. The lack of interest among local investors is also attributable to falling global public investment expectations and rising interest rates. Indonesia’s investment climate worries foreign investors. Indonesia is a poorer investment than Vietnam, Thailand, Malaysia, and China. Foreign investors are hesitant to participate in its execution because to labor issues, a lack of legal clarity, security concerns, and regional autonomy exercises.

Table 1 shows that the number of investors in Indonesia is low relative to the country’s population growth, hence their desire to invest is low. Foreign investors control Indonesian capital planting. This is a concern since citizens’ investments determine a country’s economic growth. Indonesians have a low investment interest, at 0.15 percent, compared to nearby countries Malaysia, Singapore, and Australia (Pajar & Pustikaningsih, 2017).

Table 1: Comparison between the Number of Investors and the Number of Indonesia’s Population

COVID-19 in Indonesia caused IDX Composite to drop to 4,000. This loss can’t be separated from investor feeling that the Indonesian government hasn’t done enough to combat the pandemic; as a result, more investors are withdrawing funds from the Indonesian capital market. As a result of the COVID-19 outbreak, many enterprises shuttered and were unable to restart, therefore new investors turned to the Indonesian capital market as an alternative revenue source. This growth in investors must be maintained. Because capital markets are vital in emerging countries, Indonesia’s capital markets must be positive about local investors.

Willingness to discuss concerns affects investing decisions. In addition to risk, an investor’s reactivity to opportunities and difficulties can affect decision-making. Nofsinger (2018) shows how mental health affects financial behavior (behavioral finance). Psychological variables influence all activities, including financial ones, explain Ton and Dao (2014). Psychological variables classify investors as risk averse, risk neutral, or risk preferring (like risk). The shifting economy affects the investor’s decision-making. Because it’s an investment signal. A savvy investor can profit from efficient markets by seizing opportunities. Study investor behavior when investing. This study examines factors affecting Indonesian investors’ share selection.

2. Literature Review

According to Winkel (2009), interest is a person’s strong inclination toward something; interest is also a sort of attraction for something or an activity. According to Tandeilin (2012), investments are pledges to multiple funds or other resources with the expectation of future financial returns. According to Nofsinger (2018), the purpose to invest is one of the perspectives held by market participants in the realm of capital markets. Where investment intention is a cognitive estimation of risk and reward. Nofsinger (2018) further emphasized that determination, self-discipline, and hard effort fostered this type of interest. Individual talents in cognition, attachment, and conation are closely associated to the “intention to invest” procedure’s high capabilities for market players.

Finance behavior is an approach that describes how psychological elements influence how humans invest or react to finances (Wicaksono, 2015). In their journal, Ricciardi and Simon (2000) noted that financial behavior is a field in which numerous disciplines interact and are regularly integrated to avoid its discussion from becoming isolated. Financial behavior is a simplistic model of human economic behavior, which posits that the principles of self-interest, rationality and perfect information govern the economic actions of individuals (Pompian, 2006).

Baker and Ricciardi (2014) show that behavioral finance offers a unique perspective on investment psychology. Understanding investor behavior can help future investors, even though they can’t change the past. Liu et al. (2020) found that yield rivalry and the forgetting effect influence institutional and individual investor equilibrium. Investors should examine personal experiences logically when making financial decisions and building long-term confidence. Individual investors should help expand the Indonesian capital market, according to Sutyanto et al. (2022) reported that individual investors should serve as a crucial metric for the development of the Indonesian capital market. In addition to psychological aspects, it is believed that demographic factors significantly influence investor decisions.

Investment decisions are defined by Manurung (2020) as the process of choosing alternative selections from the available options. The actual investment decisions of investors are impacted by their prior profit experiences and their projections of future gains (Virlics, 2013). Boda and Sunitha (2018) found that investors’ psychological state affects their investment decisions. Understanding investor behavior allows them to profit on psychological biases. Gill et al. (2018) discovered a favorable connection between economic expectations and investment behavior. In the case of economic expectations, full mediation is advised when the search information variable is included in the mediator. Trust bias positively affects decision-making.

According to Ratnadi et al. (2020), investment decision-making uses availability bias. Accounting mentality is a behavioral prospect variable, stock price patterns are market behavior, and investor herding is reaction speed. Multiple linear analysis shows that heuristics have no effect on investing decisions, while behavioral prospect factors and market behavior do and herding does not. Adiputra (2021) says overconfidence, representational bias, and risk tolerance affect investing decisions. Thus, elements that influence investor decision making include readily available or trusted information, availability bias, and representation prejudice (Kamran et al., 2020).

“Risk management” combines “risk” and “management” Risk is an institution’s loss due to an uncertain future occurrence that can be assessed and minimized (Manurung, 2020). Identification, measurement, analysis, implementation, and evaluation of risks. According to Markowitz (1952), if investors are only interested in maximum income, they tend to have one type of investment that they think will provide the highest return in the future, but the alternative states that many investors pay attention to the income while having equal concerns on the risk that will arise.

The research gap analysis explains differences between this study’s results and those of other studies, such as if earlier studies addressed investor behavior and decision making as independent variables. This study examines investor behavior, decision-making, risk management, and interest in buying shares in the Indonesian capital market. We’ll develop hypotheses and cross-diagrams on this foundation (path analysis). This study also aims to determine the interrelationships between the exogenous (investors’ decision-making) and endogenous (Figure 1) latent variables. We’ll develop research hypotheses and cross-diagrams using this methodology (path analysis).

Figure 1: SEM Model

Based on the SEM model, the following hypotheses are obtained:

H1: Investor Behavior affects the Stock-Buying Interest.

H2: Investor Behavior affects Investor-Decision-Making.

H3: Stock-Buying-Interest affects Investor-Decision-Making

H4: Investor Behavior affects Risk Management

H5: Risk Management affects Investor-Decision Making

H6: Investor-Decision-Making affects Stock Model Selection.

3. Research Methods and Materials

This research was conducted in Greater Jakarta/Jabodetabek (Jakarta, Bogor, Depok, Tangerang, and Bekasi) region, focusing mainly on millennials and all Jakarta-based Indonesians. The retrieval and measurement of time occurred between July 2021 and February 2022. This section of the dissertation was expected to be completed by December 2022 at the latest. The design (research plan) employs a mixed technique (qualitative and quantitative) approach. This investigation is categorized as a causality study (cause and effect principle). The SEM approach can be used to identify characteristics that influence investors’ selection of stocks on the Indonesian capital market.

This research aims to make use of both primary and secondary data sources. Survey questionnaires and talks are used to collect primary data. Whereas secondary data is market data, stock price movement data sourced from the Indonesian Stock Exchange, the Central Custodian Securities Indonesia, and the Bank of Indonesia, as well as empirical literature on behavior finance, precision making investors, and interest in buying stocks, constitute primary data. On a scale from 1 to 5 (1 = strongly disagree; 2 = disagree; 3 = undecide; 4 = agree; 5 = strongly agree) (McLeod, 2008). Likert scale indicator variable measures were utilized in this study.

This study optimizes the application of nonprobability purposive sampling. As primary data, this study’s population consists of stock enthusiasts and practitioners. Referring to Hair et al. (1998), the minimum number of respondents required for this research is 250. The investigator is interested in the entire group of persons, the chivalry, or the thing known as the population (Sekaran, 2010). This study’s population for primary data consists of citizens/potential stock trading investors, while secondary data consists of prior research on investor behavior and investor decision-making.

Structural Equation Modeling (SEM) is the analytical tool used to examine the influence of factors that can affect investors’ stock selection on the Indonesian capital market. SEM is a statistical technique used to evaluate the associations between one or more independent variables and a continuous or discrete dependent variable. SEM is a statistical method for investigating causal links among latent variables (unobservable variables).

Several SEM steps are necessary to finish the modeling procedure: 1) The creation of theoretical models The first phase in building the SEM model is the search for or construction of a model with good theoretical explanations (Ferdinand, 2000). The second step is the development of track diagrams to simplify researchers’ understanding of the to-be-examined causal link. The flowchart depicts the link between constructs using arrows (Figure 2), and 4) illustrates the trajectory diagram in the analysis of the relationship between variables. The next step is to select the data input matrix and estimate the proposed model. For the overall estimate, only covariant matrices or correlation matrices are used as input data in SEM analysis. 5) Determine that the researched model is neither under-identified nor unidentifiable due to a reliable inference procedure. 6) Examine and assess the requirements for the fitness or appropriateness test. At this stage, the model is tested against multiple criteria for the goodness of fit. 7) The validity of the indicators used to change the measurement model’s structure can be determined by utilizing LISREL 8.80 to process the data. The indicator’s t-value must be greater than 1.96.

Figure 2: Path Model Diagram Variable Hypothesis

This research bases its operationalization of variables on five factors: Behavior Investors, Decision Making Investors, Risk Management, Stock-Buying Interest, and Stock Selection Models. The operational definitions of the variables used in this study will be explained in detail in Table 2.

Table 2: Variable, Definition, and Indicator

4. Results and Discussion

Each latent variable’s measurement model’s validity and reliability are evaluated. Validity measures truthfulness. In SEM, validity is measured against construct reliability to show that the instrument measured the concept as described by the theory. Reliability measurements ensure that the measuring device is unbiased over time.

If we evaluate the SLF value and t-value of each research variable, we can conclude that all indicators are valid and give a large contribution (at the 5% significance level). Appendix 1 shows that the SLF value of each variable meets the model’s goodness-of-fit requirements; therefore, we can conclude that the Stock-Buying Interest variable (SBI), Investor Behavior (IB), Risk management (RM), Investor Decision Making (IDM and Stock Selection Model (SSM) are valid; this is further supported by the value of t-value 1.96 (5% real rate), which indicates that the variables are significant. The overall model has great construct dependability with CR and VE values of 96.7% and 64.6%, respectively. CR and VE values already match standard norms or have been certified valid, where CR is valid if >70% and VE is >50%.

4.1. Overall Fit Test Model

The goodness of Fit measurement reveals the overall structural model compatibility test (GoF). A measurement model is considered “fit with data” if it can estimate the covariance matrix of the data. The fit size is given by the size of the Khi square (2)/df 3. According to the processed data, the value of Khi-squared is (2)/df = 1.63. This indicates that the model for measuring fit is “excellent.” CFI > 0.90; P-calculate Khi-squared > 0.05; RMSEA 0.08 The RMSEA value in this model is deemed acceptable because it is less than 0.08, particularly 0.08. The majority of the model’s conformance criteria have been satisfied. This indicates that the model created through study can be deemed effective (Table 3).

Table 3: Overall Goodness of Fit Test Model

4.2. SEM Structural Model Analysis

After getting the results of process variables, a research framework-compliant structural model is constructed. The depiction of the path diagram includes the variables Stock-Buying Interest (SBI), Investor Behavior (IB), Risk management (RM), Investor Decision Making (IDM), and Stock Selection Model (SSM). It is evident from Figure 3 and Figure 4 that there is a substantial relationship between factors. The t-value for each variable is less than 1.96 (5% significance level), indicating that the variables are already significant. The total model has strong construct dependability with CR and VE values of 96.7% and 64.6%, respectively, where CR and VE values meet or have been deemed valid.

Figure 3: SEM Output Standardized Solution Lisrel 8.80 Model

Figure 4: SEM Output t-value Lisrel 8.80 Model

SEM’s structural model connects latent variables (latent exogenous and latent endogenous). Table 4 shows the study’s structural model hypotheses. Hypothesis 1 is confirmed because the t-value in Table 4 is greater than 1.96 (at a 5% significance level). A one-unit rise in Investor Behavior leads to a 0.63-unit increase in stock-buying interest. Investor behavior toward stock-buying interest depends on how easy or difficult an activity is in proportion to the resources available possibilities (Ajzen, 2005).

Table 4: Hypothesis Test Result

Ajzen (2005) says, based on the notion of the planned behavior model, that the greater an individual’s control over his or her behavior, the greater the likelihood that the individual will do an action or behavior. These findings are consistent with the findings of Phan and Zhou (2017); according to Mahastanti and Hariady (2014), perceptions of behavioral control have a major impact on investment preferences.

The t-value IB➔IDM is greater than 1.96 (at the 5% significant threshold), proving hypothesis 2. It means one unit of investor behavior increases investor-decision-making by 0.39 units. When investors rely on past scenarios or experiences, their behavior affects their decision-making. Investors brainstorm because today’s problems are similar to those of the past; therefore, the same approach is used. Behavior herders tend to rely more on others’ investing decisions. Herding conduct is a common mistake where investors rely on collective knowledge from other investors rather than personal information.

Based on the findings of the analysis, it is known that the stock-buying interest variable influences investor-decision-making, since the t-value is more than 1.96 (5% actual rate), hence proving hypothesis 3. This suggests that a one-unit increase in stock-buying interest will result in a 0.36-unit decrease in investor-decision-making.

Hypothesis 4 is validated since the largest t-value IB ➔ RM is greater than 1.96 (5% significance level), indicating that Investor Behavior effects Risk Management. This suggests that a one-unit increase in Investor Behavior will result in a 0.62-unit increase in risk management.

Risk management does not influence investor-decision-making because the t-value is 0.19 < 1.96 (5% significance level). Hypothesis 5 is declined. Examining a company’s financial performance may show its health, but not its future or the dangers that impact investment decisions, hence risk management does not affect investor-decision-making. Ratnadi et al. (2020) found that availability bias affects investment decisions. Accounting mentality is a behavioral prospect variable, stock price patterns are market behavior, and investor herding is reaction speed. Multiple linear analysis shows that heuristics do not affects investor-decision-making, while behavioral prospect factors and market behavior do and herding does not. Sattar et al. (2020) found prejudice affected investment decisions. Heuristics influence investment decisions more than prospects and personalities. Herdianto’s analysis says risk management has little impact on investment decisions. Effective human resource management requires risk management knowledge and abilities.

Nevertheless, investor-decision-making influences the Stock Selection Model, since the t-value is greater than 1.96 (5% significant level), proving hypothesis 6. This suggests that a one-unit increase in investor-decision-making increases investors’ capacity to perform a 0.25-unit stock selection model. According to the research of Gill et al. (2018), there is a positive relationship between economic expectations and investor-decision-making, and investor behavior. Moreover, overconfidence bias has a significant positive relationship with investor-decision-making and investor behavior when information seeking is added as a mediator. According to Chronopoulos (2011), the ability of stocks to modify their risk aversion might affect the value of investment options. In extending the traditional real options approach to investing under uncertainty, companies must determine strategies regarding the determination of duopolistic competition that can influence the entry of investors in avoiding risk and how the value of the company can vary between two distinct oligopolistic markets based on risk aversion and uncertainty.

Based on the results of the SEM research, it is known that investor behavior and stock-buying interest influence an investor’s stock selection on the capital market. Investor behavior influences investor-decision-making, which is known to affect the stock selection model. When an investor wishes to perform a stock buy and select a stock selection model, it may be claimed that the investor considers stock-buying interest and the purchase choice, but not risk management. For judgments to be founded on logical considerations, resulting in reasonable benefits, these factors must be examined.

5. Conclusion

Investor behavior affects stock-buying interest, and both affect investor-decision-making, according to the SEM study. Risk management doesn’t affect investor decisions. Investor behavior influences risk management. Investor-decision-making impacts the stock selection model. this shows that a one-unit improvement in investor-decision-making boosts investors’ stock-selection abilities. When the investigator’s psychological capacity creates more decision information by reducing biases, the optimum stock selection model can be chosen. Political stability, economic factors, and business stability affect the stock selection model. Investors who choose the best stock selection model will focus on behavior and establish an investment appetite. More variables that could affect the stock selection model are expected in future research. Assessing currency exchange rates and inflation enhances findings. For future research, it is anticipated that more variables with the potential to influence the Stock Selection Model would be included. For instance, assessing more particular risks, such as currency exchange rates and inflation, improves results.

Appendix